adaptive mac protocols for data-intensive wireless sensor ......medium access control protocol that...

Adaptive MAC Protocols ForData-intensive Wireless Sensor Networks

This thesis is

presented to the

School of Computer Science & Software Engineering

for the degree of

Doctor of Philosophy

of

The University of Western Australia

By

Alvaro Enrique Monsalve Ballester

April 2016

c⃝ Copyright 2015

by

Alvaro Enrique Monsalve Ballester

iii

Abstract

This thesis presents PRIMAC, which is a medium access control protocol that

provides quality of service for data-intensive wireless sensor networks through

service differentiation. Data-intensive wireless sensor networks comprise nodes that

generate high volumes of data during an event, for instance, images, audio, video or

seismic monitoring. Data-intensive applications introduce new research challenges

due to the high volume of data to be transmitted over unreliable channels, and the

bursty nature of their transmission profiles.

PRIMAC is based on a channel contention CSMA mechanism with non-uniform

contention window. It achieves higher access priority for selected data-intensive

nodes without deteriorating the network channel utilization. Experimental results

demonstrate that PRIMAC provides better network performance than the widely

adopted IEEE 802.15.4 standard, in terms of normalised channel throughput and

packet delivery ratio.

We also introduce wireless sensor network designs that could achieve optimal through-

put for nodes with homogeneous data-intensive traffic conditions. The sensor nodes

operate using the contention access method of IEEE 802.15.4 MAC protocol with

optimised setting of the standard protocol parameters. An analytical model of the

carrier sense multiple access with collision avoidance (CSMA-CA) algorithm is pro-

posed and equations are derived to obtain the appropriate CSMA-CA parameters.

We present PRIMAC-Uniform for homogeneous data-intensive WSNs, which is an

enhanced carrier sense multiple access with collision avoidance scheme of IEEE

v

802.15.4 with uniform contention window for that guarantees near optimal nor-

malised channel throughput. We evaluate the performance of our protocol and

compare it with the standard CSMA-CA algorithm of IEEE 802.15.4 by using an

experimental testbed in an indoor environment. We find that PRIMAC-Uniform

doubles the packet delivery ratio for any network size whilst keeping high levels of

throughput.

In summary, this thesis focuses on the design of medium access protocols and op-

timal network architecture for a newer generation of wireless sensor networks that

have high data transmission requirements. The results demonstrate that data-

intensive WSNs could be realised through the implementation of optimal strategies

in the nodes in order to successfully contend for a shared medium.

vi

Preface

This thesis is the result of my research work at the School of Computer Science

and Software Engineering, The University of Western Australia. The thesis

consist of seven chapters. Two chapters contain papers that were accepted for

international publications. The research content of chapter 3 was carried out at the

Faculty of Information and Communication Technologies, Swinburne University of

Technology.

Chapter 4 covers the analytical study of optimal WSNs using the IEEE 802.15.4

medium access control protocol that was presented in [64]. Chapter 5 proposes en-

hancements to the recommended carrier sense multiple access with collision avoid-

ance of IEEE 802.15.4 to enable data-intensive applications that was published in

[63]. Chapter 6 introduces a new MAC protocol for generic data-intensive wireless

sensor networks.

Publications (Fully Refereed)

1. “Optimal designs for IEEE 802.15.4 wireless sensor networks”. A.

Monsalve, H. Vu and Q. Vo. Wireless Communications and Mobile Comput-

ing. Volume 13. Issue 18. Pages 1681-1692. 2011. [Chapter 4]

2. “Empirical evaluation of adapting IEEE 802.15.4 contention win-

dows for maximum performance”. A. Monsalve, R. Cardell-Oliver, A.

Datta and C. Huebner. IEEE International Symposium on Personal, Indoor

vii

and Mobile Radio Communications . Pages 260-265. 2012. [Chapter 5]

viii

Contribution of the Candidate to

Publications

My contribution in all publications was 85%. I developed and implemented the

protocols, performed the simulations and experiments, and wrote the papers. My

supervisors, Associate Professor Rachel Cardell-Oliver and Associate Professor Ami-

tava Datta, reviewed all the papers and provided useful feedback to improve the

quality and readability. Professor Hai Vu and Associate Professor Bao Quoc Vo

provided feedback on the paper “Optimal designs for IEEE 802.15.4 wireless sen-

sor networks” [64]. Professor Christof Huebner provided feedback to improve the

quality of the paper “Empirical evaluation of adapting IEEE 802.15.4 contention

windows for maximum performance” [63].

Alvaro Enrique Monsalve Ballester (Candidate) Date

Rachel Cardell-Oliver (Supervisor) Date

ix

Acknowledgements

Iwould like to especially thank my supervisors Associate Professor Rachel Cardell-

Oliver and Professor Amitava Datta. My gratitude also goes to Professor Christof

Huebner, Professor Le Hai Vu and Associate Professor Bao Quoc Vo. To my friends

and colleagues at the University of Western Australia and family.

xi

Abbreviations

AEA Adaptive Election Algorithm

B-MAC Configurable MAC Protocol

BNC Bayonet Neil-Concelman Connector

BPSK Binary Phase Shift Keying

CAP Contention Access Period

CC Channel Capacity

CCA Clear Channel Assessment

CFP Contention Free Period

CSL Conflit Slot List

CSMA/p* Non-persistent Carrier Sense Multiple Access

CSMA-CA Carrier Sense Multiple Access with Collision Avoidance

CW Contention Window

DI Data Intensive

DRAND Distributed Slot Assignment Protocol

DSSS Direct Spread Spectrum Spreading

EAP1 Exclusive Access Phase 1

EAP2 Exclusive Access Phase 2

ED Energy Detection

EDD Earliest Due Date

FCS Frame Check Sequence

FlexiTP Flexible-Schedule-Based TDMA Protocol

FTS Fault-tolerant Listing Slot

G Data Traffic Load

xiv

GHS Global Highest Slot

GTS / GTSs Guaranteed Time Slot / Guaranteed Time Slots

HBC Human Body Communications

IEEE Institute of Electrical and Electronics Engineers

IoT Internet of Things

ISM Industrial, Scientific and Medical

L Length of Packet in slots

LEACH Low Energy Adaptive Clustering Hierarchy

LQI Link Quality Indication

M2M Machine-to-machine

MAC Medium Access Control

MAP Managed Access Phase

MFS Multifunction Slot

MMSN Multifrequency MAC Protocol.

MPDU MAC Protocol Data Unit

N Node

NAM Network Animator

NAMA Node-Activation Multiple Access

NB Narrowband

NCR Neighbourdood-aware Contention Resolution

NP Neighbour Protocol

ns-2 Network Simulator 2

ODA On-demand Aggregation

OPNET Optimized Network Engineering Tools

OQPSK Orthogonal Quadrature Phase Shift Keying

OSI Open Systems Interconnection

OST On-demand TDMA Slot Transfer

OTCL Object-oriented Tool Command Language

Ps Packet Delivery Ratio

PAN Personal Area Network

PHR Physical Layer Header

xv

PHY Physical Layer

PRIMAC Priority Medium Access Control Protocol

PRIMAC-Uniform PRIMAC with Uniform CSMA-CA Scheme

PSDU Physical Service Data Unit

PTS Possible Transmitter Set

QoS Quality of Service

RAP Another Managed Access Phases

RAP1 Random Access Phase 1

RAP2 Random Access Phase 2

RSL Receive Slot List

RSSI Received Signal Strength Indication

S Normalised Channel Throughput

SEP Schedule Exchange Protocol

SHR Synchronization Header

SMACS Self-Organizing Medium Access Control for Sensor Networks

STE Shortest Time To Extinsion

TTS Toggle Snopping Period

TTT Toggle Transmission Period

TDMA Time Division Multiple Access

TEA-15.4 Traffic and Energy Aware IEEE 802.15.4

TMCP Tree-based Multichannel Protocol

TRAMA Traffic-adaptive Medium Access Protocol

TSL Transmit Slot List

UP User Priority

UWB Ultra-wideband

WBA Wireless Body Area

WoT Web of Things

WPAN Wireless Personal Area Network

WSN / WSNs Wireless Sensor Network / Wireless Sensor Networks

Z-MAC Zebra Medium Access Control

xvi

Contents

Abstract v

Preface vii

Contribution of the Candidate to Publications ix

Acknowledgements xi

Abbreviations xiv

1 Introduction 1

1.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.1.1 Network Description . . . . . . . . . . . . . . . . . . . . . . 6

1.2 Research Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.2.1 Enable Data-intensive Applications . . . . . . . . . . . . . . 8

1.2.2 Provide Predefined Levels Of Quality Of Service . . . . . . . 9

1.2.3 Develop A Medium Access Protocol For Data-intensive WSNs 9

1.3 Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.3.1 Adaptive CSMA-CA For Homogeneous Conditions . . . . . 11

1.3.2 PRIMAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

xvii

1.4 Thesis Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 Literature Review 15

2.1 Multiple Access Control . . . . . . . . . . . . . . . . . . . . . . . . 15

2.1.1 Contention-based Protocols . . . . . . . . . . . . . . . . . . 16

2.1.2 Scheduled-based Protocols . . . . . . . . . . . . . . . . . . . 21

2.1.3 Multichannel Protocols . . . . . . . . . . . . . . . . . . . . . 25

2.1.4 Cross-Layer Design . . . . . . . . . . . . . . . . . . . . . . . 29

2.1.5 Hybrid Protocols . . . . . . . . . . . . . . . . . . . . . . . . 30

3 Methodology 37

3.1 Evaluation Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 37

3.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3 Evaluation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.3.1 Analytical Modelling . . . . . . . . . . . . . . . . . . . . . . 40

3.3.2 Network Simulator 2 (ns-2) . . . . . . . . . . . . . . . . . . 41

3.3.3 Experimental Sensor Nodes . . . . . . . . . . . . . . . . . . 47

4 Data-Intensive Networks with Optimal IEEE 802.15.4 49

4.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.2 The IEEE 802.15.4 Protocol . . . . . . . . . . . . . . . . . . . . . . 52

4.3 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.3.1 Sensing Rate for Unsaturated Case . . . . . . . . . . . . . . 55

4.3.2 Sensing Rate for Saturated Case . . . . . . . . . . . . . . . . 56

4.3.3 Sensing Failure Probability . . . . . . . . . . . . . . . . . . . 56

xviii

4.3.4 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . 58

4.3.5 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . 58

4.4 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.5 Performance Optimization . . . . . . . . . . . . . . . . . . . . . . . 61

4.6 Optimal Normalised Channel Throughput . . . . . . . . . . . . . . 63

4.6.1 Design 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.6.2 Design 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.6.3 Design 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.7 Evaluation Of Optimal Networks . . . . . . . . . . . . . . . . . . . 72

4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5 Enhanced CSMA-CA For Homogeneous Conditions 79

5.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.2 Performance Experiments . . . . . . . . . . . . . . . . . . . . . . . 82

5.2.1 System Design . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.2.2 Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . 85

5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.3.1 Saturated Trial . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.3.2 Unsaturated Trial . . . . . . . . . . . . . . . . . . . . . . . . 88

5.4 PRIMAC-Uniform: Uniform CSMA-CA Scheme . . . . . . . . . . . 90

5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6 A Priority MAC Protocol for Data-Intensive Networks 97

6.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

6.2 PRIMAC Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

xix

6.3 Differentiated and Non-Uniform Medium Access Strategy . . . . . . 102

6.3.1 Collision Minimizing . . . . . . . . . . . . . . . . . . . . . . 102

6.3.2 Best Contention Window for PRIMAC . . . . . . . . . . . . 104

6.3.3 Node-Independent Attempt Distribution . . . . . . . . . . . 109

6.3.4 Node-dependent Distribution for High Priorities DI Nodes . 110

6.4 A comparison: PRIMAC and IEEE 802.15.4 . . . . . . . . . . . . . 113

6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

7 Conclusions 121

7.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . 121

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Bibliography 127

xx

List of Tables

1 Chipcon CC2420 Transceiver [17] . . . . . . . . . . . . . . . . . . . 59

2 Optimal Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

xxi

List of Figures

1 Architecture Of A Homogeneous Data-Intensive WSN . . . . . . . . 5

2 Architecture Of A Heterogeneous Data-Intensive WSN . . . . . . . 6

3 IEEE 802.15.4 (a) Peer-to-Peer Topology, (b) Star Topology . . . . 17

4 Superframe Structure Of IEEE 802.15.4 . . . . . . . . . . . . . . . . 17

5 Superframe Structure Of IEEE 802.15.6 In Beacon Mode . . . . . . 19

6 LEACH Structure And Operation . . . . . . . . . . . . . . . . . . . 22

7 SMACS Link Between Two Sensor Nodes . . . . . . . . . . . . . . 23

8 Self Organised Network (SMACS) Of 18 Sensor Nodes . . . . . . . 24

9 Conceptual System Design Of TMCP . . . . . . . . . . . . . . . . . 25

10 Toggle Transmission In MMSN . . . . . . . . . . . . . . . . . . . . 26

11 WirelessHART System Architecture . . . . . . . . . . . . . . . . . . 28

12 Frame Structure Of TRAMA Including Detailed Slot Format . . . . 30

13 Frame Structure Of Z-MAC Including Detailed Slot Format . . . . 32

14 FlexiTP Slot’s Assignation . . . . . . . . . . . . . . . . . . . . . . . 33

15 (a) Four slot-requestors In The Network: B, F ,G, and H Need To

Find a Slot-supplier. (b) B, F, and H Manage To Get Extra Slots

From C, E, and K Respectively. H Uses Multiple Paths To The Base

Station Whilst C, E and K Stop Their Schedule Temporarily. . . . . 34

xxii

16 Basic Architecture Of ns-2 . . . . . . . . . . . . . . . . . . . . . . . 42

17 Architecture Of A ns-2 Node . . . . . . . . . . . . . . . . . . . . . . 44

18 WPAN Device Architecture . . . . . . . . . . . . . . . . . . . . . . 45

19 Packet Transmission Of A Node In An Unsaturated Network . . . . 54

20 S for Different Packet Arrival Rates And N=20 . . . . . . . . . . . 60

21 S for Different Packet Arrival Rates and N=30 . . . . . . . . . . . . 60

22 S For Different M . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

23 S For Different BEmax . . . . . . . . . . . . . . . . . . . . . . . . 62

24 S For Different BEmin . . . . . . . . . . . . . . . . . . . . . . . . . 63

25 Sopt For Different Length Of Packet L . . . . . . . . . . . . . . . . . 65

26 Sopt And S(λ=λSAT ) For L And N . . . . . . . . . . . . . . . . . . . 65

27 λopt And λSAT For Different M . . . . . . . . . . . . . . . . . . . . 67

28 λopt And λSAT For Different BEmin . . . . . . . . . . . . . . . . . 67

29 Comparison Between Sopt And Sbest . . . . . . . . . . . . . . . . . . 70

30 Sopt For Different L And N . . . . . . . . . . . . . . . . . . . . . . 72

31 Nbest For Different L And λ . . . . . . . . . . . . . . . . . . . . . . 73

32 Sbest For Different L And λ . . . . . . . . . . . . . . . . . . . . . . . 73

33 Sopt For An Optimal Saturated Network . . . . . . . . . . . . . . . 74

34 Tnet For Optimal Designs 1 and 2, And Saturated Networks . . . . . 75

35 Tnet For Optimal Design 3, And Saturated Networks . . . . . . . . . 76

36 Wireless Sensor Node Hardware . . . . . . . . . . . . . . . . . . . . 83

37 PHY And MAC Packet Structure . . . . . . . . . . . . . . . . . . . 84

38 Network Setup Deployment . . . . . . . . . . . . . . . . . . . . . . 85

39 S For Saturated Trial . . . . . . . . . . . . . . . . . . . . . . . . . . 87

xxiii

40 Ps For Saturated Trial . . . . . . . . . . . . . . . . . . . . . . . . . 88

41 S For Unsaturated Trial . . . . . . . . . . . . . . . . . . . . . . . . 89

42 Ps For Various Parameters In N=10,L=12 . . . . . . . . . . . . . . 90

43 S For Various Parameters In N=10,L=12 . . . . . . . . . . . . . . . 91

44 Analytical S/SoptRatio . . . . . . . . . . . . . . . . . . . . . . . . . 93

45 S For PRIMAC-Uniform And IEEE 802.15.4 with Default Parameters 93

46 Ps For PRIMAC-Uniform And IEEE 802.15.4 with Default Parameters 94

47 Normalised Channel Throughput S Of PRIMAC - Homogeneous

L=12 For Different Network Sizes And Contention Windows . . . . 103

48 Packet Delivery Ratio Ps Of PRIMAC - Homogeneous L=12 For

Different Network Sizes And Contention Windows . . . . . . . . . . 103

49 Inter-Successful-Packet Time Of PRIMAC - Homogeneous L=12 For

Different Network Sizes And Contention Windows . . . . . . . . . . 104

50 Analytical Normalised Channel Throughput S Of PRIMAC - Ho-

mogeneous L=2 Slots For Different Network Sizes And Contention

Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

51 Analytical Inter-Successful-Packet Time Of PRIMAC - Homogeneous

L=02 Slots For Different Network Sizes And Contention Windows . 106

52 Normalised Channel Throughput S Of PRIMAC - Homogeneous L=2

Slots For Different Network Sizes And Contention Windows . . . . 107

53 Inter-Successful-Packet Time of PRIMAC - Homogeneous L=2 Slots

For Different Network Sizes And Contention Windows . . . . . . . . 107

54 Normalised Channel Throughput S Of PRIMAC - Homogeneous

L=30 Slots For Different Network Sizes And Contention Windows . 108

55 Inter-Successful-Packet Time Of PRIMAC - Homogeneous L=30 Slots

For Different Network Sizes And Contention Windows . . . . . . . . 108

xxiv

56 Attempt Probability Distribution For Networks Of 3 And 20 Nodes

And Average Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

57 S For CW = 10 And Two Optimal Slot Attempt Probability Distri-

butions: Exact and Average Curve . . . . . . . . . . . . . . . . . . 110

58 Inter-Succesful-Packet Time For CW = 10 And Two Optimal Slot

Attempt Probability Distributions: Exact and Average Curve . . . 111

59 Attempt Probability Distribution For Heterogeneous Network . . . 112

60 S - One High Priority DI Node . . . . . . . . . . . . . . . . . . . . 113

61 Inter-Successful-Packet Time Per Node - One High Priority DI Node 114

62 Ps per Node - One High Priority DI Node . . . . . . . . . . . . . . 114

63 S - Three High Priority DI Node . . . . . . . . . . . . . . . . . . . 115

64 Inter-Successful-Packet Time Per Node - Three High Priority DI Node115

65 Ps Per Node - Three High Priority DI Node . . . . . . . . . . . . . 116

66 Normalised Channel Throughput S For Acknowledged And Unac-

knowledged IEEE 802.15.4 With Default Parameters Under Satu-

rated Conditions, And Also The Optimal Channel Throughput Achiev-

able In Networks Using IEEE 802.15.4 . . . . . . . . . . . . . . . . 117

67 Optimal Normalised Channel Throughput Sopt Achievable Under Un-

saturated Conditions And Using IEEE 802.15.4 Default Parameters 117

68 Packet Delivery Ratio Ps For Acknowledged And Unacknowledged

IEEE 802.15.4 With Default Parameters Under Saturated Condi-

tions, And Also The Corresponding Optimal Packet Delivery Ratio

Achievable In Networks Using IEEE 802.15.4 . . . . . . . . . . . . . 118

xxv

Chapter 1

Introduction

Wireless sensor networks (WSNs) are a growing technology that is trans-

forming the existing means of communication for sensing devices. The

research and development of WSNs has primarily been focused on architectures

in which every sensor node has the same physical capabilities and can only inter-

act with neighbouring nodes. The first generation of sensor nodes are stringently

limited in terms of energy supply, processing power, memory and physical size.

Consequently, the state-of-the-art algorithms and protocols are tailored for these

requirements and find their applicability on low data systems such as monitoring

of temperature, pressure, humidity, or object location. However, there is a recent

demand for sensor nodes with higher data transmission requirements that can col-

lect more meaningful information from the environment, for example, multimedia

content such as still image, audio and video, health monitoring of elderly people,

and also industrial telemetry such as the output of a spectral density or chemical

composition analyser [49]. In the following, we will refer to this new type of sensor

nodes as Data-Intensive Wireless Sensor Networks.

Data-intensive WSNs enable applications that generates high volumes of data. If

the application contains video, audio streams and still images we can also refer to

1

2 CHAPTER 1. INTRODUCTION

this data-intensive system as Wireless Multimedia Sensor Networks [2]. Neverthe-

less, data-intensive WSNs are not only limited to multimedia content. They also

include mission-critical applications [87] such a medical patient monitoring [79],

car-park management [13] or seismic monitoring where bursty data needs to be

transmitted to the master sensor node of the network, i.e. sink, in a reliable fash-

ion. Additionally, we consider that data-intensive WSNs represent a step towards

the deployment of richer applications for the Internet of Things (IoT), machine-

to-machine (M2M) scenarios and Web of Things (WoT), which are at the moment

limited by resource-constrained devices [10].

Research challenges on the communication infrastructure of WSNs exist within ev-

ery of the seven layers of the Open Systems Interconnection (OSI) stack [88]. The

OSI protocol stack consists of the physical layer, data link layer, network layer,

transport layer, session layer, presentation layer and application layer. A com-

prehensive survey with theoretical proposals, experimental designs and industrial

standards for each layer can be found in [3]. In this thesis, we will provide solu-

tions for the data link layer that are tailored for the requirements of data-intensive

WSNs.

Amongst the challenges of data-intensive WSNs, the enhancement of the medium

access techniques is continuously at the centre of the technology. Wireless trans-

mission is characterised by poor reliability as a consequence smart communication

protocols are required in order to guarantee effective system operation. Communi-

cation between network nodes could be coordinated by a master controller to avoid

transmission collisions or, alternatively, use an uncoordinated communication mode

where contentions are possible in time and/or frequency. Optimal usage of the wire-

less medium is fundamental in an uncoordinated network that requires high data

transmission. In data-intensive applications, the information collected by sensor

nodes is of bursty nature and therefore it also defines a bursty transmission profile.

Medium access control (MAC) protocols based on contention to access a common

medium, such as Aloha [45], have traditionally proven to offer the best performance

3

for this bursty scenarios. They are capable of achieving timely and reliable packet

delivery without network coordination. On top of that, these protocols also allow

the implementation of energy saving management strategies on WSNs. However,

it is still of paramount importance to enhance the contention-based mechanism of

protocols such as Aloha or carrier sense multiple access (CSMA) [55] before they

can be applied to data-intensive WSNs.

Sensor nodes are compact electronic devices that are generally powered by limited

energy supply sources, for instance, one or two standard 1.5 volts AA batteries of

2000 mAh. Consequently initial research efforts have focused on protocol develop-

ment with the solid purpose of extending the network lifetime. In particular, it has

been found that the radio transmission module is the main contributor in energy

consumption and therefore numerous saving strategies were devised to minimize ra-

dio usage at the MAC sublayer. The need of innovative solutions for data-intensive

applications is causing an evolution in the wireless sensor networks paradigm. Now,

the focus is to design WSNs that could effectively deliver high volumes of data with

predefined levels of quality of service (QoS) [59], and for which energy saving comes

as a requirement of second order.

The Institute of Electrical and Electronics Engineers (IEEE) have released standard

specifications for the physical layer of WSNs [93, 94, 95, 96]. The modulation scheme

have been designed to support low battery power, short to medium range coverage

range (tens of meters) and maximum data rates around 40-250 Kbps. The IEEE

802.15.4 [93] standard recommends the use of the carrier sense multiple access with

collision avoidance (CSMA-CA) mechanism for the uplink transmission of data from

sensor nodes to the sink node during the contention access period of its superframe

structure. We will prove in the following chapters that CSMA-CA of IEEE 802.15.4

requires an automatic adapting mechanism of its transmission parameters in order

to maximize the use of the common wireless channel and thus enable data-intensive

WSNs. This adapting mechanism can be incorporated into the standard. We will

demonstrate with experiments that our proposal is feasible if knowledge of the


network characteristics, mainly the number of active nodes, and their data load is

available during the design. Lastly, we will propose a protocol that can reach an

equivalent level of optimal channel utilization without knowledge of the network

and load.

In this dissertation, data-intensive WSNs adhere to strict performance metrics that

predefine a level of quality of service. Specifically, this level of QoS achieved by

the sensor nodes will be determined in terms of normalised channel throughput,

packet delivery ratio and data transmission delay. We refer to normalised channel

throughput as the fraction of time that the wireless medium is occupied by successful

transmissions over the total active time. Experiments conducted on IEEE 802.15.4

[25] have found that the maximum achievable channel throughput lies around 32

Kbps in a network with star topology whose sensor nodes present permanent full

data buffers and their radio modules are capable of transmitting at 250 Kbps.

This performance is equivalent to a normalised channel throughput of 0.12. In

our own experiments with the recommended parameters of IEEE 802.15.4 in a

similar scenario we also found that packet delivery ratio is around a low 50 percent.

Additionally, these inefficient levels of normalised channel throughput and packet

delivery ratio cause longer transmission delays too. In comparison, our new MAC

protocol will be tested against those three metrics and demonstrate to be adequate

for data-intensive WSNs.

1.1 Objectives

This research project proposes an enhanced communication solution for the trans-

mission of data over a common wireless medium from multiple sensor nodes to a

single sink. The objective of this solution is to provide reliable packet delivery for

data-intensive WSN applications through the implementation of a weighted fair ac-

cess protocol. The study focuses on a network with star topology where all nodes

are in range of communication. The sensor nodes send data packets to a sink in

1.1. OBJECTIVES 5

Figure 1: Architecture Of A Homogeneous Data-Intensive WSN

an uncoordinated fashion where access to the common medium is obtained through

contention. The wireless radio capability of the sensor nodes is bounded by a given

data transmission bandwidth which closely approaches to the application demanded

load.

Two possible cases for Data-Intensive WSNs can be identified. First, consider an

architecture where all sensor nodes generate the same of volume of data. In every

node, the data is saved into packets of predetermined maximum size which are then

inserted into a queue for immediate transmission to the sink. The sensor nodes

demand a fair data transmission access, this is an equitable throughput and latency

performance. These conditions define a network where each node demands the

same level of QoS, which we will refer as Homogeneous Data-intensive WSNs. This

architecture is depicted in figure 1.

In the second case, sensor nodes generate dissimilar volumes of data. We will

classify nodes based on their type of data into scalar and critical nodes. Scalar

nodes produce packets of small size which are generated periodically and have high

tolerance to delivery delays. Critical nodes generates big volumes of bursty data

and their packets are of maximum size and delay-intolerant. In this case, the

differentiated levels of QoS originates Heterogeneous Data-intensive WSNs. This

architecture is depicted in figure 2.

In both scenarios, each sensing device will be equipped with the same radio module


Figure 2: Architecture Of A Heterogeneous Data-Intensive WSN

in order to enable communication between the entire group of sensor nodes that

compose the network, however, other physical capabilities, such as the memory,

processing power or battery capacity, can vary from device to device.

The use of sensor nodes with heterogeneous levels of quality of service, some with

real-time and high data demand, and others with conventional scalar data and

delay-tolerant, could facilitate the development of the new data-intensive WSNs

giving that the nodes are still limited in their capabilities. This is a vision which

is shared in a survey on Wireless Multimedia Sensor Networks [2]. Finally, notice

that this thesis will refer to network designs as the optimal selection of network

topologies and medium access protocols for WSNs.

1.1.1 Network Description

The characteristics of the data-intensive WSNs that are within the scope of this

project are the following:

1. Packet Generation. Scalar nodes transmit data periodically or event-driven,

and critical nodes present event-driven high data that could be of streaming

1.1. OBJECTIVES 7

or non-streaming nature. Streaming and non-interactive multimedia data re-

quires real-time delivery; however, it is loss-tolerant, for instance, the video/audio

recording of an event where the streams could play with a certain level of dis-

tortion and initial delay. On the other hand, non-streaming data does not

have real-time delivery constraints.

2. Weighted Fair Access. The objective is to obtain a fair medium access accord-

ing to the type of sensor node. Medium access is equitable amongst sensor

nodes of the same type, this is, scalar nodes or critical nodes are expected to

achieve the same level of QoS while contending only with nodes of their same

type. In comparison, medium access is prioritised or accelerated for critical

nodes with respect to scalar nodes due to its data importance.

3. Network Scale. Sensor nodes are arranged in a single star topology and are

always in range of communication. The scope of this project does not include

monitoring areas larger than the radio range of individual nodes. The latest

scenario is an open research study that could be addressed by having the sink

node of a first network acting as a critical node and relying the cumulative

data a second sink.

4. Lifetime Expectancy. Each type of sensor nodes has their respective lifetime

requirement. The expectancy should be larger for nodes that transmit only

scalar data and the lifetime could be reduced if a data-intensive flow is trig-

gered. For example, in a volcano application scalar sensors could operate in

saving mode for long periods of time while data-intensive nodes could stay in

dormant state until an event occurs. On doing so, we can guarantee a long

network lifetime before the event and afterwards we only require a lifetime in

the orders of days or hours


1.2 Research Challenges

In the following, we describe in detail the sub-problems to be addressed in this

project:

1.2.1 Enable Data-intensive Applications

The first generation of wireless sensor networks have been mostly developed for

monitoring applications with low data collection and transmission. The main char-

acteristics of such generation of WSNs were outlined in the surveys [1, 2, 3, 4, 12,

21, 23, 24, 29, 36, 43, 84, 85, 87, 100, 105]. Those characteristics have determined a

series of general design factors. The tolerance to failures, scalability of the network

and use of wireless medium for transmission are some of the factors, however, the

most important consideration has always been the use of energy saving techniques

to extend network lifetime. For these reasons, sensor devices have been equipped

with energy-saver radio modules that work with data rates in the 10-250 Kbps range

in order to conserve energy. Moreover, the vast majority of existing protocols for

the medium access layer have been designed to extend the network lifetime longer,

by implementing duty cycling strategies in which sensor nodes are sent to inactive

state for long periods of time between the inter-transmission of two consecutive

packets.

The medium access protocols designed for the first generation of WSNs do not sat-

isfy the requirements of data-intensive applications. Firstly, the amount of collected

data expected in a data-intensive application is higher, for example, multimedia con-

tent like still images or audio. Secondly, the newer generation requires a better use

of resources like channel capacity in order to fulfil the predefined levels of QoS. Up

to now scalar nodes under-use the communication medium by transmitting very few

packets. Lastly, the network lifetime expectancy of a data-intensive scenario could

be shorter since we only need to capture data for a period of hours or days rather

than years, for example, the video monitoring of an erupting volcano. For all of

1.2. RESEARCH CHALLENGES 9

these reasons, it is necessary to design a medium access mechanism that addresses

the requirements of the new generation of WSNs.

1.2.2 Provide Predefined Levels Of Quality Of Service

To develop WSNs with predefined levels of quality of service is a big challenge since

sensor devices are still constrained in terms of battery, memory, and data rate, while

the reliable delivery of data-intensive flows may be a resource-demanding task. In

this project we will ensure that the medium access layer is capable of satisfying

these QoS requirements by proposing a innovative MAC protocol. In particular,

we need to find mechanisms to increase the normalised channel throughput and

reduce packet transmission delay with minimal coordination between sensor nodes.

In contrast to traditional research on scalar and energy saving WSNs, we intend to

utilize the radio resources up to the maximum in order to guarantee the stringent

QoS requirements of data-intensive WSNs.

1.2.3 Develop AMedium Access Protocol For Data-intensive

WSNs

The type of medium access control needed by a data-intensive WSN depends on

the considered scenario. For the case of homogeneous WSNs, the objective of

this project is to find optimal designs that can guarantee the correct operation

of a network. Therefore, we propose to enhance the medium access scheme of the

contention-based MAC protocol defined for IEEE 802.15.4 in order to provide the

high levels of normalised channel throughput and packet delivery ratio. In particu-

lar, we intend to find appropriate CSMA-CA MAC parameters for different network

setting (network sizes and data load). On doing so, we can provide the same level

of QoS to each sensor node.

For the case of heterogeneous WSNs, we will require to design a new medium access


mechanism that could guarantee the QoS requirements of both scalar sensors and

critical nodes. As mentioned before, in recent years numerous MAC protocols have

been proposed in the literature for the first generation of WSNs. In general, all

the proposals are characterized by minimizing the energy consumption of nodes at

the expense of reducing the communication tasks. In the context of data-intensive

WSNs, the energy saving paradigm has to be re-thought in view of the need for a

mechanism to achieve predefined level of QoS. In respect to this, we will need to

develop a protocol that could maximize the channel utilization and node-to-sink

delay, and at the same time guarantee higher levels of packet delivery ratio in a

single-hop and star topology.

1.3 Solutions

In the first phase of the project, we studied the current state-of-the-art MAC pro-

tocols recommended for WSNs; in particular, we evaluated the performance of the

IEEE 802.15.4 MAC in WSNs with single-hop topology and data-intensive traf-

fic. In the literature review, we consider the suitability of numerous protocols for

data-intensive WSNs. Specifically, we analyse scheduled-based protocols such as

Low Energy Adaptive Clustering Hierarchy (LEACH) [33, 32] and Self-Organizing

Medium Access Control for Sensor Networks (SMACS) [83, 82]. Hybrid Protocols

covered TRAMA [97, 98], Z-MAC [73, 74] and FlexiTP [52, 53], and we considered

multichannel proposals such as TMCP [103] and MMSN [107]. Contention-based

protocols included TEA-15.4 [86], IEEE 802.15.6 [96], and Saxena [78]. In general,

there are multiple reasons why these proposals are not appropriate for data-intensive

applications, which are explained in details in chapter 2. Still from this list of pro-

tocols, we found that the IEEE 802.15.4 standard could be adapted such that its

CSMA-CA mode of operation could become suitable for burst, critical and high

data traffic.

In the second phase, we proposed a smart adapting mechanism for the IEEE

1.3. SOLUTIONS 11

802.15.4 contention-based MAC protocol in order to find optimal network designs

for WSNs that could satisfy the data requirements of homogeneous conditions. In

the final phase, we proposed PRIMAC, which is a new MAC protocol for data-

intensive WSNs of homogeneous and heterogeneous traffic conditions.

1.3.1 Adaptive CSMA-CA For Homogeneous Conditions

In chapter 4 and 5, we investigate possible enhancements for the IEEE 802.15.4

MAC protocol in order to achieve high resource utilization. In particular, the ob-

jective is to maximize the normalised channel throughput in a network whose sensor

nodes use a single channel for communication and half-duplex radio transceivers.

To do so, we develop an analytical model to characterize the performance of a

single-hop wireless sensor network which uses the carrier sense multiple access with

collision avoidance (CSMA-CA) medium access mechanism of IEEE 802.15.4 MAC

protocol. The analytical model can be used in order to propose optimal network

designs for the data-intensive applications by choosing appropriate MAC parame-

ters. The analytical model and optimal proposals are validated by using a network

simulation environment, i.e. ns-2, which is a highly accepted simulator in indus-

try and academics for wireless networks. Moreover, we present a testbed of sensor

nodes that was created to evaluate the performance of the CSMA-CA algorithm and

corroborate the feasibility of the optimal network designs. The findings of this in-

vestigation allow us to propose a smart adapting mechanism that can be integrated

into IEEE 802.15.4 to realize homogeneous data-intensive WSNs.

The proposed adaptive IEEE 802.15.4 mechanism generates WSNs that are char-

acterised by:

1. High Normalised Channel Throughput. High resource utilisation is possible

with recommended CSMA-CA parameter if we design a network with the right

combination of number of nodes, data packet size and data packet arrival.

Also the model allow us to achieve optimality in networks where the number


of node is not known by adapting the contention window length.

2. Fair Channel Access. Nodes are homogeneous in their traffic generation and

hence they equitably share the common medium.

3. Standard Support. The proposed mechanism represents an extension feature

that can be complement the IEEE 802.15.4 specification. Therefore, industrial

use of our optimal designs could become a seamless adoption.

1.3.2 PRIMAC

We propose a Priority MAC (PRIMAC) in chapter 6, which is a novel medium access

protocol that satisfies the requirements of data-intensive applications in WSNs with

both homogeneous and heterogeneous levels of QoS. In contrast to the optimal

designs for homogeneous conditions of the previous phase, notice that the predefined

levels of quality of service vary depending on the type of sensor node. Consequently,

new techniques need to be developed in order to guarantee the QoS of the different

nodes under the restriction of limited communication capability given by the limited

data bandwidth (100-250 Kbps) of the radio module of such sensor nodes.

In principle, we aim to focus on three of the four pillars of quality of service [45].

The first pillar is high resource utilization, the second pillar is packet classification,

and the third pillar includes scheduling and policing. PRIMAC needs a medium ac-

cess scheme that could achieve higher channel utilization, and provide weighted or

differentiated access for nodes. To do so, we propose to have a MAC protocol based

on the CSMA protocol since it is very suitable for burst and event-driven traffic

scenarios, in which the information about what nodes are active and when they

transmit is unknown. Additionally, we maximize the normalised channel through-

put by adopting an improved non-uniform backoff scheme on CSMA that will reduce

the idle time and minimize the occurrences of collisions.

WSNs that uses PRIMAC as their access protocol present the following character-

istics:

1.4. THESIS CONTRIBUTIONS 13

1. High Normalised Channel Throughput. Our proposal uses a non-uniform

medium access method that guarantees near-optimality in the worst cases,

for networks where the number of nodes is not known a priori.

2. Weighted Channel Access. PRIMAC can prioritise the medium access for

node of critical importance such as the one described as heterogeneous data-

intensive WSNs. This weighted access scheme is achieved through adjustable

contention windows and different non-uniform probability of medium attempt

for every class of node.

3. Maximize Packet Delivery. Contention-based protocol are known for having

high packet loss behaviour . PRIMAC is part of the contention family of pro-

tocol but its packet delivery ratio exceeds the performance of IEEE 802.15.4

CSMA-CA.

1.4 Thesis Contributions

The core contribution of this work will be the provision of data communication

guarantees for nodes with burst, critical and high data in wireless sensor networks.

The guarantees will be accomplished through optimal network topologies and en-

hanced medium contention with weighted fair access. Specifically, this work present

the following contributions:

1. Demonstrate analytically and with experimental results that homogeneous

data-intensive WSNs are possible and practical by choosing optimal IEEE

802.15.4 CSMA parameters.

2. PRIMAC achieves optimal normalised channel throughput without knowledge

of the network size and load.

3. The novel use of non-uniform access probability distribution combine with

adaptive contention window sizes in order to achieved weighted access to a


common channel.

4. Combine non-uniform access probability distribution with adaptive contention

window sizes in order to improve the QoS of critical nodes in a data-intensive

WSN.

5. Enable data-intensive WSNs with radio transceiver of low-power and low-to-

medium data transmission rate which is achieved by using resources optimally.

1.5 Thesis Overview

This thesis is organized in seven chapters. Chapter 2 presents a review of the wire-

less sensor network technologies and applications that preceded our study. Firstly,

we present a classification of the existing MAC protocols for wireless networks and

secondly we describe the relevant proposals and industrial standards that constitute

the first generation of WSNs. Chapter 3 describes the methodological approach we

use to address the requirements of data-intensive WSNs. In chapter 4, we will

study how to build homogeneous data-intensive WSNs by introducing an adaptive

mechanism on top of the standard IEEE 802.15.4 CSMA-CA MAC protocol. Chap-

ter 5 corroborates with experimental testbeds that the implementation of a smart

adapting transmission parameter mechanism on the IEEE 802.15.4 is feasible and

practical. In chapter 6, we will consider sensor nodes with heterogeneous levels of

quality of service, some with real-time and high data demands, and others with

conventional scalar data and delay-tolerant. This architecture represents a promis-

ing approach that could facilitate the development of the new data-intensive WSNs

giving that the devices are still limited in their capabilities, in particular, in terms

of maximum data transmission rate. The final chapter present the conclusions of

this dissertation.

Chapter 2

Literature Review

In this chapter we present the relevant medium access control schemes for WSNs

that have preceded this thesis dissertation. The following industrial technologies

and academic proposals have served as background for our PRIMAC protocol and

research studies.

2.1 Multiple Access Control

Wireless broadcast radio transmission is the most suitable communication method

for most sensor networks including our data-intensive WSNs, primarily, because it

provides flexibility in terms of physical node placing, network configuration and

deployment. In a wireless network, a MAC scheme is necessary to orchestrate the

transmission from the various nodes, and thus prevent or minimize communication

interference. In general, there exist two broad categories of medium access tech-

niques [54]. In the first category we found a channel partitioning scheme where

nodes are assigned dedicated and collision-free radio channels for their steady data

transmissions. In the second category nodes share a common medium through the

utilization of a dynamic medium access protocol. Amongst these two categories the

dynamic scheme offers the best choice for WSN due to their adaptability to bursty

15

16 CHAPTER 2. LITERATURE REVIEW

data traffic conditions, and low processing power and bandwidth requirements.

Depending on the dynamic channel access strategy, MAC protocols can be di-

vided into three categories: scheduled-based, contention-based and hybrid proto-

cols. Scheduled-based protocols use a time division or a multiple channels scheme

to assign every node a dedicated medium to communicate at any moment. These

protocols find their applicability in a network whose nodes have a periodic or pre-

dictable data traffic pattern. In a contention-based protocol, uncoordinated sensor

nodes share a unique channel and evaluate the idleness of this medium before at-

tempting a transmission. The medium sensing feature of contention-based protocols

allows proper handling of bursty and unpredictable data transmission. The protocol

PRIMAC that we present in this dissertation is part of the contention-based family.

Hybrid utilizes a combination of both methods depending on intensity of the traffic

or the decision of the coordinator of the network.

One of the drawbacks of contention-based protocols is that packet delivery becomes

unreliable as the data transmission load of nodes increases. Additionally the nor-

malised channel throughput becomes inefficient even though the channel capacity

CC is still less that the data traffic load G. A direct approach to solve this problem

is by implementing a flexible TDMA protocol with weighted fair access in order to

accommodate the exchange of packets. Against this problem, there have been nu-

merous hybrid solutions, for instance, FlexiTP [53]. However, this protocols require

time to adapt to the network traffic demands and consume time to reach a stable

transmission schedule.

2.1.1 Contention-based Protocols

IEEE 802.15.4

The release of IEEE 802.15.4 specification [93, 94, 95] standardized a MAC protocol

for low data rate wireless sensor networks which is based on a combination of

2.1. MULTIPLE ACCESS CONTROL 17

(a) Peer-to-Peer (b) Star

Full Function Device Reduced Function Device WPAN Coordinator

Figure 3: IEEE 802.15.4 (a) Peer-to-Peer Topology, (b) Star Topology

GTS GTS Inactive Part

CFPCAPBeacon

Beacon Interval

Beacon

Transmission Slots

Figure 4: Superframe Structure Of IEEE 802.15.4

contention and scheduled access. The specification defines two topologies: peer-to-

peer topology and star topology. In the peer-to-peer topology seen in Fig. 3 (a),

any sensor node can communicate with any other node if they are in communication

range of each other and there is a coordinator node that takes care of managing and

identifying the Wireless Personal Area Network (WPAN). Additionally, there are

full function devices that can create connections amongst themselves and reduced

function devices that are limited to contact coordinators. In the star topology seen

in Fig. 3 (b), all communication exchanges go through the WPAN coordinator.

IEEE 802.15.4 incorporated power saving mechanisms in its two modalities of oper-

ation: beaconless and beacon-enabled mode. In the case of beaconless, every node

accesses the medium using an unslotted Carrier Sense Multiple Access with collision

avoidance (CSMA-CA) scheme. For beacon-enabled the coordinator establishes a

duty cycle period that allows sensors to enter in shutdown state, and hence save


energy. A superframe structure specifies the parts that delimit the duty cycle pe-

riod as shown in Fig. 4. The active part of the cycle is divided in a contention

free period (CFP) and a contention access period (CAP). In CFP node follows a

scheduled access with the use of guaranteed communication time slots and in CAP

a CSMA-CA scheme is employed. A sensor node can request a guaranteed time slot

(GTS) in the CAP. The coordinator can allocate up to a maximum of seven GTSs.

Time sychronization in both CAP and CFP is achieved by diving time into trans-

mission slots of fixed size. A slot duration is predetermined by the IEEE 802.15.4

specification and note that a GTS extends through numerous slots.

Data-intensive WSNs are foreseen to present heterogeneous data traffic require-

ments [2]. To address this consideration, it recently appeared some proposals that

aim at providing service differentiation by enhancing existing MAC protocols or by

developing new solutions. The authors of [67] proposed to provide traffic prioriti-

zation support in IEEE 802.15.4 MAC by defining two traffic classes. The traffic

prioritization is implemented by adjusting the parameter settings of the CSMA-CA

scheme during the CAP. A mathematically analysis is presented to evaluate the

performance of this service differentiation. However, the authors did not extended

the analysis for cases when more than two traffic classes exist and neither found the

optimal parameter settings in terms of normalised channel throughput or delay.

TEA-15.4

TEA-15.4 in [86] proposes an enhancement of the standard IEEE 802.15.4 for sup-

porting multimedia content. The idea is to improve throughput by using an adaptive

CAP in correspondence to the level of traffic. With this enhancement, the number

of still images collected in a period of 100 seconds raises from 6 to 51 files. However,

this strategy is very limited because an extended access period does not necessarily

leads to an efficient use of the channel. Additionally, TEA-15.4 does not consider

heterogeneous traffic requirements.

IEEE 802.15.6

The authors of IEEE 802.15.6 [96] developed a communication standard for short-


B UP7

CSMA-CA

EAP1

All UPs

CSMA-CA

RAP1

Polling/Posting

Mechanism

MAP

UP7

CSMA-CA

EAP2

All UPs

CSMA-CA

RAP2

Polling/Posting

Mechanism

MAP

B2 All UPs

CSMA-CA

CAP

B UP7

CSMA-CA

EAP1

Beacon Period (superframe) Next superframe

Figure 5: Superframe Structure Of IEEE 802.15.6 In Beacon Mode

range and low power devices which operates in and around the human body area.

A wireless body area (WBA) network is composed of one coordinator and numer-

ous sensor nodes in the range between 1 and 64. The physical layer defines three

radio communication modes: Narrowband (NB), Ultra-wideband (UWB) and Hu-

man Body Communications (HBC). The MAC sublayer incorporates four access

mechanisms, these are: random access, improvised and unscheduled access, sched-

uled access and variants and medical implant communications service band access.

IEEE 802.15.6 defines a superframe structure of fixed length which can be bounded

by periodic network beacons. The superframe contains nine access phases, which

are: beacon Exclusive Access Phase 1 (EAP1), Random Access Phase 1 (RAP1),

Managed Access Phase (MAP), Exclusive Access Phase 2 (EAP2), Random Access

Phase 2 (RAP2), Another Managed Access Phases (RAP) and the Contention Ac-

cess Phase (CAP). EAP, RAP and CAP are the phases within the superframe in

which the medium access is achieved via a contention protocol, whether CSMA-CA

or slotted aloha 5. In the CSMA-CA mode, a sensor node with data to transmit

use a backoff counter and a contention window in order to win a transmission slot.

The user priority (UP) values can be chosen according to the traffic designation.

The standard specifies a relation between user priorities and contention window

boundaries.

The authors of [18] developed an analytical model to evaluate the performance of the

CSMA-CA protocol in a load saturated network composed by one coordinator and

three sensor nodes. The average payload size is 100 Bytes, the data rate is 971.4

Kbps and three different user priorities (emergency, medical data and controlled

load) are set. They found that the CSMA-CA protocol utilizes the common medium


poorly under saturated conditions and the highest user priority (emergency traffic)

nodes transmit more often than other sensor nodes. Still the maximum achieved

normalised channel throughput does not exceed 0.00186 which is an unsatisfactory

result for high data demand applications.

Saxena

In [78] we can find a new MAC protocol that guarantees quality of service in terms of

end-to-end delay and data rate requirements by implementing service differentiation

and increasing the throughput of a contention-based scheme. They propose to

adaptively adjust the contention access strategy in a CSMA-CA scheme based on

historical statistics of the network and adapt the duration of the duty cycle periods

in correspondence to the traffic class. Although, this proposal is promising, the

solution is limited to single-hop case and the medium access strategy depends on

historical information. The solution represents a closed-loop control system that

needs time to reach an optimal point, if such a point exists and is reached with the

proposed method.

Slotted CSMA-CA has been widely used as the preferable contention-based protocol

for applications with event-driven traffic because of its effective mechanism to share

a common wireless channel among uncoordinated devices. However, the reliability

of the communication is compromised by the interference caused by the competing

sensor nodes. In the context of data-intensive applications where high normalised

channel throughput is indispensable, interference escalates this problem to a higher

degree.

SIFT and CSMA/p*

The authors of [90] proposed a protocol, named CSMA/p*, which has a non-

persistent carrier sense multiple access scheme with a carefully chosen non-uniform

probability distribution p* that nodes use to randomly select contention slots. They

showed that CSMA/p* is optimal in the sense that p* is the unique probability dis-

tribution that minimises collisions between contending nodes in a network where the

number of active nodes N is known. It could also achieves suboptimal performance


when N is unknown. An assumption of CSMA/p* is that nodes have always packets

awaiting to be transmitted. This assumption resembles the data requirements of

critical data intensive nodes.

SIFT [40] was designed for event-driven applications and they assume that only the

first R of N potential reports are the critical information that needs to be sensed

and transmitted to the sink. Low latency is achieved through the use of fixed-size

contention window and a carefully chosen non-uniform probability distribution of

transmitting in each slot within the window. If no node starts to transmit in the

first time slot in the window then each node exponentially increases its transmission

probability for the next slot.

We found that the proposals of CSMA/p* and SIFT with their non-uniform prob-

ability of attempt could contribute in the development of data-intensive because

they present low transmission delay and minimises the probability of collisions in

a CSMA-based access method. However, both protocols are not adequate for het-

erogeneous data-intensive WSNs because they do not provide mechanisms for given

priority access to critical nodes.

2.1.2 Scheduled-based Protocols

This section contains the most notable TDMA-based proposals for data-intensive

WSNs.

Low-Energy Adaptive Clustering Hierarchy (LEACH)

LEACH is a popular self-organising protocol that creates clusters within a large

WSN. It utilises randomized rotation of local cluster base stations (local sinks) to

evenly distribute the energy load among the sensors in the network. The operation

of LEACH is divided into rounds. In the cluster setup phase, each node invokes

an algorithm to decide whether or not it wants to serve as a cluster head. A node

that opts to be cluster head announces its decision to other nodes which are in

range of communication, otherwise a node must join any other cluster that would


Time

Frame

Figure 6: LEACH Structure And Operation

be controlled by other head node. In the steady-state phase, the nodes can transmit

their backlogged data to the cluster heads using a reserved slot, as seen in Fig. 6.

The received data is aggregated in the cluster head before it gets transmitted to

the sink. After a period of time, the network finishes the steady-state round and

goes back to the setup phase to select another cluster.

This protocol assumes that all nodes can transmit with enough power to reach

the main sink node, which is one of the premises for data-intensive WSNs. On

the other hand, LEACH was designed for large networks, for instance 100 nodes

as tested in [32], where clusters’ rotation could potentially generates a saving in

terms of energy consumption. In comparison, data-intensive networks are more

likely to be composed by few tens of nodes. Remember that sensor nodes are still

limited by radio transceiver of low-to-moderate data rate. Moreover, LEACH was

developed for periodical data collection which makes the protocol unsuitable for

event generated data of burst nature [24].

Self-Organising Medium Access Control for Sensor Networks (SMACS)

SMACS is a TDMA protocol that allows nodes to discover their neighbours and

establish communication schedules without the control of any master or sink node.

SMACS combines the neighbouring discover and channel assignment phases, and

data transmission is possible before a full network discovery is reached. A link


Figure 7: SMACS Link Between Two Sensor Nodes

between two nodes consists of two time slots, one for each node to transmit, that

are a randomly chosen and allocated to a given frequency as depicted in Fig. 7.

In this protocol, time is synchronised between two communicating nodes. As a

consequence, no centralised schedule or time synchronisation is required.

One of the advantages of SMACS includes its capacity to form a connected network

rapidly, which is a desired feature in data-intensive WSNs, and also this protocol

uses multiple frequencies (multichannels) to rely data from node to node. The use

of multiple channels is one of the most viable ways for improving throughput. The

drawbacks that we found in this proposal is that the communication schedule was

designed with the primary purpose of saving energy and not increasing throughput.

Data aggregation might not be possible in some applications, such as the trans-

mission of video. Finally, multiple hops communication, as shown in Fig. 8, could

cause bottlenecks and congestion through some routes, especially if there is not

central sink who could determine the best paths.

Shortest Time to Extinction (STE) and Earliest Due Date (EDD)

STE [77] and EDD [19] are two similar TDMA protocols that are designed for

real-time streaming video applications. In both protocols, packets are sent in the

increasing order of their respective delay tolerance. In EDD, the TDMA structure

is fixed with constant frame and slot duration. The quality of service of the mul-

timedia stream defines a flexible TDMA structure in STE that takes into account

the packet dropping rate, arrival rate, and the delay tolerance. The best TDMA


0

3

4

6

10

11

13

5

2

1

8

14

17

16

15

12

9

7

Figure 8: Self Organised Network (SMACS) Of 18 Sensor Nodes

schemes are chosen during the design phase of the network and it requires knowl-

edge of the expected data traffic load, the number of nodes and the maximum delay

tolerance. STE derives multiple TDMA schemes, allows to identify the performance

limiting factors associated with the suboptimal schemes, determine the magnitude

of their (negative) impact and to point to performance improving modifications

which should be pursued to the extent permitted by technological constraints.

Data-intensive WSNs are not limited to real-time streaming applications where

deadlines could be set on a packet’s lifetime. In contrast, we consider that reliable

packet delivery is of paramount importance in data-intensive applications. For

this reason the schemes proposed in STE and EDD could not always satisfy the

requirements of our considered scenario. In subsection 2.1.5 we will consider other

protocols with adaptive TDMA schedule which could be more suitable for data-

intensive WSNs.


Frequency 1

Sink

Frequency 2

Frequency 3

Figure 9: Conceptual System Design Of TMCP

2.1.3 Multichannel Protocols

In this section we present protocols that aims at eliminating interference by using

multiple channels of different frequencies.

TMCP

TMCP [103] is a multi-frequency protocol for a data collection sensor network with

tree-based topology that partitions the whole network into multiple sub-trees, al-

locates different channels to each subtree, and then forwards each data flow along

its corresponding subtree, as depicted in Fig. 9. The objective of this protocol

is to create an optimal network topology that guarantees collision-free communi-

cation and reduce channel interference. It proposes an algorithm that determines

how to partition a given network into subtrees such that inter-tree interference is

eliminated, this is the interference amongst different trees. On the other hand, the

intra-tree interference, which is the potential interference amongst nodes within a

tree, cannot be avoided completely and the TMCP protocol could only attend to

reduce its impact on the general performance.


Transmission with fdest Transmission with fself

Preamble

PHY Protocol Data Unit

Figure 10: Toggle Transmission In MMSN

Although, TMCP could potentially increases throughput and therefore it could be

considered as a candidate for data-intensive applications, the improvement is not

guaranteed since intra-tree interference could still happen. Additionally, TMCP

does not consider the traffic distribution, this is, some subtrees could be overloaded

in their channel capacity. Finally, the proposal assumes that the base station (sink

node) is equipped with multiple radio transceivers, all tune in on different channels

simultaneously. In general, devices with multiple transceivers are not practical

solutions due to complexity, cost and high energy consumption.

MMSN

MMSN [107] is a multi-frequency media access control protocol for WSNs with mesh

topology and multi-hop communication that assigns each node a different receiv-

ing channel within two communication hops, posses a common broadcast channel

and uses a non-uniform backoff algorithm for contention. MMSN is node-based

multi-channel protocol because its scheme consists of having each node learn the

neighbouring node’s channel choices and use this knowledge in order to select a

channel which is not in used by any other node within two hops range. The pro-

tocol introduces the toggle snooping and toggle transmission features to enable

communication between two nodes that employs different channels. Before sending

a data packet, a node should first listen to its own channel (fself )for incoming data

and then assess the idleness of the destination channel (fdest) prior to transmis-

sion. After successfully assessing the idleness of both channel, the node transmits a

preamble on fself and fdest, alternatively as seen in Fig. 10. The toggle transmission


prevents other nodes from sending data to the transmitting node and allows other

nodes to avoid collisions on the destination channel. Detection of the preamble is

ensured by using a toggle snooping period (TTS)two times longer than the toggle

transmission period (TTT ).

The proposal could minimize the interference between nodes at the expense of high

complexity and high resource demands for sensor nodes. Nevertheless, we find

that the throughput of MMSN could be below the requirements of a data-intensive

application because this proposal finds its greater improvement on a large network

where multiple channels are available and whose topology is a data collection system

in which the data flows from numerous nodes to a sink. A final consideration is

that the toggle scheme could cost high energy consumption not only in the sink,

but also in the critical and scalar nodes.

Load Balance

Another approach for data-intensive WSNs with mesh topology was adopted in [48].

The idea is balance the traffic load by grouping nodes that communicates frequently

in a cluster. Then, communication interference is avoided by assigning a different

channel to each group. The proposal guarantees that an optimal number of sensor

nodes are grouped in each cluster; however it did not propose an improved medium

access scheme. Consequently, we found that the proposal is insufficient to enable

data-intensive applications if the MAC protocol and radio transceiver capacity do

not satisfy the demand of high data transmission.

WirelessHART

The WirelessHART [26] specification is the first open wireless communication stan-

dard for measurement and control in the process industries. It uses wireless mesh

networking between field devices to provide secure, reliable communications that

can meet the requirements of industrial applications. A WirelessHART network

[20, 36, 87] is composed of the following devices: the network and security man-

ager, the gateways and the field devices, as shown in Fig. 11. The network manager


Host System

WirelessHART

Gateway

WirelessHART

Network Manager

WirelessHART

Security Manager

WirelessHART

Access PointWirelessHART

Access Point

WHD

1

WHD

3

WHD

2

WHD

4

WHD

4

WirelessHART

Field Device (WHD)

WHD

Data Base

Figure 11: WirelessHART System Architecture

administrate the network configuration, establish the communication schedules be-

tween devices, and monitor the system health. The gateway is a powerful device

that relays data between the field devices, the network manager and the control

station. The MAC scheme of WirelessHART combines TDMA with frequency hop-

ping for channel access. It utilises the physical layer specified for IEEE 802.15.4 on

the 2.4 GHz ISM band where a range of 11-25 different channels are available.

The communication between two field devices is driven by a predetermined fre-

quency hopping schedule that guarantees collision-free transfers and end-to-end

delays. The protocol executes a slotted hopping scheme that determines the chan-

nel to use in each slot. The advantages of this protocol includes an interference

avoidance feature that allows the blacklisting of channels with poor performance,

also field devices can create a mesh network with multiple data paths to destination,

and the use of retransmissions to improve the packet delivery ratio.


The disadvantages of WirelessHART is that the communication performance of its

network depends on the decisions made by the network manager in terms of trans-

mission schedules and data routing. Therefore, the optimality or not of the network

relies on a single centralized component which must meet the requirements of load

balancing, maximum end-to-end delays, reliable routes. Although WirelessHART

provides end-to-end delays guarantees in packet delivery, we found this protocol

inadequate for data-intensive applications because it was primarily designed for the

transmission of scalar data, such as valves, motor relays, pumps, amongst other

control devices in a factory environment where scan rates can be configured to 1,

2, 4, 8, 16, 32, 64+ seconds. Nevertheless, it is still open for research an evalua-

tion of the maximum capacity that this protocol could possibly reach in terms of

normalised channel throughput.

2.1.4 Cross-Layer Design

We can find a work in [59] that proposes a cross-layer approach to reliable and

flexibly deliver QoS to applications in heterogeneous wireless multimedia sensor

networks. The idea is to design a radio transceiver to enable high data rate within

tens of meters. The design requires an integration of the physical, data and network

layers since different traffic classes and communication reliability levels, which are

inherent characteristics of higher layers, are implemented by using different cod-

ing schemes in the physical layers. The biggest obstacles of this proposal are the

complexity of the sensor devices and the short range of the radio modules, in the

order of 10 meters. Therefore, it is more feasible to develop data-intensive WSNs

with sensor nodes whose functionalities are well defined at each layer and present

higher transmission range. On top of that, the high energy consumption expected

for the proposed radio module is still prohibited for most sensor networks applica-

tions. Moreover, the sensors with scalar requirements will underuse the resources

provided by the radio module. Finally, there exist data-intensive applications that

could operate with the capacity of existing radio transceivers, for instance in [86],


Figure 12: Frame Structure Of TRAMA Including Detailed Slot Format

we can find an image capture application where one sensor node in a network of

few nodes uses a standard IEEE 802.15.4 radio transceiver of 250Kbps to transmit

images of typical 320x240 pixels size at a rate of 64 files every 100 seconds.

2.1.5 Hybrid Protocols

Scheduled protocols where Time division multiple access (TDMA) is used are char-

acterised by low channel utilization. To overcome this drawback a numerous of

proposals have opted for combining TDMA with contention-based protocols such

as CSMA-CA.

TRAMA

TRAMA [97, 98] is a protocol for multi-hop networks with varying data load condi-

tions. The protocol attends to increase throughput by allocating slots only to nodes

that requires data to transmit. In TRAMA, the time is divided in transmission slots

and a group of consecutive slots delimits a frame, as depicted in Fig. 12. A frame

consists of a random access period where sensor nodes can access the medium using

CSMA, followed by a scheduled access period where TDMA is utilized. During

the contention period, the sensor nodes perform a CSMA-style communication for

sending control messages. During the scheduled period, the sensor nodes transmit,

receive and relay data packets.


TRAMA adopts the Neighbourhood-aware Contention Resolution (NCR) algorithm

of NAMA [11] in order to determine the ownership of a slot which determines a

node’s priority to transmit at given time slot t. For any particular slot, the node

with the highest priority within the two-hop communication neighbourhood wins

the slot. TRAMA uses an Adaptive Election Algorithm (AEA) to reuse slots that

are discarded by their owners. When a winner of a slot does not have data to send,

it scans its one-hop network using the Neighbour Protocol (NP) and the Schedule

Exchange Protocol (SEP). If one-hop neighbours has the highest priority among

its own two-hop neighbours, then the one-hop neighbour is added in the Possible

Transmitter Set (PTS). The node with the highest priority in the PTS wins the

data slot.

TRAMA is not a viable solution for data-intensive WSNs because the protocol can-

not guarantee collision-free transmission since the nodes have no entire knowledge

of the two-hop neighbourhood. Secondly, the scheme generates considerable con-

trol overhead at the expense of delay which also impacts in the normalised channel

throughput. TRAMA does not implement a mechanism to prioritise the transmis-

sion of critical nodes. When the topology changes, sensor nodes waste normalised

channel throughput due to incorrect schedules and this state can degrade the overall

performance of the network.

Z-MAC and DRAND

Zebra MAC (Z-MAC) [73, 74] is built on B-MAC [69] and aims to improve through-

put and latency in multi-hop networks under different data load conditions. The

underlying MAC scheme used by Z-MAC is CSMA, and it adopts TDMA during

high contention periods. Z-MAC assigns sensor nodes a time transmission slot, but

it also allows sensor nodes to reuse slots they do not own through a contention

method which is based in CSMA with prioritised backoff times. This backoff time

acquire a random value in a range that depends on whether or not the sensor node

owns the slot. During the network creation phase, a distributed slot assignment pro-

tocol (DRAND) [75] defines a slot allocation schedule for the transmission of each


Figure 13: Frame Structure Of Z-MAC Including Detailed Slot Format

sensor nodes. The schedule guarantees that two-hop distant neighbours do not get

assigned the same slot number. In general, it has been found that DRAND protocol

causes a large overhead during the network formation, however it intends to com-

pensate this with reduced communication and therefore lower energy consumption

during the network’s lifetime.

Z-MAC divides times in slots and each slot consists of a small contention window,

followed by data transmission time, as depicted in Fig 13. During the contention

window of a slot, the sensor nodes assess the idleness of the medium to determine

whether or not their neighbours are sending a message in a particular slot. The

slot owner has a higher priority to send message, if any, than non-owners. When

the slot owner has no data to send, it allows other nodes to use its slot. The owner

of the current slot selects a random backoff time of up to To. On the contrary,

sensor nodes that do not own the current slot select a backoff time between To and

Tno, where Tno > To. Afterwards, the sender performs CSMA and if the channel

is clear, it proceeds to transmit its packet. The dynamic allocation of slot in Z-

MAC exposes the protocol to collisions during high contention periods mostly due

to hidden terminal phenomenon. In this case, Z-MAC uses congestion notification


Figure 14: FlexiTP Slot’s Assignation

(ECN) messages to prevent neighbouring nodes to contend for the same slots which

causes more overhead and reduces even further the normalised channel throughput

in a state where more data requires to be transmitted.

The main advantage of Z-MAC is that allows sensor nodes to handle low and high

data load requirements in a multi-hop network. However, it incurs in considerable

overhead both during the network creation phase and when the data load changes

from low to high. In the optimal case, Z-MAC could provide equal access amongst

all nodes whereas in data-intensive applications we strive to providing higher access

priority to critical nodes. Additionally, the contention windows of every trans-

mission slot leads to lower normalised channel throughput and latency under high

contention conditions.

FlexiTP, OST and ODA

FlexiTP [52, 53] is designed to provide end-to-end guarantees on packet delivery in a

multi-hop data gathering network, while respecting the severe energy and memory

constraints of first generation of WSNs. FlexiTP implements a TDMA schedule

in which sensor nodes only transmit and receive packets in their own time slots.

FlexiTP generates a lookup table in each sensor node during the initial global

setup phase, nodes use this table to claim or remove a slot without exchanging


Figure 15: (a) Four slot-requestors In The Network: B, F ,G, and H Need To Finda Slot-supplier. (b) B, F, and H Manage To Get Extra Slots From C, E, and KRespectively. H Uses Multiple Paths To The Base Station Whilst C, E and K StopTheir Schedule Temporarily.

information with other nodes. A node’s schedule structure is composed of non-

contiguous slots and represents a list of slots when a node should be active. The

groups of slots include: the Fault-Tolerant Listening Slot (FTS), the Receive Slot list

(RSL), Transmit Slot List (TSL), Multifunction Slot (MFS), Conflit Slot List (CSL)

and Global Highest Slot (GHS). Fig. 14 depicts an example of slot’s assignation.

An on-demand TDMA slot transfer (OST) [51] algorithm allows time slots from

one part of the network to be transferred to another part. The FTS slot can be

used to allow nodes to request extra slots from their neighbouring nodes and so

they can transfer their data at higher rates. The requesting node can specify the

number of extra slots required in a data gathering cycle and specify or change

how long these extra slots will be required. OST includes local and central slot

request modes. In local OST, a sensor node can try to get extra slots from nodes

in its neighbourhood, a slot-supplier can lend its data gathering slot if available,

as shown in Fig. 15. In a centralised OST, the base station performs sensor data

analysis, processing and TDMA slot re-allocation. The main advantage of the latter


algorithm is the introduction of energy saving strategies through the adaptation of

the length of the data gathering cycle.

An on-demand aggregation (ODA) [51] algorithm enables nodes to perform slot

aggregations based on current network conditions and capacity constraints. ODA

allows sensor nodes to reserve some of their slots according to a desired data aggre-

gation compression ratio. Nodes may have different priorities of data, for example,

abnormal or fast changing data are more important that slow changing data. In

this case, it is desirable to let nodes with low priority packets to aggregate their

packets in order to send their data to the base station. The aggregation ratio is de-

cided in the central base station using downstream queries to nodes. Additionally,

the protocol assumes that mathematical operators as SUM, AVG, MAX, MIN or

MEDIAN could be applied to the data.

FlexiTP TDMA schedule are designed for application with scalar data in which

an event can trigger an increase in the sensed scalar data. However, the data

generation rate is slower than the application we consider for data-intensive WSNs.

The OST algorithm could effectively help to handle those localized data generation

increases in many circumstance, however, the availability of slots highly depends

in the network topology and energy conditions of the sensor nodes. Finally, the

ODA algorithm is a initial approach for weighted fair access amongst node, but

it differs considerably from our protocol PRIMAC because ODA requires central

coordination.

Chapter 3

Methodology

In this chapter we present the methods used in this research project to simulate

and validate the performance of the adaptive 802.15.4 MAC scheme for homoge-

neous conditions and PRIMAC. Specifically, we discuss the assumptions and met-

rics employed in the evaluation of a network. We describe the adopted analytical

modelling method, the components of the ns-2 network simulator which is used a

validation method and finally the experimental method that employs real wireless

sensor nodes.

3.1 Evaluation Assumptions

To evaluate the performance of our proposals we utilise the following network as-

sumptions:

1. Static Nodes. PRIMAC and adaptive IEEE 802.15.4 are designed for networks

with star topology whose sensor nodes are immovable.

2. Location-Independent Nodes. Scalar and data-intensive nodes are assumed to

be in half duplex range of communication with each other. As a consequence,

the location of each node in space is not relevant for performance evaluation.

37

38 CHAPTER 3. METHODOLOGY

3. Network Size. PRIMAC is designed to operate near-optimally in networks

where the number of nodes is unknown providing that the network size is

within certain range. For this reason, we assume that the network size could

vary during the lifetime of a WSN. For adaptive IEEE 802.15.4, the network

size is calculated for optimal channel utilisation.

4. Generic Application. The proposals of this dissertation are evaluated using

analytical modelling, ns-2 simulator and an experimental sensor nodes testbed

where a packet generation is simulated using constant bit stream, event-driven

streams and Poisson distributed packet arriving forms. In all cases, the con-

tent of packets are filled with data that emulates data-intensive scenarios but

are not related to any specific industrial, commercial or scientific application.

5. Wireless Communication Channel Problems. In the star topology network

considered in this project, the negative effects of wireless phenomenons like

hidden terminal and exposed terminals problems [45, 65, 88] are not present

since all nodes are in range of communication. Any other system in the

vicinity of our WSN should be assumed that it does not interfere with the

communication channel.

6. Radio Signal Propagation. The chosen evaluation methods, ns-2 simulator

and experimental hardware, incorporate the effects of transmission losses in

the propagation of radio signals over the space.

3.2 Evaluation Metrics

In order to meet the requirements of data-intensive WSNs we will evaluate the MAC

protocols against the following metrics:

1. Normalised Channel Throughput, denoted as S. It is defined as the fraction

of time that the wireless medium is occupied by successful transmissions over

3.2. EVALUATION METRICS 39

the total active time of the network.

S =SuccessfulPacketTransmissionT ime

TotalNetworkActivetime. (1)

The target in data-intensive application is a protocol that reaches maximum

normalised channel throughput even at the expense of high energy consump-

tion.

2. Packet Delivery Ratio Ps. It is defined as the fraction of packet successfully

received in the destination node over the total number of transmitted packets.

Ps =NumberofPacketsSuccessfullyReceived

TotalNumberofTransmittedPackets. (2)

The objective in a data-intensive application is to guarantee high levels of

packet delivery for both scalar and critical nodes. Reliable packet delivery is

a challenge in every contention protocol based on Aloha and CSMA, there-

fore, we require smart and adapting mechanism to maximize this performance

metric.

3. Inter-Successful-Packet Time. It is defined as the elapsed time between two

successful packet transmissions from same or different nodes. This metric

evaluates the delay in the packet transmissions. In this thesis, we use slot as

the unit of time. A slot is a parameter of MAC protocols that is equivalent

to a configurable number of seconds

Inter − Successful − PacketT ime = Packeti+1 − Packeti. (3)

A data-intensive protocol should guarantee low transmission delay in the up-

link data flow from sensor nodes to the sink. This requirement is of signifi-

cant importance in networks whose critical nodes carry streaming/multimedia

data.


3.3 Evaluation Methods

In this thesis, we will use three different methods to evaluate the performance of

WSNs. These are:

1. Analytical Models

2. Network Simulations

3. Experimental testbeds

The use of different methods will guarantee the validity of the proposed designs.

3.3.1 Analytical Modelling

The main tool that we use throughout this thesis to evaluate the performance of

MAC protocols is the formulation of a mathematical model to mimic the functional

behaviour of a protocol under defined conditions and finite number of inputs. In

literature, we can find two prominent types of analytical models: Markov-based

[14, 15, 22, 28, 42] and mean-field analysis [46, 56, 76, 99]. Markov-based analysis

is a widely used technique for modelling wireless networks which use CSMA for

medium access since the seminal work of Professor Bianchi [16]. In this technique

a Markovian model of the system is developed and its state transition probabilities

need to be found. The state space of the model increases with both the complexity of

the protocol and the number of users in the system. Pollin [70] and Misic [60, 61, 62]

presented Markovian models for the MAC protocol of IEEE 802.15.4. Pollin et al.

found a solution for saturated traffic conditions in which sensor nodes have always

backlogged packets to transmit and the buffer is considered to be of infinite size.

Misic et al. presented another solution for saturation; however, simulation results

show a deviation from their own model.

Alternatively, we will adopt a mean-field approach, also known mean-value or re-

newal process, for our analytical models of the IEEE 802.15.4 and PRIMAC since

3.3. EVALUATION METHODS 41

it has proven to be a successful technique for evaluating CSMA-CA MAC proto-

cols. In this technique, the analysis consists in finding the average duration of

the contention period of an individual node and the probability of success at the

end of each contention period. Mean-field approach can be used for saturated and

unsaturated traffic conditions with packet generation driven by constant, periodic

or Poison-based profile. Although, sensor nodes have limited buffering capabilities

[41, 101] in reality, it is very common to assume initially that the buffer of nodes

is infinite in order to facilitate the analysis of networks under saturated traffic con-

ditions. Ultimately, the minimum buffer size of a node could be estimated from

the initial derivations. Mean-field could also be used to find optimal network de-

signs for sensor nodes with homogeneous and heterogeneous data-intensive traffic

requirements. The outputs of this type of model include the optimal MAC param-

eter values (network size, data load, etc.) of the considered protocol. In our study,

we found that mean-field approach is very tractable and produces very accurate

results. The accuracy could be confirmed by the study of many scientist, including

[56].

3.3.2 Network Simulator 2 (ns-2)

The other main tool that we utilise in this project to evaluate the performance of

IEEE 802.15.4 and PRIMAC is ns-2 version 2.33 [66]. The network simulator version

2 [38, 71] is an event-driven and object oriented simulation tool that is widely used in

the scientific community to study the dynamic nature of communication networks.

NS-2 provides users with a way of specifying network protocols and simulating

their corresponding behaviour. It was developed under the VINT project as a

collaboration between UC Berkeley, USC/ISI, LBL, and Xerox PARC.

NS-2 is written in two languages: C++ for back-end and Object-oriented Tool

Command Language (OTcl) for the front-end, as shown in Fig. 16. C++ defines the

internal mechanism of the simulation objects whilst OTcl assembles and configures

objects, and schedule the discrete events that sets up a simulation. ns-2 uses a fast


ns-2 Shell Executable Command (ns)

Simulation

Objects

Simulation

Objects

C++ OTcl

Tcl

Simulation

Script

Simulation

Trace

File

NAM

(Animation)

Xgraph

Matlab

Figure 16: Basic Architecture Of ns-2

run-time speed language, C++, to implement the network protocols and algorithms

because it can efficiently manipulate bytes, packets and large data sets. On the other

hand, OTcl is the best choice for the configuration of a simulation scenarios because

it gives low turn-around time for tasks such as finding bugs, change network model,

recompile, and run scripts. The configuration of a simulation scenario runs only

one, so total simulation is not affected. C++ and OTcl components are linked

together using the TclCL interface.

Variables in the OTcl domain, known as handles, are mapped to a C++ object. A

handle (e.g., n as node handle) is a string (e.g., o10) in the OTcl domain, and its

functionality (e.g. receiving a packet) is defined in the C++ object (e.g. of class

Connector). In the OTcl domain, a handle acts as a front-end which interacts with

users and other OTcl objects. ns-2 provides a large number of built-in C++ objects

that covers an extensive list of network protocols. For PRIMAC, we created our

own C++ objects and use a OTcl configuration to put together those objects.

To run ns-2, a user inputs arguments in a command shell or generally enters a

simulation scripting file written in Tcl, see Fig. 16. The scripting file contains the

description of the network scenario and protocols to be loaded into a simulation

run. The ns-2 output can be given in the form of log files and/or animations. The

two standard log files are:


1. Trace File. This file records the time that events occurred during a simu-

lation run. Specifically, it contains the details of a node activities, packet

transmission and receptions through the layers of a node, type of packet,

packet collisions and buffer drops, etc. Using this information, we can pro-

duce performance graphs.

2. Network Animator Trace File. The NAM trace file is used by the network

animator tool to depict the network topology, location of nodes, communica-

tion links and packet transfer. Similarly, the content of this file can be used

for custom analysis and plots.

Simulation Process

In general, the key steps to define a simulation scenario are:

1. Design. In this step, the user must the determine the simulation purpose,

the network configuration and protocols, the input traffic and assumptions,

whether or not new C++ and OTcl objects are required, and finally the

performance metrics.

2. Configuration. The TCL script with the simulation scenario is created. The

content of the Tcl script can be divided in two sections. First, the user add

the network components (e.g., nodes, WPAN, propagation model, trace files)

and configure them according to the simulation design (e.g., communication

links between nodes) . Second, the user scheduled time triggers for data

transmission.

3. Post Processing. At the end of the execution of a Tcl script, the user can use

the trace files to analyses the results.


Network Layer

Propagation

Model

Data Layer

Physical Layer

Queue

Type

Antenna

Type

Wireless Channel

NODE

Upper Layers

Figure 17: Architecture Of A ns-2 Node

Simulation Framework

In ns-2, a node is basic network component that forwards packets to other nodes

through a connecting link and delivers a received packet to the attached transport

layer. A node encapsulates the functionalities of the network layer, data layer, and

physical layer, and also includes the type of queue, antenna, channel and radio

signal propagation method, as seen in Fig. 17.

In our simulations, we configured nodes to use a drop-tail queue and the network

layer uses a simple agent witch forwards packets to destination nodes without cal-

culating special routes. Each node utilises an omnidirectional antenna to access

the common wireless channel. The wireless channel is an object that works as an

interface between the physical layer of the nodes and its function is to signal when


MAC

PHY

Physical Medium

Upper Layers

SSCS

802.2 LLC

Figure 18: WPAN Device Architecture

a node is sending or receiving packets. The physical layer is powered by the wire-

lessPhy module which is an interface between the MAC sublayer and the wireless

channel. It also links to the propagation model in order to calculate the received

signal power in each node.

In the case of PRIMAC, we developed a new MAC module that introduces the

necessary features to achieve non-uniform contention and weighted access priority

for the data-intensive sensor nodes. This MAC modules sits directly on top of the

standard ns-2 wirelessPhy and below the network layer.

Wireless Personal Area Network (WPAN) MAC Module

In the case of IEEE 802.15.4 scenarios, the nodes are configured to use the WPAN


module developed by Zheng and Lee for Samsung/CUNY [106] which defines the

functionalities of the MAC sublayer, adds extra features to the standard wirelessPhy

module and implements the service specific convergence sublayer of IEEE 802.15.4,

as depicted in Fig. 18.

The MAC sublayer features of WPAN includes:

1. Carrier sense and multiple access with collision avoidance (CSMA-CA) access

method,

2. Beacon transmission and time synchronisation,

3. Association and Disassociation of nodes,

4. Tree topology formation,

5. Direct and indirect transmission,

6. Packet filtering and error modelling,

7. Enhance Nam animations.

The IEEE 802.15.4 physical features in WPAN are:

1. Energy detection (ED) within the current channel,

2. Clear channel assessment for CSMA-CA,

3. Link quality indication (LQI) for received packets,

4. Multiple channel operation.

Radio Signal Propagation Method

The propagation model to be adopted in all simulation scenarios is the two-ray

ground-reflection model, which allows to predict the power loss of a signal at the

receiving node. This method has been proved to be very suitable for the simulation

of WSNs [51]. The two-ray ground-reflection method considers not only the losses


due to direct path transmission in a direct path (free space model) but also the

reflections due the existing a ground surface giving more accurate results at a long

distance [72]. Another advantage of the two-ray ground-reflection model is that the

received power is deterministic for the same inputs allowing a direct comparison

between MAC protocols. Then, the received power by a node is calculated in ns-2

as

PRX(d) =PTXGTXGRXhTX

2hRX2

d4L, (4)

where

d is the distance between the transmitting and receiving node,

PRX(d) is the signal power in the receiving node at a distant d,

PTX is the generated power in the transmitting node,

GTX is the antenna gain of the transmitting node,

GRX is the antenna gain of the receiving node,

hTX is the antenna height of the transmitting node,

hTX is the antenna height of the receiving node,

L is the power loss due to system.

In our simulation scenarios for IEEE 802.15.4 and PRIMAC, we assume that the

sensor nodes have omnidirectional antennas of gain equal to unity, GRX = GTX = 1,

height equals to 1.5 meters, hRX = hTX = 1.5, transmission power equals to 0.2818,

PTX = 0.2818 and no system loss, L = 1. These assumptions allow us to model

generic radio characteristics where no special gain is added to any specific node or

transmission path. The assumptions are equivalent to similar studies on wireless

sensor networks [51].

3.3.3 Experimental Sensor Nodes

We developed experimental testbeds of sensor nodes in order to corroborate the per-

formance of data-intensive WSNs over a real wireless environment. A node is formed


with small microcontroller devices which are equipped with a radio transceiver that

operates in the 900 MHz band. We implemented the specific optimal protocols at

the MAC level, and we also developed specific application layer programs that could

test the maximum performance of the network.

An experimental tesbed is an important validation method because it allow us to

evaluate some of the assumptions of the analytical models and ns-2 simulations.

The considered assumptions include

1. Nodes in the entire network are in range of communication,

2. Wireless links are asymmetric and time-independent,

3. In unsaturated conditions, periodical packet generation process follows a poi-

son distribution,

4. Time-synchronization among nodes and precise clear channel assessment are

feasible,

5. Optimal normalised channel throughput is achievable.

Chapter 4

Data-Intensive Networks with

Optimal IEEE 802.15.4

The IEEE 802.15.4 standard specifies the medium access protocol (MAC) and

physical layer radio that is recommended for first generation of WSNs. This

first generation was devised for WSNs with low data rate and low power applica-

tions, however we will demonstrate in this chapter that the IEEE 802.15.4 medium

access mechanism can be enhanced by choosing appropriate MAC parameters to

a degree where data-intensive scenarios are achievable. We consider that optimal

network designs and protocol enhancements in IEEE 802.15.4 could facilitate a

seamless migration from low data rate to more demanding cases in scientific and

industrial applications.

In IEEE 802.15.4, the contention access period of the MAC protocol is based on a

carrier sense multiple access with collision avoidance algorithm that enables sensor

nodes to access a common shared wireless medium. Although the recommended

MAC protocol is primarily designed to save energy, its default parameters are not

necessarily optimized in terms of normalised channel throughput.

With this consideration in mind, we analyse in this chapter how the CSMA-CA al-

gorithm of IEEE 802.15.4 can be optimized in order to achieve maximum normalised

49

50CHAPTER 4. DATA-INTENSIVE NETWORKSWITHOPTIMAL IEEE 802.15.4

channel throughput for WSNs under unsaturated and saturated data traffic con-

ditions. By saturation we mean that a node in the network always has packet

awaiting to be sent as it occurs with data-intensive nodes. We show in this chap-

ter that maximizing throughput can further enhance energy saving and improve

network lifetime expectancy compared to saturated network settings that use the

default MAC parameters of IEEE 802.15.4. We will aim to find in this chapter the

optimal MAC parameters that yield optimal normalised channel throughput.

The contributions of this chapter are:

1. Introduce a mean-field approach to model the IEEE 802.15.4 CSMA-CA algo-

rithm under saturated and unsaturated traffic conditions. This model allow us

to obtain fundamental network metrics such as normalised channel through-

put, packet delivery ratio and packet transmission delay, and evaluate the

network for a variety of packet arrival rates, network size and packet lengths.

2. Derive a simple expression for optimal throughput received at the sink in

a network with star topology. This mathematical expression provides the

maximum achievable normalised channel throughput in a WSN, and therefore

it represents a considerable contribution to the knowledge of IEEE 802.15.4

CSMA-CA algorithm.

3. Design networks with optimal throughput by choosing appropriate MAC pa-

rameters. Specifically, we present how to realise WSNs with homogeneous

conditions where packet generation and arrival in nodes is intensive for all

transmitting nodes.

The rest of the chapter is organized as follows. In Section 4.1, we review the exist-

ing approaches for analyzing the IEEE 802.15.4 standard. In Section 4.2, the IEEE

802.15.4 CSMA-CA algorithm is described. In Section 4.3, we present our analytical

model for unsaturated conditions and revisit the literature for saturated conditions.

Section 4.4 corroborates the validity of the model by comparing analytical and ns-2

4.1. RELATED WORK 51

simulations. Section 4.5, we study the impact of different MAC layer parameters

on normalised channel throughput. Section 4.6 derives the expression for optimal

normalised channel throughput and present the proposals for the design of optimal

WSNs. In the next section ( Section 4.7) we use ns-2 simulations to verify the nor-

malised channel throughput of the optimal designs and evaluate their performance.

Finally, Section 6.5 concludes the chapter with some remarks.

4.1 Related Work

There exist a number of analytical models in the literature studying the performance

of the IEEE 802.15.4 MAC protocol. Misic et al. [60, 61, 62] proposed several

models based on discrete time Markov Chains and M/G/1/K queues to evaluate

the impact of MAC parameters in networks with saturated and unsaturated uplink

and downlink traffic. The authors made various simplifications to their models

in order to reduce the complexity of the analysis, i.e., unlimited retransmissions

of unacknowledged packets. Although these proposals are promising, the main

common disadvantage is the considerable discrepancy between their simulation and

analytical results, as shown in their paper figures. We show later in this chapter

that our analytical model is more tractable and at the same time improves on the

accuracy of the results.

He et al. [30, 31] presented a model based on two two-dimensional Markov chains

for networks under saturated conditions. The objective of this study is to pre-

dict the performance with the highest attainable accuracy. Consequently, it does

not analyse the maximum achievable normalised channel throughput nor consider

networks under unsaturated conditions, where this maximum is reached.

Pollin et al. [70] extended the Markov-based analysis of the well-known IEEE 802.11

protocol in [16] to capture the different behaviour in the IEEE 802.15.4 MAC proto-

col for saturated and periodic traffic conditions. Shu et al. [80] proposed a simplified

Markov model to evaluate the protocol performance under similar saturated and


periodic traffic conditions.

Tao et al. [89] also extended the Markovian analysis presented in [16], but only for

network under saturated traffic conditions. In addition, the study provided recom-

mendations for throughput improvements by adjusting the CSMA-CA parameters,

however, these enhancements target specific network settings under saturated traf-

fic conditions, while we provide a generic derivation for the maximum normalised

channel throughput for both saturated and unsaturated networks..

In a different approach, Ling et al. [57] utilized a method based on a renewal

theorem first introduced in [44] to analyse the saturated network performance. The

authors also suggested an extension of their model for unsaturated networks. This

extension, however, is not straight forward as it requires adjusting the definition

of the renewal cycle to the actual service time of a MAC frame in an unsaturated

network. In this chapter we present our simple mean-field model that accurately

predicts the protocol performance under unsaturated conditions.

4.2 The IEEE 802.15.4 Protocol

The IEEE 802.15.4 standard in [93] defines the physical layer (PHY) and medium

access control sublayer (MAC) for wireless sensor networks that require low-power

consumption, low-rate and low-cost. The definition of upper layers is still open for

design and optimization. Nevertheless, up to now, the ZigBee specification [108]

has become the most adopted implementation atop the IEEE 802.15.4 MAC.

The physical layer is based on the Direct Sequence Spread Spectrum (DSSS) spread-

ing technique and provides a total of 27 operational frequencies: 16 channels in 2.4

GHz, 10 channels 915 MHz and 1 channel 868 MHz. The modulation and nominal

data rate of each band are 250 Kbps using Orthogonal Quadrature Phase Shift

Keying (OQPSK) at 2.4GHz, 40 Kbps using Binary Phase Shift Keying (BPSK) at

915 MHz, and 20 Kbps using also BPSK at 868MHz.

4.2. THE IEEE 802.15.4 PROTOCOL 53

The MAC sublayer provides two different channel access methods: a beacon-enabled

mode and a nonbeacon-enabled mode. In a beacon-enabled mode, the coordinator

defines a superframe structure that is bounded for network beacon packets and is

divided into 16 equally sized slots. The active portion of the superframe is composed

of three parts: a beacon, a contention access period (CAP) and a contention-free

period (CFP). The coordinator transmit the beacon at the starting edge of the su-

perframe, and the CAP shall commence immediately after the beacon transmission.

The CFP is an optional period, if present, follows immediately after the CAP and

extends to the end of the active portion of the superframe. Guaranteed time slots

for dedicated communication could only be allocated within CFP.

The specification incorporates a mechanism to access the medium within the con-

tention access period (CAP), in which nodes transmit uplink data to a coordinator

using the Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) algo-

rithm. Time is divided into backoff slots of fixed duration and the nodes synchronize

with the coordinator upon the reception of a beacon.

A node with a pending packet to transmit starts with a random backoff period,

denoted as b, picked uniformly in the range of [0, CW ], where CW=2BE-1 is the

contention window and BE is the backoff exponent used in the generation of the

contention window for each transmission attempt. For the first attempt BE is made

equal to a system parameter macMinBE (by default 3) that sets the minimum

contention window for backoff.

At the end of the backoff, the node performs a clear channel assessment (CCA) by

sensing the idleness of the medium. If the channel is idle for two consecutive slots

(i.e. two successive CCAs without failure), the node proceeds with the transmission

of the packet and concludes the backoff cycle for that packet. Otherwise, if the

channel in any of the two slots assessed is found to be busy, the node initiates a

new backoff before performing a retransmission attempt until reaching the system

maximum allowable number of attempts M=macMaxCSMABackoffs+1 (by default

M=5). The algorithm terminates with a channel access failure status and the packet


R1

T1 T2 T3 Ti TK

R2 R3 RK...

...

Arrival of

Packet 1

Time

Arrival of

Packet 2

Arrival of

Packet 3

Arrival of

Packet iArrival of

Packet K

Ri

b0 Att0 bj Attj bM-1 AttM-1Ri = total number of attempts for packet i

bj= Backoff stage j for packet i......

Figure 19: Packet Transmission Of A Node In An Unsaturated Network

is discarded after the maximum number of attempts is reached. The contention

window might be increased in each subsequent attempt to achieve higher probability

of success. To do so, BE is set as the minimum min(BE+1,macMaxBE) where

macMaxBE is another system parameter (by default 5) that limits the maximum

contention window.

According to the IEEE 802.15.4 standard, the use of acknowledgement packets is

optional and it must be explicitly requested by the transmitting node. Especially

for wireless sensor networks with high throughput demands, which is the scenario

of our study, ACK is usually disabled in order to gain throughput in applications

with redundant information (e.g. the same information can be obtained from the

different nodes within a close proximity).

4.3 Analytical Model

Consider a single-hop star topology network consisting of N homogeneous nodes,

each reporting uplink data to a sink. Assume there is no data acknowledgements,

nor contention free period (CFP). The length of the CAP is assumed to be large

enough so that a node can utilize all the allowable transmission attempts if neces-

sary. The wireless medium is assumed to be error-free and all nodes are in range

of communication. As a consequence, the hidden terminal and exposed node prob-

lems are not present in this framework. Note that in contrast to the legacy IEEE

802.11 networks that use MAC packets of up to 2346 bytes (including header and

4.3. ANALYTICAL MODEL 55

cyclic redundancy check) [37], the maximum MAC packet size of 802.15.4 is just

127 bytes, and hence, the likelihood of having a lost packet due to hidden collisions

is much reduced.

4.3.1 Sensing Rate for Unsaturated Case

For unsaturated networks we assume that packets arrive at each node according to

a Poisson process with rate λ (packets/slot) such that a node has a period of time

when its queue is empty, i.e. a node has no packet to send. Furthermore, assume

that the buffer is big enough, so packets drop due to overflow cannot occur.

Let Ri be a random variable (r.v.) that represents the number of sensing attempts

(CCA) for a packet i and let Ti be the inter-arrival time between packets i and

i + 1. Then, the probability that a tagged node senses the channel in a given slot

(i.e. sensing rate) ϕ is given by

ϕ =R1 +R2 + ...+RK

T1 + T2 + ...+ TK

, (5)

where K is the number of packets arriving at the tagged node over a sufficiently

long period of time. This is an exact expression for ϕ on unsaturated networks and

can be re-written using the mean-field value as

ϕ =KE[R]

K( 1λ)

= λE[R], (6)

where E[R] is the average number of sensing attempts per packet by a node. Note

that the denominator in (6) is based on the fact that the arrival of packets forms a

Poisson process with average inter-arrival time 1/λ between packets. The concept

for the calculation of the sensing rate is illustrated in Fig. 19.

Let γ be the probability of failing a sensing attempt by finding a busy channel

while performing the two CCAs (i.e. finding the channel is busy in either the first

or second CCA). For tractability, assume γ to be constant and independent of the

past attempts, then E[R] can be computed as

E[R] =M−1∑i=0

γi =1− γM

1− γ. (7)


From (6) and (7), we obtain the sensing rate for unsaturated conditions as

ϕ = λ(1− γM

1− γ). (8)

4.3.2 Sensing Rate for Saturated Case

As opposed to the unsaturated case, the sensing rate ϕ in saturated networks can be

calculated as E[R]/E[X] according to [57] where X is defined as a r.v. representing

the packet service time and E[X] is the expectation of X.

E[X] is calculated by averaging over different sensing attempts. A node will be

in the ith attempt with a probability γi and have an average backoff duration of

bi =2BE−1

2. The service time also includes the sensing duration of 2 slots if successful

and of δ = 1 + (1 − α) = 2 − α otherwise where α is the probability of finding a

busy channel for the first CCA, and a packet transmission time of L slots after a

successful attempt. Thus E[X] is given by [57]

E[X] =M−1∑i=0

biγi + (L+ 2)(1− γM) + δ

M∑i=1

γi, (9)

Finally, the sensing rate in saturated network is given as

ϕ =

∑M−1i=0 γi∑M−1

i=0 biγi + (L+ 2)(1− γM) + δ∑M

i=1 γi. (10)

4.3.3 Sensing Failure Probability

Denote β as the probability of finding a busy channel for the second channel sam-

pling CCA. The sensing failure γ occurs when the tagged node finds the medium

busy in either first or second channel sampling, i.e.

γ = α + (1− α)β. (11)

The derivations of α and β have been presented in [70, 27, 102]. The tagged node

finds the medium busy in the first CCA when other nodes are already transmitting

in that slot. This happens when at least one of the other N-1 nodes starts sensing

4.3. ANALYTICAL MODEL 57

in a previous slot, which occurs with probability (1−(1−ϕ)N−1), and given that the

other nodes do not fail their first and second CCAs, which occurs with probability

(1− α) and (1− β), respectively. Then, the medium is elapsed for L slots, i.e.

α = L(1− (1− ϕ)N−1)(1− α)(1− β). (12)

The tagged node finds the medium busy in the second CCA when at least one of

the other N-1 nodes commences to transmit in that slot. β can be derived as the

probability of having at least one of the other N-1 nodes start sensing in a slot

previous to tagged node’s first CCA slot, which occurs with probability (1 − (1 −

ϕ)N−1), and given that the other nodes succeed in their second CCA, this is (1−β).

Then, we have

β = (1− (1− ϕ)N−1)(1− β). (13)

Substitute (12) and (13) into (11) to obtain the sensing failure probability as

γ =(L+ 1)(1− (1− ϕ)N−1)

(L+ 1)(1− (1− ϕ)N−1) + 1. (14)

In summary, equations (8) and (14) establish a fixed-point formulation from which

ϕ and γ for unsaturated conditions can be obtained using numerical techniques.

For saturated conditions, the fixed-point equations are (10) and (14).

Let λSAT be the arrival rate at which a node becomes saturated. In this setting

the tagged node can be modelled as an M/G/1 queue with the packet service time

equals to 1/λSAT which implies zero probability that the node is idle. Note that

the network transition to saturation when

λSAT = 1/E[X]. (15)

where E[X] can be obtained from (9) and using the fixed-point solution for equa-

tions (10) and (14).


4.3.4 Throughput Analysis

Given that the tagged node is transmitting a packet, the conditional probability

(refer to as η) that the transmission is successful is that none of the other N-1

nodes attempts channel sensing in the same slot, η = (1− ϕ)N−1. This probability

is conditioned on the fact that the tagged node has made a sensing attempt in

that slot, then successfully performed two CCAs and found the channel idle in both

previous two slots just before its transmission, this is ϕ(1 − γ). Therefore, the

probability of successful transmission in a slot is

Psslot = ϕ(1− γ)(1− ϕ)N−1. (16)

As there are N homogeneous nodes, the average normalised channel throughput S

at the MAC layer is the portion of time the channel has been used for successful

transmissions and is expressed as

S = LNϕ(1− γ)(1− ϕ)N−1. (17)

4.3.5 Network Lifetime

The tagged node is an electronic device equipped with a radio transceiver, i.e.

Chipcon CC2420 [91], that has four states of operation: shutdown, idle, transmit

and receive. Assume that tagged node is in shutdown state when its buffer is empty,

in idle state when performing backoff before a transmission attempt, in receive state

when sensing the medium (CCAs), and in transmit state when sending a packet.

Moreover, assume that the energy consumption in shutdown state is negligible, since

shutdown power is at least a factor 5000 lower than any other state according to [17].

Then, the average energy consumption per packet transmission in a tagged node

(refer to as Epacket) can be calculated by summing the products of the average period

of time a node spends in each state for a packet transmission and its corresponding

power consumption

Epacket = PidleTidle + PtxTtx + PrxTrx. (18)

4.4. MODEL EVALUATION 59

Table 1: Chipcon CC2420 Transceiver [17]

Parameter Description ValuePidle Power in idle state 712 µWPrx Power in receive state 35.28 mWPtx Power in transmit state 30.67 mW

where Pidle, Ptx and Prx are known from the radio specifications and presented

in Table 1. The average idle time is given by the sum of the average backoff

periods Tidle =∑M−1

i=0 biγi. The average transmit time is given by the product of

the length of a packet and its corresponding probability of success in M attempts,

Ttx = (1 − γM)L. The average receive time accounts for the number of CCA slots

sensed in a packet transmission Trx = δ∑M

i=1 γM + 2(1− γM).

Let Tnet be the network lifetime that represents the average time a network operates

until a node dies out and let Ebat be the stored energy in tagged node. In a

homogeneous network all nodes should live on average the same amount of time.

Then, the network lifetime is given by

Tnet =Ebat

λEpacket

. (19)

4.4 Model Evaluation

To verify the accuracy of the proposed unsaturated model, we compare the an-

alytical results with simulations. The simulations were performed using the ns-2

simulator (version 2.33). We simulate a beacon-enabled, star-topology sensor net-

work where we choose the MAC and physical layer parameters consistently with the

default values specified in [93]. In particular, the minimum and maximum back-

off exponents are macMinBE=3 and macMaxBE=5, respectively. The maximum

backoff stage is set to M=5 (macMaxCSMABackoffs=4). The length of a packet (in

backoff slots) is L=8 representing a 640 bits packet in a network, since the backoff

slot is 320µsecs and the data transmission rate is 250Kbps.

In Fig. 20 and Fig. 21 we show the normalised channel throughput obtained from


0 0.005 0.01 0.015 0.02 0.0250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

PACKET ARRIVAL RATE λ (pkts/slot)

CH

AN

NE

L TH

RO

UG

HP

UT

S

AnaSimAna [60]Ana [70]Ana [80]

Figure 20: S for Different Packet Arrival Rates And N=20

0 0.005 0.01 0.015 0.02 0.0250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


CH

AN

NE

L TH

RO

UG

HP

UT

S

AnaSimAna [60]Ana [70]Ana [80]

Figure 21: S for Different Packet Arrival Rates and N=30

4.5. PERFORMANCE OPTIMIZATION 61

the analytical models and simulation results as a function of the packet arrival rate

using two different network sizes N=20 and N=30, respectively. The analytical

results depicted in the figures are obtained by using the fixed-point formulations

(8) and (14) or (10) and (14) depending on whether λ is less than λSAT or not,

respectively.

Observe that the analytical results closely match with the simulation compared to

the saturated models presented in [70] and [80], and the unsaturated model in [60].

The latest is the most similar unsaturated study of IEEE 802.15.4 we could found

in the literature. The model is characterized by Poisson arrivals but differs from

our scenario due to the use of unlimited retransmissions until a packet is positively

acknowledged, and it is also limited to small packet arrival rates. In contrast, our

proposal extends the arrival range analysis and as expected, each network saturates

at a given arrival value and after that the normalised channel throughput remains

constant. Moreover, notice that the optimal throughput is around 0.56 for both

cases and is reached under unsaturated conditions.

4.5 Performance Optimization

The normalised channel throughput of a network depends on the MAC layer param-

eters M, BEmin and BEmax. In Fig. 22 and Fig.23 we can see the impact of each

parameter on a network of N=20 and L=8. Note from Fig. 22 that a higher number

of attempts improves the performance under light packet arrival rate (λ < 0.005

pkts/slot) because less packets are discarded. In contrast, the combination of high

values of λ and M causes more collisions and then a degradation of throughput.

Fig. 23 and 24 show that higher values of macMinBE and macMaxBE can increase

the normalised channel throughput under saturated conditions since longer backoff

duration bi reduces the probability of collisions. However, these changes also reduce

the value of λSAT , which in turn limits the maximum number of packets these

network optimizations can handle. Therefore, in the next section we extend the


0 0.005 0.01 0.015 0.02 0.0250

0.1

0.2

0.3

0.4

0.5

0.6

0.7


CH

AN

NE

L T

HR

OU

GH

PU

T S

Ana M=1,BEmin=3,BEmax=5



Figure 22: S For Different M

0 0.005 0.01 0.015 0.02 0.0250

0.1

0.2

0.3

0.4

0.5

0.6

0.7


CH

AN

NE

L T

HR

OU

GH

PU

T S

Ana BEmax=3,M=1,BEmin=3



Figure 23: S For Different BEmax

4.6. OPTIMAL NORMALISED CHANNEL THROUGHPUT 63

0 0.005 0.01 0.015 0.02 0.0250

0.1

0.2

0.3

0.4

0.5

0.6

0.7


CH

AN

NE

L T

HR

OU

GH

PU

T S

Ana BEmin=1,M=1,BEmax=5



Figure 24: S For Different BEmin

analysis to find the optimal normalised channel throughput when λ, L and N are

also considered.

4.6 Optimal Normalised Channel Throughput

In this section, we derive the optimal normalised channel throughput received at

the sink given in (17). By substituting (14) into (17) we can have

S = LN

[1

ϕ

(L+ 2

(1− ϕ)N−1− (L+ 1)

)]−1

. (20)

To maximize the normalised channel throughput the following minimization is re-

quired

min

[1

ϕ

(L+ 2

(1− ϕ)N−1− (L+ 1)

)]. (21)

Taking the derivative of (21) with respect to ϕ and making this equals to zero, we

can obtain the corresponding optimal sensing rate ϕopt which is the solution of the

following equation

(L+ 1)(1− ϕopt)N +N(L+ 2)ϕopt − (L+ 2) = 0, (22)


For ϕopt ≪ 1 and N > 1, the following approximation can be used to obtain an

explicit expression for ϕopt

(1− ϕopt)N ≈ 1−Nϕopt +

N(N − 1)

2ϕ2opt, (23)

N(N − 1) ≈ N2,

which gives

ϕopt ≈√2L+ 3− 1

(N − 1)(L+ 1). (24)

Let γ be the corresponding sensing failure probability that a tagged node experi-

ences providing that the normalised channel throughput is maximized. This can be

obtained from (14) as

γ =(L+ 1)(1− (1− ϕopt)

N−1)

(L+ 1)(1− (1− ϕopt)N−1) + 1. (25)

Based on the same approximation ϕopt ≪ 1 as in (23) we have that

γ ≈(√2L+ 3− 1)

[1− (N−2)(

√2L+3−1)

2(N−1)(L+1)

](√2L+ 3− 1)

[1− (N−2)(

√2L+3−1)

2(N−1)(L+1)

]+ 1

. (26)

Notice that we can approximate (N−2)N−1

≈ 1 when N ≫ 1 and it can be seen that

γ is then constant and independent of N . Finally, the optimal normalised channel

throughput (refer to as Sopt) can be obtained from (17), (24) and (26) as

Sopt =(

NN−1

) (L

L+1

) (√2L+ 3− 1

)x(1− ϕopt)

N−1(1− γ).(27)

Figure 25 shows Sopt as a function of length of packet for different network sizes

(N=10,N=20,N=30). In general, the optimal channel throughput increases with

respect to Lmainly because the medium is occupied longer time for larger successful

packets. Although these big packets generate higher sensing failure probability γ,

the probability of having a collision reduces due to a lower optimal sensing rate.

A comparison between the optimal normalised channel throughput and the nor-

malised channel throughput under saturated conditions for different L and N is


2 4 6 8 10 120.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

LENGTH OF PACKET L (Slots)

OP

TIM

AL C

HA

NN

EL T

HR

OU

GH

PU

T

So

pt

Ana N=10

Ana N=20

Ana N=30

Figure 25: Sopt For Different Length Of Packet L

05

1015

1020

3040

500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

LENGHT OF PACKET L (slots)NETWORK SIZE (Nodes)

CH

AN

NE

L T

HR

OU

GH

PU

T

Sopt

S(ʎ=ʎSAT)

Figure 26: Sopt And S(λ=λSAT ) For L And N


depicted in Fig. 26. The optimal normalised channel throughput corresponds to

the upper surface, while the normalised channel throughput under saturated con-

ditions (λ = λSAT ) corresponds to the lower surface. Observe that Sopt presents a

better performance for the different values of L and N . Therefore, in the follow-

ing, we consider different network designs to achieve optimal normalised channel

throughput.

4.6.1 Design 1

In this scenario, for a given L and N , we aim to find the optimal packet arrival

rate (λ) so that channel is maximally utilized. In practice the packet arrival rate

is in close relation to how frequent a node will sample the environment. In this

section, we assume that the network is unsaturated and discuss the saturated case

in a latter design.

Denote λopt as the optimal packet arrival rate for an unsaturated network of N

nodes and packets of length L. From (24) and (8), we can obtain λopt as

λopt =

[ √2L+ 3− 1

(N − 1)(L+ 1)

] [1− γ

1− (γ)M

], (28)

where γ is given by (26). Equation (28) gives us the optimal packet arrival rate,

however, we still need to confirm that the network operates under unsaturated

conditions with such λopt. Note that λSAT is the arrival rate at which a node

becomes saturated, and therefore λopt should satisfy

λSAT > λopt, (29)

Figure 27 shows λopt and λSAT for different sensor network configurations (M=1

and M=5 ) and packets of length L = 8. Observe that for a small number of nodes,

the condition λSAT > λopt is not fulfilled. In those cases, the optimization should

be conducted based on design 3 and using the resulting λopt as the packet arrival

rate (λ) for the new design.


0 5 10 150

100

200

300

400

500

NETWORK SIZE N (Nodes)

PA

CK

ET

AR

RIV

AL R

AT

E λ

(pkts

/secs)

λopt

M=1

λopt

M=5

λSAT

M=1,BEmin=3,BEmax=5

λSAT

M=5,BEmin=3,BEmax=5

Figure 27: λopt And λSAT For Different M

0 2 4 6 8 100

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

NODES N

PA

CK

ET

AR

RIV

AL R

AT

E λ

λopt

M=5

λSAT

M=5,BEmin=3,BEmax=5

λSAT

M=5,BEmin=1,BEmax=5

Figure 28: λopt And λSAT For Different BEmin


Notice in Fig. 27 that λopt and λSAT decrease as the number of nodes in the

network increases. It is because when the number of nodes increases the contention

in the network is expected to be higher and thus a node becomes saturated with

smaller packet arrival rate. However, an interesting case can be observed whenM=1

because higher packet arrival rate is required to saturate larger networks when the

number of attempts per packet is limited to 1.

The explanation for this is that an increase in the number of nodes results in more

contention and higher failure probability. As a consequence, the probability of dis-

carding packets without transmission (which is the same as the failure attempt

probability γ when M=1 ) is increasing, leading to higher packet arrival rate to

saturate. This can also be shown via the analytical model. Furthermore, in Figure

28 shows the impact of the backoff window parameters on the feasibility of opti-

mal unsaturated network. Observe that a small value of BEmin enable optimal

throughput on unsaturated network of fewer nodes.

4.6.2 Design 2

In this design, we aim to find the optimal network size (N) so that the normalised

channel throughput is maximized. The packet length (L) and packet arrival rate

(λ) are given in this scenario as opposed to the previous design. The network in

this design is still assumed to be unsaturated, so λ is less than λSAT . Knowing the

value (λ), optimal N can be found by solving (28) and the result must be an integer

value.

The search for such optimal N is described in Alg. 1, which is based on a bisection

method. Let Nbest be the outcome of the bisection method, for which the corre-

sponding normalised channel throughput (refer to Sbest) is very close to the optimal

value.

Depending on the particular application the range in which the bisection method

is applied could be different. For the solution of this design to be feasible, the


Algorithm 1 Algorithm for finding Nbest given λ and L

Require: λopt(NL) ≥ λ ≥ λopt(NH)while NH −NL ≤ 1 doN = 1

2(NL +NH) //Middle point for bisection

Calculate λopt(N) according to (28)if λopt(N) == λ thenbreak while loop

else if λopt(N) > λ thenNL ⇐ N //Set new low border for bisection

else if λopt(N) < λ thenNH ⇐ N //Set new high border for bisection

end ifend whileNbest = round[N ] //Solution Found

following condition is required λopt(NL) ≥ λ ≥ λopt(NH) where λopt is defined in

(28). Note it can be seen that λopt is a monotonic decreasing function with respect

to N .

Fig. 29 compares the deviation between Sbest and the optimal normalised channel

throughput for two different length of packets. Sbest and Sopt were calculated for an

extensive range λopt(NH) < λ < λSAT and using the corresponding Nbest, where we

set the minimum and maximum network size to be NL = 2 and NH = 200 nodes,

respectively. In particular, the boxes in Fig. 29 shows the median, the upper

quartile (75th percentile q3) and lower quartile (25th percentile q1) of the deviation

D = Sopt − Sbest for the range of λ. The crosses represent outliers: that is values

larger than q3+1.5(q3−q1) or smaller than q1−1.5(q3−q1). The dotted whisker lines

show the extent of non-outlier values which corresponds to over 99% of the data set.

Observe that the solution Sbest given by Alg. 1 reaches very close to the optimal

normalised channel throughput for the different L. Note that the upper outliers,

which correspond to low values of Nbest, slightly increase with L due to two reasons.

First, a round-up of N in Alg. 1 causes more collisions due to higher number

of contenders. Second, a round-down of N prevents the successful transmission

of some packets which in turn reduces the channel utilization. Nevertheless, the

deviation is still very small for the different L.


0

2

4

6

8

x 10−3

2 8

LENGTH OF PACKET L

VA

RIA

TIO

N I

N

S op

t−S

be

st

Figure 29: Comparison Between Sopt And Sbest

4.6.3 Design 3

In this design, we maximize the normalised channel throughput for a given N , λ and

L. In contrast to the previous designs, there are more constraints as both N and

λ are predefined. Knowing N and L, we obtain λSAT and λopt using (15) and (28),

respectively, for a particular set of values BEmin and M . The relation between λ

and λSAT will dictate whether the optimal network can be designed for unsaturated

or saturated conditions. The possible scenarios are listed below:

1. λ < λopt ≤ λSAT

2. λopt < λ ≤ λSAT or λ ≤ λSAT < λopt or

λSAT < λ

For the first scenario, we can have an optimal unsaturated network by adjusting

M . In particular, observe that λopt is a monotonic decreasing function in M and

thus λopt can be adjusted downward by increasing M . In other words, it is possible

to find an M such that λopt is very close to the given λ, which then results in an

optimal unsaturated network.


Table 2: Optimal Designs

Design Input Parameters Output Parameters1 L,N λ2 L,λ N

3 L,N ,λ b

For the second scenario, although λ can be less than λSAT , an optimal unsaturated

network is not feasible. It is because the network is already saturated (λSAT < λopt),

which is the case mentioned in design 1, or is not possible to find M so that the

throughput is maximized under unsaturated conditions when λ > λopt. In this

scenario, we propose to design a network that saturates at λopt by adjusting M .

Then, the optimal throughput is restrained for any given λ.

In order to find M , the saturated sensing rate ϕ given by (10) should be equal to

ϕopt in (24). As a result we have that

M−1∑i=0

biγi =

[1− γM

1− γ

] [ϕ−1opt + (L+ 2)(1− γ)− δγ

]. (30)

The maximum number of attempts (M) is then a solution of the equality in (30).

However, (30) cannot be easily solved and even if the solution exists it is not nec-

essary an integer value. The same objective can be met in this case if we vary the

initial backoff window instead of adjusting M . To this end, we propose to keep the

same backoff window in every attempt, which implies minimum change to the IEEE

802.15.4 protocol. Then equation (30) reduces to

b = ⌈ϕ−1opt + (L+ 2)(1− γ)− δγ⌉. (31)

Note that b guarantees optimal normalised channel throughput regardless of M .

As summary, see table 2 with the different input and output parameters for each

design.


010

2030

0.010.03

0.050

0.2

0.4

0.6

0.8

1

NETWORK SIZE N (nodes)PACKET ARRIVAL RATE λopt (pkts/slot)

CH

AN

NE

L TH

RO

UG

HP

UT

Sop

tAna L=2,M=5Ana L=8,M=5Sim L=2,M=5Sim L=8,M=5

Figure 30: Sopt For Different L And N

4.7 Evaluation Of Optimal Networks

The three designs of optimal WSNs were verified using the ns-2 simulator. For

the first design, we calculate λopt for different N using (28). The analytical values

(N, λopt) were used as inputs for the simulation. A comparison between analytical

and simulation optimal normalised channel throughput for networks of two different

L are presented in Fig. 30. The results prove the validity and accuracy of the

performance optimization.

For the second design, we evaluate the validity of the algorithm proposed in sec.

4.6.2. Figure 31 shows Nbest obtained for a range of packet arrival rates (λ) and

a default network (M=5), where we set the minimum and maximum network size

to be NL = 2 and NH = 200 nodes, respectively. We use the calculated analytical

pairs (Nbest, λ) as inputs for the simulation. A comparison between analytical and

simulation results are presented in Fig. 32 where we can observe that normalised

channel throughput is very close to the optimal.

4.7. EVALUATION OF OPTIMAL NETWORKS 73

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.0450

10

20

30

40

50

60

70

80

90

100


NE

TWO

RK

SIZ

E N

best

(Nod

es)

Ana L=2,M=5Ana L=8,M=5Sat Point L=2,M=5,BEmin=3,BEmax=5Sat Point L=8,M=5,BEmin=3,BEmax=5

Figure 31: Nbest For Different L And λ

050

100150

200

00.01

0.020.03

0.040.05

0

0.2

0.4

0.6

0.8

1

NETWORK SIZE Nbest (Nodes)PACKET ARRIVAL RATE λ (pkts/slot)

CH

AN

NE

L TH

RO

UG

HP

UT

Sbe

st

Ana L=2,M=5Ana L=8,M=5Sim L=2,M=5,BEmin=3,BEmax=5Sim L=8,M=5,BEmin=3,BEmax=5

Figure 32: Sbest For Different L And λ


0 2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

NUMBER OF ATTEMPTS M

CH

AN

NE

L TH

RO

UG

HP

UT

Sop

t

Ana L=2,N=20,λ=0.02Ana L=8,N=20,λ=0.02Sim L=2,N=20,λ=0.02Sim L=8,N=20,λ=0.02

Figure 33: Sopt For An Optimal Saturated Network

Notice that the small deviations in Fig. 30 and Fig. 32 correspond to small net-

work sizes (less than 10 nodes), and is due to the assumption made in subsec.

4.3.1 that claims γ to be constant and independent in every attempt. This mod-

elling hypothesis is not precise for unsaturated networks of few nodes [35] because

simulation-based studies have showed that the sensing failure probability tends to

be lower in the first attempt, especially as the length of packet increases. The sens-

ing failure probability in the first attempt is lower because of the few number of

contending nodes and, in addition, larger packet transmissions occupy the channel

for longer time increasing the sensing failure in subsequent attempts. However, the

small deviation between analytical and simulation demonstrates that is still a good

approximation.

For the third design, we consider a network of N = 20 nodes and packet arrival

rate λ = 0.02 (λ > λopt for all M). We can estimate the optimal backoff duration

b for L = 2 and L = 8 using (31), the result are b = 35 and b = 53 backoff slots,

respectively. The analytical and simulation results are presented in Fig. 33 for

different number of attempts (M) where we can observe that the saturated network

is optimal with any M . Figure 34 compares the network lifetime of optimal designs

4.7. EVALUATION OF OPTIMAL NETWORKS 75

24

68

1012

10

20

30

40

500

100

200

300

400

NETWORK SIZE (Nodes)

NETW

OR

KLIF

ETIM

E(d

ays)

Ana Sopt

Ana S(ʎ=ʎSAT)

LENGTH OF PACKET L (slots)

Figure 34: Tnet For Optimal Designs 1 and 2, And Saturated Networks

that use the standard IEEE 802.15.4 CSMA-CA backoff scheme (design 1 and 2)

with the network lifetime of settings under saturated conditions for different N

and L, and where we assume that Ebat is 28 Kjoules (2 AA batteries, each of

2.5Ah). The upper surface depicts the behaviour of the optimal design 1 and

2, while the lower surface corresponds to a networks under saturated conditions

(λ = λSAT ) and whose MAC layer parameters are macMinBE=3, macMaxBE=5,

and macMaxCSMABackoffs=4. Recall from Fig. 29 that the approximation made

for design 2 has a negligible deviation Sopt−Sbest and the same is still valid here for

network lifetime. Observe that the proposed designs always achieve larger lifetime

in days because the relation λopt < λSAT holds for all cases, and therefore the

sensor nodes corresponding to the optimal designs are less busy. Additionally, the

expectancy is raised when the network size increases since the traffic load is now

shared amongst a higher number of nodes.

Similarly, figure 35 compares the network lifetime of optimal design 3 with the

network lifetime of settings under saturated conditions for different N and L, and

where we assume again that Ebat is 28 Kjoules. In this case, the network lifetime

of design 3 is higher because the optimal backoff window b, obtained from (31), is


24

68

1012

10

20

30

40

500

50

100

150

200

250

300


NETW

OR

K L

IFETIM

E (days)

Ana S

Ana S(ʎ=ʎSAT)

opt

LENGTH OF PACKET L (slots)

Figure 35: Tnet For Optimal Design 3, And Saturated Networks

usually larger than the different backoff windows bi of the IEEE 802.15.4 CSMA-

CA standard, and hence the sensor nodes spend longer time in idle state, a state

of lower power consumption. In summary, the results for the normalised channel

throughput of the optimal designs represent a notable improvement compared to

the default IEEE 802.15.4, and also show that the optimal designs guarantee longer

network lifetime, especially when a big number of nodes is used to cover the sensing

area.

4.8 Conclusion

In this chapter, we developed a simple analytical model for the IEEE 802.15.4

MAC protocol under unsaturated conditions. We also investigated the transition

from unsaturation to saturation in these networks. The closed-form expressions for

normalised channel throughput in a single-hop infrastructure network are derived

under both unsaturated and saturated conditions. We then proposed several designs

to optimize the normalised channel throughput given the packet arrival rate at the

4.8. CONCLUSION 77

node, the number of nodes in the network or both. We found that optimal networks

are feasible for both unsaturated and saturated conditions and discussed the impact

of various MAC parameters in each of the designs.

The obtained results were validated against simulations and showed that the nor-

malised channel throughput can be optimized with an arbitrary packet arrival rate

and/or number of nodes in the network. Moreover, we showed that the network

lifetime of the optimal designs is considerably higher than the lifetime of network

settings that use the default MAC parameters of IEEE 802.15.4. Finally, we can

mention that these optimal designs could potentially be used for data-intensive ap-

plications such as seismic monitoring where an event could cause the rapid need to

transmit critical data, image or audio, to the master sensor node. Other potential

data-intensive applications are presented in chapter 1.

Chapter 5

Enhanced CSMA-CA For

Homogeneous Conditions

In the previous chapter we found that the carrier sense multiple access with col-

lision avoidance algorithm that was designed for IEEE 802.15.4 networks can be

optimised by choosing non-standard parameters. The optimization make possible

data-intensive wireless sensor network with homogeneous saturated traffic condi-

tions. To do this, the network designer requires complete knowledge of the packet

length L, arrival rate λ, and the number of sensor nodes N in order to create

a network that operates at the best performance point. However, this complete

knowledge is not always available during the design phase, and in particular the

number of active nodes when an unexpected sensing event occurs.

With this consideration in mind, we address here the following question: whether or

not it is possible to achieve a permanent optimal performance in terms of normalised

channel throughput with an enhanced CSMA-CA algorithm when the number of

active nodes are not known a priori, for instance, imagine a building monitoring

application where a maximum number of nodes NTotal are deployed, but only NActive

become active in an event, where NActive < NTotal.

To answer the above question, we firstly conduct an experimental analysis of the

79

80CHAPTER 5. ENHANCED CSMA-CA FOR HOMOGENEOUS CONDITIONS

IEEE 802.15.4 CSMA-CA algorithm for wireless sensor networks operating in a

single-hop star topology mode to corroborate the accuracy of the model and opti-

mal network designs proposed in previous chapter. In particular, we measure the

channel utilization and communication reliability of the network given by the met-

rics: normalised channel throughput and packet delivery ratio. To do this, several

experimental trials are set up using a testbed of sensor nodes in an indoor envi-

ronment. Then, we analytically demonstrate that the CSMA-CA algorithm can be

enhanced for homogeneous data-intensive scenarios. We show that packet delivery

ratio and throughput could be improved if the sensor nodes use an uniform con-

tention window in every backoff attempt. The final validation is presented with a

testbed that uses the enhanced CSMA-CA scheme and compare the results with

standard IEEE 802.15.4 experimental trials.

The rest of the chapter is organized as follows: in Section 5.1, we present the

related work. In section 5.2, we explained the developed hardware and software,

and the measurement setup for the experimental trials. Section 5.3 present the

results for the saturated and unsaturated by comparing experimental values and

analytical predictions. In section 5.4, we formally introduce a new priority medium

access protocol for homogeneous data-intensive WSNs that is based on an uniform

CSMA-CA scheme (PRIMAC-Uniform). Finally, section 5.5 concludes the chapter

with some remarks.

5.1 Related Work

In the literature, we can find several analytical studies of the IEEE 802.15.4 CSMA-

CA algorithm. In general, all of these models require a number of simplifications and

conventional assumptions, especially regarding the transmission over the wireless

channel. These assumptions could generally be considered of hard nature for real

implementations, i.e. an error-free wireless medium, full range of communication

between all nodes, absence of the hidden and exposed node problems. Among those

5.1. RELATED WORK 81

models, we could include our own study in [64] where we find the channel utilization

for single-hop star WSNs under saturated and unsaturated conditions. In the model,

the saturated condition refers to a situation where each node has always backlogged

packets awaiting for transmission, whilst in unsaturated condition, packets arrive

at each node according to a Poisson process such that a node has a period of time

when its queue is empty. For the experimental trials, we consider again saturated

and unsaturated conditions.

Despite of the extensive industry acceptance of the IEEE 802.15.4 standard, there

exist a limited number of experimental trials in the literature searching for its max-

imum performance limits. Lee made a preliminary evaluation of the IEEE 802.15.4

MAC protocol in [50] by measuring the throughput, packet delivery ratio and re-

ceived signal strength indication (RSSI) in a single-hop star topology network. The

findings showed that the highest achievable normalised channel throughput is 62%

when there is only one active node and also that delivery ratio degrades rapidly by

increasing the network size. These experiments consisted of a maximum number of

4 nodes and the analysis did not include enhancement recommendations. Ferrari

et al. [25] compared the empirical performance of IEEE 802.15.4 MAC protocol

with a proprietary Z-Wave protocol for WSNs in indoor scenarios. The results were

obtained in terms of throughput, RSSI and delay. They found that the experimen-

tal values are in good agreement with simulations in Opnet [68]. Moreover, the

maximum achievable throughput in a single-hop link was only 32 Kbps out of 250

Kbps in the presence of maximum offered load, this is, saturated conditions.

Hengstler [34] deployed a wireless image sensor network for distributed surveillance

using a compliant IEEE 802.15.4 radio module, the Texas Instrument CC420 [92].

Initially, the testbed could achieve up to a maximum of 89.6 Kbps in indoor single-

hop star scenarios. Due to these low rates, the authors proposed enhancements for

the MAC protocol such as the use of reduced number of acknowledgement packets.

However, they do not consider the optimization of the CSMA-CA algorithm by


choosing different parameter settings that could reduce latency for the multime-

dia packets whilst keeping the same level of delivery ratio and normalised channel

throughput.

Anastasi et al. [6] introduced the concept of MAC unreliability to describe a prob-

lem that arises in IEEE 802.15.4 WSNs with periodic traffic and active power man-

agement that causes very low packet delivery ratio. They proposed to increase the

reliability, by setting the maximum recommended values given by the specification

[93] for every CSMA-CA parameter, at the expense of high latency and thus low

throughput. In contrast to Anastasi et al., the effect of active power management

is not considered in our experiments, however, we could still show that better reli-

ability could be achieved for WSNs under saturated conditions.

5.2 Performance Experiments

In this section, we describe the experimental trials that we carried out to evalu-

ate the performance of the IEEE 802.15.4 CSMA-CA algorithm in beacon-enabled

WSNs. In particular, we aim at measuring normalised channel throughput and

packet delivery ratio for networks with a number of 8 to 14 nodes under saturated

and unsaturated conditions. Beacon-enabled mode is adopted instead of nonbeacon-

enabled mode because the first could provide better performance for the metrics

of interest. This is due to the reduced probability of collision in a slotted protocol

where nodes transmit in a synchronize fashion.[54].

5.2.1 System Design

Our wireless sensor nodes are formed with an ATMEL ATZB-900-BO ZigBitR 900

MHz Wireless Module. The ATZB module is composed by an ATmega1281V mi-

crocontroller and an AT86RF212 RF transceiver, which complies with physical and

5.2. PERFORMANCE EXPERIMENTS 83

AVR Dragon

Programmer

Power Supply

PC Serial

Interface

Node

Figure 36: Wireless Sensor Node Hardware

medium access control specifications of the IEEE 802.15.4 standard, and addition-

ally offers the capability for developing customized MAC protocols. We designed a

board for mounting the ATZB module as shown in Fig. 36. This board provides

a port for programming, a BNC port for connecting a monopole antenna and 20

pins for powering and accessing the I/O of the microcontroller. In the following we

will refer to the board-mounted ATZB module as the node. The most important

wireless features of our nodes include adjustable output transmission power up to

10 dBm, high receive sensitivity of -110 dBm, over 6 km of outdoor line-of-sight

range and operation in the 868/915 MHz band.

For the software framework, we used the IEEE 802.15.4 MAC C library offered by

ATMEL in [8] in order to implement the IEEE 802.15.4 CSMA-CA algorithm and

the application layer routines needed for controlling the nodes. The library provides

a basic set of resources that enables the re-programming of all protocols atop the

physical layer allowing the design of tailored applications and optimization of the

algorithms. The compiled code was downloaded onto the nodes through a AVR

Dragon programmer [7], as seen in Fig. 36.


PHY Protocol Data Unit (PPDU)

Preamble Sequence SFDFrame

LengthPHY Payload

5 octets

Synchronization Header (SHR)

1 octet

(PHR)

max. 127 octets PHY Payload

PHY Service Data Unit (PSDU) = MAC Protocol Data Unit (MPDU)

Frame

Control

2 octets

(FCF)

Sequence

Number

1 octet

Destination

PAN ID

2 octets

Destination

Address

2 octets

Source

Address

2 octets

MAC Payload

max. 116 octets

FCS

2 octets

CRC-16

Figure 37: PHY And MAC Packet Structure

Using the IEEE 802.15.4 standard as a basis, we developed two different types of

devices: a Personal Area Network (PAN) coordinator and a node. The features

available in the PAN coordinator includes creation of a network, association of sen-

sor nodes, beacon broadcast, and it could also adjust the CSMA-CA parameters

of nodes, provide clock synchronization and relay the received data to a gateway,

in this case, a computer connected via serial port. To do so, we developed a serial

interface board around a MAX3232 transceiver [58], see in Fig 36. The nodes are

capable of joining a network administrated by a PAN coordinator, synchronizing

their clocks and setting new CSMA-CA parameters upon the reception of a bea-

con, and operating according to the IEEE CSMA-CA algorithm for beacon-enabled

WSNs in order to transmit data packets to the coordinator.

The structure of the physical layer packet consists of a synchronization header

(SHR), a PHY header (PHR) and PHY service data unit (PSDU), as seen in Fig.

37. The PHR field denotes the length of the PSDU, which is between 1 and 127

octets. The PSDU contains the MAC protocol Data unit (MPDU), which in turns

consists of several MAC header fields, a payload and a frame Check Sequence (FCS).

Since we adopt short addressing, the MAC payload length could go from 1 to 116

octets. According to IEEE 802.15.4 standard, the physical layer is based on the

Direct Sequence Spread Spectrum (DSSS) spreading technique and provides a total

of 27 operational frequencies: 16 channels in 2.4 GHz, 10 channels 915 MHz and

5.2. PERFORMANCE EXPERIMENTS 85

Nodes

Coordinator

Figure 38: Network Setup Deployment

1 channel 868 MHz. The modulation and nominal data rate of each band are 250

Kbps using Orthogonal Quadrature Phase Shift Keying (OQPSK) at 2.4GHz, 40

Kbps using Binary Phase Shift Keying (BPSK) at 915 MHz, and 20 Kbps using

also BPSK at 868MHz. The range of communication was original intended for

tens of meters which corresponds to a limited personal operating space, however,

commercial 802.15.4 radio modules reach up to few kilometers [9].

5.2.2 Measurement Setup

We arranged a single-hop network with star topology consisting of N nodes and

one coordinator in an indoor environment. The nodes are placed at 1 meter from

the coordinator, and within 0.2 to 1.8 meters from each other, see Fig. 38. Due

to the close proximity between the nodes, the output transmission power is set to

minimum, this is -11 dBm. The measurement setup guarantees that all nodes are

within range of communication, and therefore, it allows us to evaluate the maximum

performance of the MAC protocol. The wireless communication is established on

the 915 MHz band using a Binary Phase-Shift Keying (BPSK) modulation scheme of

maximum 40 Kbps data transmission rate and a backoff slot duration of 2 (ms/slot).

This backoff duration value was chosen in order to accommodate ten octets per


backoff slot as defined by the IEEE 802.15.4 MAC specification for the higher 2.4

GHz band. The superframe order and beacon order was set to 6, disabling the duty

cycle mechanism, but at the same time, allowing frequent time synchronization of

nodes. Acknowledgement packets are not requested to avoid over congestion and

higher transmission delays.

We performed two set of experiments with IEEE 802.15.4 in our testbed. In the first

experiment, i.e. saturation trial, each node attempts the continuous transmission of

1000 packets of a fixed length L upon the reception of a beacon from the coordinator,

where L denotes a physical packet length, this is, PHY PPDU. The nodes use the

default values of the IEEE 802.15.4 CSMA-CA parameters, as defined in [93], and

the experiment is repeated for several packet sizes. In the second experiment, refer

to as unsaturated trial, the packets are periodically generated in each node with a

constant time interval of rate λ (pkts/s). An important objective of this trial is to

contrast the performance of a network under periodic traffic with the corresponding

analytical predictions of our study [64] that models Poisson traffic in an equivalent

single-hop star scenario. Similarly to previous trial, each node attempts to transmit

1000 packets of fixed length L whilst using the default values of IEEE 802.15.4

CSMA-CA parameters.

5.3 Experimental Results

In this section, we present the results of the two experimental trials. In particular,

we will measure the normalised channel throughput S and the packet delivery ratio

Ps. Note that normalised channel throughput S at the MAC layer is the portion

of time that the channel has been used for successful transmissions and can be

measured as

S =ApktsL

Tend − Tstart

(32)

whereApkts denotes the number of received packets in the coordinator, and Tstart and

Tend are the starting and ending time of the trial, respectively. The packet delivery

5.3. EXPERIMENTAL RESULTS 87

0 2 4 6 8 10 12 140.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

CH

AN

NE

L TH

RO

UG

HP

UT

S

LENGTH OF PACKET L (SLOTS)

Ana N=10Exp N=10

Figure 39: S For Saturated Trial

ratio, referred as Ps, is also a metric of interest that represents the probability that

the coordinator receives a packet successfully, and is given by

Ps =Apkts

Gpkts

, (33)

where Gpkts accounts for the total number of packets generated by the nodes.

5.3.1 Saturated Trial

In this trial we create a network size of 10 nodes that are configured to use the

default IEEE 802.15.4 parameters: macMinBE=3, macMaxBE=5 and M=5. The

performance metrics are measured for packet lengths in the range of 2 to 12 slots.

The average and maximum absolute deviation values of S and Ps are calculated over

10 independent replicas. Then, a comparison between the experimental normalised

channel throughput and packet delivery ratio, and the corresponding analytical

metrics is shown in Fig. 39 and Fig. 40, respectively. The analytical values,

denoted as Ana in the figures, are obtained from the fixed-point solution established

by equations (17), (16), (14) and (10) proposed in [64] and presented in the previous

chapter.


0 2 4 6 8 10 12 140.1

0.2

0.3

0.4

0.5

0.6P

AC

KE

T D

ELI

VE

RY

RA

TIO

P s

LENGTH OF PACKET L (SLOTS)

Ana N=10Exp N=10

Figure 40: Ps For Saturated Trial

Fig. 39 shows that the experimental normalised channel throughput of an indoor

network matches very closely to the theoretical expected values. Therefore, we

can confirm that the adopted modeling assumptions that consider an error-free

wireless channel and neglect the negative effects of hidden terminal and exposed

terminal for indoor networks with nodes in close proximity are suitable. Fig. 40

also corroborates that the experimental packet delivery ratio follows the theoretical

expectation with a negligible deviation of only 0.04. Finally, notice that the default

parameter values of the IEEE 802.15.4 specification limits the utilization of the radio

capacity, specifically, up to 57 percent in this network setting. As a consequence,

it is not recommendable to design a network that operates with default parameters

due to poor channel utilization and packet delivery ratio.

5.3.2 Unsaturated Trial

This trial considers a network size of 10 nodes under unsaturated conditions with

packets arriving periodically, the test range is from 0.5 to 13 (pkts/s), and we set

macMinBE=3, macMaxBE=5, M=5, and L = 12 slots, or equivalently LLoad =

5.3. EXPERIMENTAL RESULTS 89

0 2 4 6 8 10 12 140

0.1

0.2

0.3

0.4

0.5

0.6

0.7

CH

AN

NE

L T

HR

OU

GH

PU

T S

PACKET ARRIVAL RATE λ (pkts/s)

Ana N=10,L=12

Exp N=10,L=12

Sopt

Figure 41: S For Unsaturated Trial

824bits. For each packet arrival rate, we performed 10 independent replicas over

which we calculate the average value and the maximum absolute deviation of the

metric. The experimental S and Ps ratio are compared with the analytical counter-

parts extracted from equations (17), (16), (14) and (8), however, the latter metrics

correspond to networks where packets arrive at each nodes according to a Poisson

process of rate λ rather than periodic traffic as implemented in these experiments.

Nevertheless, Fig. 41 shows a very accurate match between Poisson analytical pre-

dictions and experimental results for indoor WSNs with nodes in close proximity,

confirming that the modelling of Poisson traffic approximates to the realistic peri-

odic traffic in this experiment.

In addition, we can observe in Fig. 41 that the optimal normalised channel through-

put, denoted as Sopt, is reached at an given arrival rate (5.25 pkts/s when N=10)

and then decreases as the arrival rate increases. This situation occurs because the

traffic demand surpasses the radio capacity. Given that the maximum data trans-

mission rate is 40 Kbps. In a network of 10 nodes, we have that the maximum

data rate possible for each node is 40Kbps10nodes

= 4Kbps when equally distributed. Since

each packet has a MAC load of 824 bits and an overhead of 136 bits, then it is not


0 2 4 6 8 10 12 140

0.2

0.4

0.6

0.8

1D

ATA

DE

LIV

ER

Y R

ATI

O P s


Exp macMinBE=3,macMaxBE=5,M=5Exp macMinBE=3,macMaxBE=3,M=5Exp macMinBE=8,macMaxBE=8,M=5

Figure 42: Ps For Various Parameters In N=10,L=12

possible to accommodate packet arrival rates that exceed more than 4000bps960bits

= 4.16

pkts without deteriorating the normalised channel throughput and without buffer-

ing. For the IEEE 802.15.4 CSMA algorithm, the maximum normalised channel

throughput is reached near 5.25 pkts/s instead of 4.16 pkts because many packets

get discarded after M attempts.

5.4 PRIMAC-Uniform: Uniform CSMA-CA Scheme

In this section, we consider the performance of two different CSMA-CA schemes un-

der saturated conditions. The first one uses the binary exponential backoff scheme

of the IEEE 802.15.4 standard with the default parameter values, in which the con-

tention window is increased by a factor of 2 after every failed transmission attempt.

For the second type of setting, we propose to employ an uniform backoff access

method, in this case, CW is a constant and fixed value for all the M transmission

attempts, and thus the average backoff period becomes b=CW/2 in each attempt.

This proposal is a medium access protocol for homogeneous data-intensive that we

will refer in the following as priority MAC uniform (PRIMAC-Uniform).

5.4. PRIMAC-UNIFORM: UNIFORM CSMA-CA SCHEME 91

0 2 4 6 8 10 12 140

0.1

0.2

0.3

0.4

0.5

0.6

0.7

CH

AN

NE

L TH

RO

UG

HP

UT

S


Exp macMinBE=3,macMaxBE=5,M=5Exp macMinBE=3,macMaxBE=3,M=5Exp macMinBE=8,macMaxBE=8,M=5

Figure 43: S For Various Parameters In N=10,L=12

Traditionally, it is well-known that long contention windows in CSMA-based MAC

protocols improves the reliability of the network in terms of Ps, as seen in Fig. 42

for the setting (macMinBE=macMaxBE=8). However, this improvement comes at

the cost of lower channel utilization, as seen in Fig. 43. Now, remember from the

unsaturated trial in subsection 5.3.2 that Sopt is reached near a λ=5 (pkts/s) when

N=10, and after that point, S and Ps decrease. Since, this point determines the

maximum transmission capacity, we propose to find a general uniform backoff period

b such that network saturates at around 5 (pkts/s). By doing this, we guarantee

the best achievable performance of the CSMA-CA mechanism for a network that

inevitably operates under saturated conditions.

We found an exact solution for b in equation (30) of subsection 4.6.3. If we replace

equ. (24) into equ. (30), the following expression is obtained

b = ⌈(N − 1)(L+ 1)√2L+ 3− 1

+ (L+ 2)(1− γ)− δγ⌉, (34)

where the sensing failure γ and average number of CCA per attempt δ are given by

γ ≈(√2L+ 3− 1)

[1− (N−2)(

√2L+3−1)

2(N−1)(L+1)

](√2L+ 3− 1)

[1− (N−2)(

√2L+3−1)

2(N−1)(L+1)

]+ 1

, (35)


δ = 1 + (1− γ)

2− (1−

√2L+ 3− 1

(N − 1)(L+ 1)

)N−1 . (36)

Although, equation (34) can guarantee the optimal normalised channel throughput

Sopt, its implementation potential is limited since it requires full knowledge of N

and L. Therefore, we propose in this chapter to use a suboptimal approach for

PRIMAC-Uniform, which consists in choosing a fixed backoff period, bN , for a

range of network sizes [NL, NH ], as described in Alg. 2.

Algorithm 2 Find bN given L and range [NL, NH ]

Require: NH > NL

Require: SubOptbest = 0for N = NL;N ≤ NH ;N ++ doCalculate b according to (34)Calculate saturated throughput for NL and NH using CSMA-CA of fixedCW=2b and M=5. Denote results as Suni(NL) and Suni(NH)

Calculate SubOptite =Suni(NL)Sopt(NL)

+ Suni(NH)Sopt(NH)

if SubOptite > SubOptbest thenbN = bSubOptbest = SubOptite

end ifend forCalculate saturated throughput for NL and NH using CSMA-CA with IEEE802.15.4 default parameters. Denote results as Sieee(NL) and Sieee(NH)

Ensure: Suni(NL)Sopt(NL)

> Sieee(NL)Sopt(NL)

and Suni(NH)Sopt(NH)

> Sieee(NH)Sopt(NH)

Note in Fig. 44 that the use of uniform backoff periods (bN=8,bN=10 and bN=12)

significantly improve the relation S/Sopt for saturated networks. To evaluate this,

we conducted experiments with several networks sizes, L=12, and bN=10, which was

obtained from Alg. 2. Fig.45 and Fig. 46 show a comparison between the proposed

uniform scheme, IEEE 802.15.4 specification with default parameters and the opti-

mal point of operation of the CSMA-CA mechanism. Notice that PRIMAC-Uniform

outperforms the standard in terms of S and Ps. In contrast to the optimal designs

in previous chapter, the adaptive scheme approaches to the optimal behaviour of

CSMA-CA though N is unknown.

5.4. PRIMAC-UNIFORM: UNIFORM CSMA-CA SCHEME 93

6 8 10 12 14 16 18 200.5

0.6

0.7

0.8

0.9

1

NETWORK SIZE N (NODES)

S/S

opt

IEEE 802.15.4b

N=8

bN=10

bN=12

Figure 44: Analytical S/SoptRatio

NETWORK SIZE N (NODES)6 8 10 12 14

CH

AN

NE

L TH

RO

UG

HP

UT

S

0.5

0.55

0.6

0.65

0.7

0.75

Ana Sopt

Exp Ssat

PRIMAC-Uniform bN=10

Exp Ssat

IEEE 802.15.4

Figure 45: S For PRIMAC-Uniform And IEEE 802.15.4 with Default Parameters


6 7 8 9 10 11 12 13 140.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

NETWORK SIZE N (NODES)

PA

CK

ET

DE

LIV

ER

Y R

ATI

O P s

Ana Ps optExp Ps PRIMAC−Uniform bN=10Exp Ps IEEE 802.15.4

Figure 46: Ps For PRIMAC-Uniform And IEEE 802.15.4 with Default Parameters

5.5 Conclusion

The most important contribution of this chapter is PRIMAC-Uniform, which is a

medium access protocol for homogeneous data-intensive WSNs. PRIMAC-uniform

is based on an enhanced CSMA-CA scheme that manages to improve the packet

delivery ratio whilst keeping high levels of throughput for data-intensive WSNs un-

der saturated conditions by opting for a constant and fixed contention window in

the M transmission attempts of a packet. Additionally, we developed a testbed of

wireless sensor network to evaluate the performance of the CSMA-CA algorithm

that is defined for the IEEE 802.15.4 MAC protocol. We consider two type of traf-

fic conditions: saturated and unsaturated. We found an excellent correspondence

between an analytical model of the IEEE 802.15.4 CSMA-CA algorithm and exper-

imental results in an indoor scenario where nodes operate in close proximity using

a Binary Phase-Shift Keying (BPSK) modulation on the 915 MHz band. Moreover,

we showed that packet arrivals based on a Poisson process approximates to periodic

traffic in the considered WSN settings under unsaturated conditions. In particular,

5.5. CONCLUSION 95

we found that the channel utilization and communication reliability are not opti-

mal for networks with both saturated and unsaturated conditions when the default

parameters of the CSMA-CA algorithm are used.

Chapter 6

A Priority MAC Protocol for

Data-Intensive Networks

We have found in the previous chapters that data-intensive applications with

unpredictable and event-based traffic are feasible using sensor nodes of lim-

ited resources in terms of processing power and wireless data transfer rates. We have

shown that the use of an optimal IEEE 802.15.4 CSMA-CA scheme is a step closer

to achieve the next generation of wireless sensor networks that have homogeneous

data traffic conditions. However, the proposed solutions do not provide means for

prioritising the access of special sensor nodes in heterogeneous data-intensive WSNs.

With this consideration in mind, we define a new contention-based medium access

control protocol in this chapter that could achieve service differentiation between

scalar and data-intensive nodes, and guarantees near-optimal normalised channel

throughput.

The characteristics that we require in our new priority MAC (PRIMAC) protocol

are:

1. Low Delay Access. Sensor nodes require rapid access to their sink, and there-

fore an un-scheduled CSMA is the preferable access method.

2. Differentiated QoS. Different levels of quality of service for sensor nodes could

97

98CHAPTER 6. A PRIORITYMAC PROTOCOL FORDATA-INTENSIVE NETWORKS

be achieved using different access attempt profile in the nodes.

3. Node Type Selection. The priority nodes can be assigned during design or

actively selected by the network coordinator. The selected data-intensive

nodes have potentially higher resources in terms of battery and processor

capacity.

4. Maximum Resource Use. To implement an optimal medium access strategy

is still fundamental since the radio transmission capacity is still limited.

The rest of the chapter is organized as follows: section 6.1 presents the motiva-

tion of our work including a review of existing QoS-aware MAC protocols. Section

6.2 introduces the PRIMAC protocol. In section 6.3, we describe the differenti-

ated access strategy of scalar and data-intensive nodes. In subsection 6.3.3, we

evaluate the performance of the PRIMAC protocol under saturated conditions in

terms of throughput, packet delivery ratio and delay. Subsection 6.3.4 presents the

differentiated access strategy for high priority nodes. Section 6.4 compares the re-

sults obtained for PRIMAC with the widely adopted IEEE 802.15.4 MAC protocol.

Finally, Section 6.5 concludes the chapter with some remarks.

6.1 Motivation

CSMA-CA has demonstrated to be an effective mechanism to distributively share

a common wireless channel amongst uncoordinated devices. However, CSMA re-

quires mutually exclusive transmissions. The mutual transmission is achieved by

distributively coordinating the transmissions of different sensors mainly by means

of two mechanisms. First, the random access mechanism contributes to distribute

the medium access attempts of sensors to different time instances, while carrier

sense mechanisms helps to reduce the probability of collisions. Collisions are fur-

ther reduced by having time synchronization with respect to the coordinator and

the introduction of slotted communication where every node could only start the

6.2. PRIMAC PROTOCOL 99

transmission of a packet at the beginning of a slot. Moreover, the use of transmis-

sion opportunities windows could reduce even more the probability of collision, and

hence, can accommodate more packets in the channel. This makes the communica-

tion achieve higher levels of throughput and reduces the packet latency.

To obtain a better understanding why the requirements of data-intensive WSNs

could not be satisfied by the existing protocols, we evaluated the performance limits

of the IEEE 802.15.4 standard for WSNs in the previous chapters. A comparison

between the optimal IEEE 802.15.4 protocol and PRIMAC is presented in section

6.4 where we show that our new PRIMAC protocol outperforms the recommended

standard.

6.2 PRIMAC Protocol

In this section, we introduce the features of the Priority MAC protocol. PRIMAC

provides service differentiation per node for single-hop networks. Depending on the

traffic priority, a network can be classified in two type of nodes: data-intensive and

scalar nodes. Data-intensive nodes, referred to in the following as DI nodes, carry

higher priority traffic that need to be transmitted with low delay, and demands

high normalised channel throughput. The scalar nodes transmit regular packets of

smaller size that do not require rapid end-to-end delivery. The protocol is based on

a time slotted contention access mechanism with an adaptive contention window

size and non-uniform probability distribution of transmitting in each slot within

the window.

The objective of our protocol is to guarantee the delivery of data packets with

reduced access delay. To do so, we adopt a non-uniform probability distribution of

transmitting in each slot within the contention window that works independent of

the number of nodes in the network. Specifically, we find the contention window

size that satisfies minimal collision probability between the nodes of the network

and also ensures low access delay. In general, we have found that a large window


size reduces the collision probability, however at the expense of normalised channel

throughput and delay in the successful reception of packets.

A node with a pending packet picks a slot to attempt transmission, denoted as

Slot(CWw) where |Slot(CWw)| < |CW |, according to a non-uniform probability

distribution chosen to reduce the likelihood of packet collisions. Before transmission,

the node senses for any ongoing transmission in any slot previous to Slot(CWw). If

the assessment finds a busy medium, the node cancels the attempt and restarts the

transmission process by choosing a new Slot(CWw). The new contention window

begins after the end of the last interfering packet transmission plus the size of fixed

acknowledgement packet and one more slot considered for processing turn-around-

time. This re-attempt process is carried out until the packet is finally transmit-

ted. If the assessment finds a clear medium, the node starts sending the packet in

Slot(CWw). Upon completion, the node waits up to one slot for the reception of an

acknowledgement from the receiver. If the acknowledgement packet is not received,

the nodes could re initiate the transmission process for the same packet up to a

maximum of three attempts (max retransmission = 3).

In the following subsection, we revisit a collision minimizing method proposed in

[90]. Using the solution found by the authors, we evaluate the impact of the con-

tention window size on the normalised channel throughput and packet access delay

of a single-hop network with all nodes in range of communication. Based on Tay’s

method [90], we propose a network-independent probability distribution that opti-

mize our two metrics of interest while keeping a minimum level of collision prob-

ability. Our proposal is different from the suboptimal minimizing proposal of Tay

because we suggest a generic distribution that could slightly outperform the mini-

mizing method when it is applied to sensor network with data-intensive data. Then,

we propose the use of a steeper attempt probability distribution for high priority

nodes. We will demonstrate that this modification provides low access delay for DI

nodes with no impact on the collision probability, and thus network packet delivery

ratio.

6.2. PRIMAC PROTOCOL 101

Algorithm 3 PRIMAC Functional Description

Require: Att=1 //Set Initial Packet Attempt Counter1: Sense channel idleness during current Slot(c)2: if Slot(c) = Busy then3: wait(Busy) and increment(Slot(c)) accordingly

// Wait until channel becomes idle4: wait(ACK+TurnT ime) and increment(Slot(c)) accordingly

// Wait for acknowledgement Packet5: end if6: Slot(CW1)=Slot(c) + 1 //Set next slot as first slot of CW7: Calculate Slot(CWw) according to [90]8: Sense channel idleness during period:

cc=[Slot(CW1), Slot(CWw − 1)]// cc denotes clear channel

9: wait(cc)10: if cc = Busy then11: retry(Return to step 1)12: end if13: Start Transmitting Packet in Slot(CWw)14: wait(ACK+TurnT ime) for ACK15: if ACK = Received then16: end(Packet Successfully Transmitted)17: else if Att ≤ max retransmission then18: Att=Att+ 1 //Increment attempt counter19: retry(Return to step 1)20: else if Attempt > max retransmission then21: end(Packet Dropped)22: end if


6.3 Differentiated and Non-Uniform Medium Ac-

cess Strategy

6.3.1 Collision Minimizing

The active nodes of a network contend to transmit their packets within a window of

CW slots. The two possible outcomes of this attempt are: a successful transmission

or a collision between two or more nodes. In order to reduce collisions, the prob-

ability of attempting a packet transmission in each slot is progressively calculated

based on the current estimation of the number of active nodes. The mechanism

assigns a low probability for the first slot since it believes that the number of active

nodes could be large, and then the probability is increased in subsequent slots since

such belief is adjusted to a more accurate network size. A transmission attempt

is guaranteed in the last slot of CW if no other node previously attempt to trans-

mit. The authors of [90, 107] found a minimizing condition for this mechanism that

requires knowledge about the exact number of active nodes, a fixed CW size and

synchronization between nodes at the beginning of the contention window.

We incorporate the collision minimizing mechanism in our proposed algorithm 3.

Then, we evaluate the performance under saturated conditions, i.e. nodes have

always packets awaiting to be transmitted, in terms of normalised channel through-

put, packet delivery ratio and inter-successful packet transmission. The results are

presented in Fig. 47, Fig. 48 and Fig. 49, respectively. Notice from Fig. 48 that

packet delivery is practically guaranteed to 100%. This is not only achieved by the

collision minimizing strategy, but also by using retransmissions of unacknowledged

packets as described in our protocol. Fig. 47 and Fig. 49 show a direct relation

between S and Inter−Successful−PacketT ime. Moreover, the graphs show that

the best performance is determined by the size of the contention window and is

almost independent of the number of nodes N.

6.3. DIFFERENTIATED ANDNON-UNIFORMMEDIUMACCESS STRATEGY103

3025

[ns-2 Simulation] PRIMAC Homogeneous L=12

20

CW (slots)

151050

10

0.6

0.45

0.5

0.65

0.55

20

N

S

Figure 47: Normalised Channel Throughput S Of PRIMAC - Homogeneous L=12For Different Network Sizes And Contention Windows

3025

CW (slots)


2015

1050

10

N

0.995

0.99

0.985

0.98

1

20

Ps

Figure 48: Packet Delivery Ratio Ps Of PRIMAC - Homogeneous L=12 For Differ-ent Network Sizes And Contention Windows


3025

CW (slots)


2015

1050

510

N

15

6

8

10

4

14

12

20Inte

r-S

ucce

sful

-Pac

ket T

ime

(slo

ts)

Figure 49: Inter-Successful-Packet Time Of PRIMAC - Homogeneous L=12 ForDifferent Network Sizes And Contention Windows

6.3.2 Best Contention Window for PRIMAC

In this subsection, we aim to find a relation between CW and the maximum per-

formance of our new protocol for different network and packet lengths. Consider a

homogeneous network of N nodes where the packets are of a fixed length L slots

and each node has always packets in the buffer awaiting to be transmitted. All

nodes are in range of communication, preventing the adverse effects of the hidden

and exposed terminal. The medium access mechanism is driven by Alg. 3 and uses

the collision minimizing strategy derived in [90] and revisited in [107]. Moreover,

all the nodes are time synchronized and could only attempt transmission at the

starting edge of a slot.

Denote p(t) as the probability that a node attempts to grab slot t. Then, the

probability that there is only one node that grabs the earliest slot, refer to as

Probability of no collision Pnc, was given in [90] as:

Pnc =CW−1∑i=1

NP(i)

CW∑t=i+1

P(t)

N−1

, (37)


In saturated networks, every packet transmission attempt describes a renewal cycle

Y . The cycle starts with the first slot t=0 of an attempt ends after an acknowl-

edgement packet is received or its corresponding timer is expired. Y includes an

average backoff period, denoted as AB, and the transmission of packet. Then, it

can be expressed as

Y = L+ ACK + AB, (38)

where AB is the average number of slots that passes since t=0 until any node

transmits its packet, this is

AB =CW−1∑i=1

j∏i=1

(1− P(t=i))N

. (39)

Within a cycle, the portion of time the nodes utilize for successful transmissions is

PncL. Therefore, a ratio between this portion of time and cycle Y results into the

normalised channel throughput of the network, i.e.

S =PncL

L+ ACK +∑CW−1

i=1

[∏ji=1(1− P(t=i))N

] . (40)

Giving Y , we can also find the average time spent between 2 successful packet

transmission from same or different network nodes, which we refer in the following

as Inter Successful PacketT ime, this is

Inter Successful PacketT ime =Y

Pnc

− L. (41)

To validate our model, we implemented the PRIMAC protocol on ns-2 version

2.33. Initially, we consider L = 2 and L = 12 slots since they are the minimum

and maximum packet lengths that are adopted by the IEEE 802.15.4. L = 2 and

L = 12 are equivalent to 20 and 120 bytes respectively. We also evaluated higher

packet lengths of L = 30, or 300 bytes, that we recommend to be used in DI nodes

for rapidly clearing buffered data. Fig 50 and Fig. 51 show the analytical results

obtained from the above equations for the case L=12 while Fig. 52 and Fig. 53 are

the corresponding simulation outputs. Notice that there is high accuracy between

both analytical and simulation results with small discrepancy when network size


3025

CW (slots)

20

[Analytical] PRIMAC Homogeneous L=02

1510

505

10

N

15

0.35

0.3

0.25

0.2

0.15

0.120

S

Figure 50: Analytical Normalised Channel Throughput S Of PRIMAC - Homoge-neous L=2 Slots For Different Network Sizes And Contention Windows

3025

CW (slots)

20

[Analytical] PRIMAC Homogeneous L=02

1510

505

10

N

15

0

10

15

5

20Inte

r-S

ucce

sful

-Pac

ket T

ime

(slo

ts)

Figure 51: Analytical Inter-Successful-Packet Time Of PRIMAC - HomogeneousL=02 Slots For Different Network Sizes And Contention Windows


3025


20

CW (slots)

151050

10N

0.1

0.4

0.3

0.2

20

S

Figure 52: Normalised Channel Throughput S Of PRIMAC - Homogeneous L=2Slots For Different Network Sizes And Contention Windows

3025

CW (slots)


2015

1050

510

N

15

6

4

8

14

12

10

20Inte

r-S

ucce

sful

-Pac

ket T

ime

(slo

ts)

Figure 53: Inter-Successful-Packet Time of PRIMAC - Homogeneous L=2 Slots ForDifferent Network Sizes And Contention Windows


3025

CW (slots)


2015

1050

510

N

15

0.65

0.7

0.75

0.8

20

S

Figure 54: Normalised Channel Throughput S Of PRIMAC - Homogeneous L=30Slots For Different Network Sizes And Contention Windows

3025

CW (slots)


2015

1050

510

N

15

10

8

16

14

12

20Inte

r-S

ucce

sful

-Pac

ket T

ime

(slo

ts)

Figure 55: Inter-Successful-Packet Time Of PRIMAC - Homogeneous L=30 SlotsFor Different Network Sizes And Contention Windows


1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

CW (slots)

PA

TT

Slot Attempt Probability Distribution

Optimal 3 NodesOptimal 20 NodesAvg between 3 and 20 Nodes

Figure 56: Attempt Probability Distribution For Networks Of 3 And 20 Nodes AndAverage Curve

is small. Although, we only show the figures for L=2, the analytical results also

extend for different values of L.

From Fig. 47, Fig. 52, and Fig. 54, we can observe that CW=10 slots is the best

choice for networks in the range of 2 to 20 nodes in single hop communication and

different packet lengths, L=2, 12 and 30 slots, since it maximizes the normalised

channel throughput and therefore minimizes the inter-successful packet time, as

seen in Fig. 53, Fig. 49 and Fig. 55

6.3.3 Node-Independent Attempt Distribution

We first propose to use a node-independent attempt probability distribution for all

networks where we set CW=10 slots. We suggest to use the average between the

exact PATT between 3 and 20 nodes, as seen in Fig. 56. The choice of such distri-

bution allows to reach a better normalised channel throughput for small network

sizes independently of the packet size, as seen in Fig. 57, and it is approximately


Network Size2 4 6 8 10 12 14 16 18 20

S

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8[Analytical] PRIMAC Slot Attempt Probability Distributions

Exact L=20Exact L=120Exact L=300Approx L=20Approx L=120Approx L=300

Figure 57: S For CW = 10 And Two Optimal Slot Attempt Probability Distribu-tions: Exact and Average Curve

equal to the values of the exact solution.

Suboptimal probability distributions were also proposed in [90] and [107] for the

case where the number of active nodes is unknown. However, they are not suitable

for our scenario because they were intended for a higher number of nodes, in the

range of thousand of nodes, and a contention window of 32 slots. In contrast,

we found that higher performance is achieved when CW is around 10 slots and

therefore, we propose an alternative probability distribution which addresses the

specific requirements of data-intensive applications.

6.3.4 Node-dependent Distribution for High Priorities DI

Nodes

Consider a heterogeneous network of N nodes where nodes are divided in two levels

of QoS: DI nodes with high access priority and nodes with regular access priority.

High priority implies that the node must be provided with advantaged access to


Network Size2 4 6 8 10 12 14 16 18 20

Net

wor

k A

vera

ge In

ter-

Suc

cesf

ul-P

acke

t Tim

e (s

lots

)

4

6

8

10

12

14

16

18[Analytical] PRIMAC Slot Attempt Probability Distributions

Exact L=20Exact L=120Exact L=300Approx L=20Approx L=120Approx L=300

Figure 58: Inter-Succesful-Packet Time For CW = 10 And Two Optimal SlotAttempt Probability Distributions: Exact and Average Curve

the common wireless medium, however it does not guarantee that every contention

attempt of a high priority node will be successful. The selection of the high priority

nodes is determined by the coordinator of the network which could permanently

assign the role to a specific node or rotate the privilege between the different nodes

of the network. The node selection should be published by the sink of the network

using a periodic beacon.

In a novel approach, PRIMAC introduces the use of node-dependent attempt distri-

butions combined with differentiated contention window lengths in order to obtain

the differentiated node access strategies. First, PRIMAC attains to provide rapid

access for the high DI nodes with the use of shorter contention windows. A node

with a contention window smaller that the network CW will normally attempt to

access the medium sooner than the rest of nodes, and thus its packet could poten-

tially be received in the sink prior to other traffic. Secondly, PRIMAC achieves

high packet delivery ratio in the network by using the collision minimizing strategy

derived in the section 6.3.2. In the case of the high priority nodes, PRIMAC utilizes


1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

CW (slots)

PA

TT

Slot Attempt Probability Distribution

High DI Priority NodeOther Nodes

Figure 59: Attempt Probability Distribution For Heterogeneous Network

a different probability of attempt that maintains the near-optimal performance nor-

malised channel throughput while giving a rapid medium access to the high priority

nodes.

In the previous section we found that a CW=10 slots provides the best results for

networks in the range of 3 to 20 nodes. In this section, we propose to use CW < 10

on high DI priority nodes in order to guarantee a higher probability of transmission

for these special nodes. Using ns-2 simulations, we consider several contention

windows for high priority nodes and we found that a rapid increasing attempt

distribution of CW=5, as seen in Fig. 59, achieves the following performance level

1. Maintain a high level of packet delivery ratio,

2. Maximize the average normalised channel throughput,

3. Reduce the interval between successful packet transmissions.

Refer to Fig. 60, Fig. 61 and Fig. 62 for one high priority node, and Fig. 63, Fig.

64 and Fig. 65 for three high priority nodes.

6.4. A COMPARISON: PRIMAC AND IEEE 802.15.4 113

0 5 10 15 200.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

N

S

[ns−2 Simulation] PRIMAC Heterogeneous − 1 High Priority Node

L=20L=120L=300

Figure 60: S - One High Priority DI Node

Let us consider having first one high priority DI node in a network of 20 sensors.

From Fig. 60 we can see that the normalised channel throughput stays constant at

the maximum level, however note in Fig. 61 that the inter-successful transmission

of the high priority DI node with ID=1 is much lower than the rest of the nodes

for the different considered packet lengths.

Likewise for the case with 3 high priority DI nodes (ID=1, ID=2 and ID=3) in

a network of 20 devices, Fig. 63, 65 show that the normalised channel throughput

and packet delivery ratio is still optimal while Fig. 64 demonstrates that the trans-

mission occurs sooner for the 3 high priority DI nodes. In this chapter, we omit

the graphs corresponding to 2 high priority DI nodes, however, we found a similar

behaviour.

6.4 A comparison: PRIMAC and IEEE 802.15.4

In section 6.2 we presented the performance behaviour of PRIMAC in a typical

WSN setting of up to 20 nodes. We have shown that PRIMAC reaches near-optimal


0 5 10 15 200

200

400

600

800

1000

N

Inte

r−S

ucce

sful

−Pac

ket T

ime

(slo

ts)


L=20L=120L=300

Figure 61: Inter-Successful-Packet Time Per Node - One High Priority DI Node

0 5 10 15 200.99

0.992

0.994

0.996

0.998

1

N

Ps


L=20L=120L=300

Figure 62: Ps per Node - One High Priority DI Node


0 5 10 15 200.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

N

S

[ns−2 Simulation] PRIMAC Heterogeneous − 3 High Priority Nodes

L=20L=120L=300

Figure 63: S - Three High Priority DI Node

0 5 10 15 200

200

400

600

800

1000

N

Inte

r−S

ucce

sful

−Pac

ket T

ime

(slo

ts)


L=20L=120L=300

Figure 64: Inter-Successful-Packet Time Per Node - Three High Priority DI Node


0 5 10 15 200.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

N

Ps


L=20L=120L=300

Figure 65: Ps Per Node - Three High Priority DI Node

normalised channel throughput under saturated traffic conditions for all the consid-

ered networks sizes. In contrast, we found in chapter 4 that the recommended IEEE

802.15.4 MAC protocol with default parameters cannot reach optimal operation un-

der saturated traffic conditions. Fig. 66 demonstrates that both unacknowledged

packet transmission, as studied in chapter 4, and acknowledged transmission of

IEEE 802.15.4 present a considerable performance degradation compared to the

behaviour of an optimal network, even when the biggest packet size is used (120

Bytes).

In general, we found that IEEE 802.15.4 requires the network to be under unsatu-

rated traffic conditions in order to achieve maximum normalised channel through-

put regardless of the number of nodes and packet sizes, as seen in Fig. 67. In

comparison, PRIMAC maintains near-optimal normalised channel throughput un-

der saturated conditions for all network and packet lengths, and furthermore, in

homogeneous and heterogeneous networks.

Similarly Fig. 68 shows that the maximum packet delivery ratio of IEEE 802.15.4

with default parameters can only be achieved under unsaturated conditions and

tends to be around 50%. In this case, PRIMAC drastically outperforms IEEE


2 4 6 8 10 12 14 16 18 20

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

N

Net

wor

k C

hann

el T

hrou

ghpu

t S

IEEE 802.15.4 − L=120 Bytes (12 slots)

Default ACK NetworkDefault UNACK NetworkOptimal Network

Figure 66: Normalised Channel Throughput S For Acknowledged And Unac-knowledged IEEE 802.15.4 With Default Parameters Under Saturated Conditions,And Also The Optimal Channel Throughput Achievable In Networks Using IEEE802.15.4

24

68

1012

5

10

15

200.2

0.4

0.6

0.8

1

LENGHT OF PACKET L (slots)

Optimal S for unsatureated IEEE 802.15.4 Networks


CH

AN

NE

L TH

RO

UG

HP

UT

S

Figure 67: Optimal Normalised Channel Throughput Sopt Achievable Under Un-saturated Conditions And Using IEEE 802.15.4 Default Parameters


2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

N

P

IEEE 802.15.4 � L=120 Bytes (12 slots)

Default ACK Network

Default UNACK Network

Optimal Network

S

Figure 68: Packet Delivery Ratio Ps For Acknowledged And Unacknowledged IEEE802.15.4 With Default Parameters Under Saturated Conditions, And Also The Cor-responding Optimal Packet Delivery Ratio Achievable In Networks Using IEEE802.15.4

802.15.4 since the packet delivery ratio is close to 100% in homogeneous and het-

erogeneous networks, as seen in Fig. 48 and Fig. 62 respectively.

6.5 Conclusions

This chapter presented Priority MAC, PRIMAC, which is medium access control

protocol for homogeneous and heterogeneous data-intensive networks. PRIMAC is a

contention-based MAC protocol that outperforms the recommended IEEE 802.15.4

MAC scheme in terms of normalised channel throughput and packet delivery ra-

tio. In a novel approach, PRIMAC combines a collision minimizing and node-

independent contention window strategies to orchestrate the access to the common

wireless medium. The devised PRIMAC MAC scheme achieves differentiated qual-

ity of service amongst nodes in a heterogeneous network. We believe PRIMAC

6.5. CONCLUSIONS 119

represents a contribution for the next generation of wireless sensor netwotks that re-

quire multimedia or critical high rate data transmissions while the radio transceivers

of nodes are still limited in terms of data bandwidth.

Chapter 7

Conclusions

This thesis has presented a medium access control protocol and optimization tech-

niques for wireless sensor networks that are capable of addressing the requirements

of data-intensive applications. In the following we revisited the main contributions,

assumptions and limitations of the proposed solutions.

7.1 Summary of Contributions

We have addressed in this thesis the requirements of the next generation of data-

intensive wireless sensor networks at the medium access control level. We have

derived mathematical formulations and designed techniques that could be applied

to the standard IEEE 802.15.4 MAC protocol in order to optimise its performance.

We proposed IEEE 802.15.4 network designs that are feasible for homogeneous

data-intensive WSNs, i.e. applications where data is generated in big quantities in

every single node. We proposed a novel Priority MAC protocol that is specifically

designed for data-intensive networks of homogeneous and heterogeneous nature.

The new protocol uses optimal contention strategies in order to effectively access a

shared wireless medium.

The main sections and contributions are summarised below:

121

122 CHAPTER 7. CONCLUSIONS

1. A model of the standard IEEE 802.15.4 medium access control protocol. This

model is based on a mean-field approach and captures the behaviour the IEEE

802.15.4 CSMA-CA algorithm under saturated and unsaturated traffic condi-

tions. It allows us to obtain fundamental network metrics such as normalised

channel throughput, packet delivery ratio and packet transmission delay, and

evaluate the network for a variety of packet arrival rates, network sizes and

packet lengths. Using the model we managed to derive a simple expression

for optimal throughput received at the sink in a network with star topology.

This mathematical expression provides the maximum achievable normalised

channel throughput in a WSN, and therefore it represents a considerable con-

tribution to the knowledge of IEEE 802.15.4 CSMA-CA algorithm.

2. A methodology to design Homogeneous Data-intensive WSNs. In this case, we

designed networks with optimal normalised channel throughput and demon-

strated howWSNs with data-intensive packet generation could be created. We

initially found that the carrier sense multiple access with collision avoidance

algorithm that was designed for IEEE 802.15.4 networks can be optimised

by choosing non-standard parameters. The optimization made possible data-

intensive wireless sensor networks with homogeneous saturated traffic condi-

tions. To do this, the network designer requires complete knowledge of the

packet length L, arrival rate λ, and the number of sensor nodes N in order to

create a network that operates at the best performance point. However, this

complete knowledge is not always available during the design phase, and in

particular the number of active nodes is unknown when an unexpected sensing

event occurs. Therefore, we proposed PRIMAC-Uniform which is an adaptive

CSMA-CA protocol that manages to improve the packet delivery ratio whilst

keeping high levels of throughput for data-intensive WSNs when the number

of active nodes are not known a priori, for instance, consider a building mon-

itoring application where a maximum number of nodes NTotal are deployed,

but only NActive nodes become active in an event, where NActive < NTotal.

7.1. SUMMARY OF CONTRIBUTIONS 123

3. Experimental validation of the performance of optimal networks. We devel-

oped a testbed of wireless sensor network to evaluate the performance of the

CSMA-CA algorithm that is defined for the IEEE 802.15.4 MAC protocol.

We consider two types of traffic conditions: saturated and unsaturated. We

found an excellent correspondence between our analytical model of the IEEE

802.15.4 CSMA-CA algorithm and experimental results in an indoor scenario

where nodes operate in close proximity using a Binary Phase-Shift Keying

(BPSK) modulation on the 915 MHz band. We corroborated that the chan-

nel utilization and communication reliability are not optimal for networks

with both saturated and unsaturated conditions when the default parameters

of the CSMA-CA algorithm are used.

4. Propose PRIMAC which is a Priority MAC protocol that provides service dif-

ferentiation per node for wireless sensor networks with data intensive traffic.

Traditionally, the main design considerations of the first generation of WSNs

were energy efficiency, scalability, coverage, and low-data transmission. PRI-

MAC steps up to a higher level of quality of service with the inclusion of

network performance metrics such as throughput, reliable packet delivery,

latency, and weighted medium access per node. PRIMAC is designed for ho-

mogeneous and heterogeneous networks that are composed of regular scalar

nodes of low-data content and data-intensive nodes. The protocol is based

on a time slotted contention access mechanism with adaptive contention win-

dow size and non-uniform probability distribution of transmitting in each

slot within the window. PRIMAC guarantees the delivery of data packets

with reduced access delay. In a homogeneous network, the nodes access the

shared medium in a fair mode with every device having the same opportunity

to have a successful transmission. In a heterogeneous network, the protocol

implements a weighted access strategy that allows the critical nodes to have

priority in the access of the shared medium, this is achieved without diminish-

ing key network performance metrics such as normalised channel throughput


and packet delivery ratio. To our knowledge, PRIMAC is the first proto-

col for wireless sensor networks that combines the use of non-uniform access

probability distributions with adaptive contention windows to orchestrate a

weighted access strategy in heterogeneous network conditions.

7.2 Future Work

We proposed in this thesis a priority MAC protocol for heterogeneous networks and

PRIMAC-Uniform for homogeneous data-intensive WSNs. In general, both propos-

als are constrained by common limitations. Firstly, the proposals were designed for

networks with single-hop communication and star topology since we estimated that

the state-of-the-art wireless sensor devices are stringently resource limited in terms

of wireless data transmission rate, available energy, processing power and physi-

cal size. We found that is feasible to maximize the efficient usage of resources in

the single-hop star topology scenarios. Therefore, we established that most generic

data-intensive applications will be characterized by such network configuration. As

part of our future work, we plan to consider networks that demand data-intensive

traffic and multi-hop communication. We will consider a data gathering network

topology where we find a main sink that collects the data from all devices, scalar

nodes that senses the environment and relay nodes that forwards the data from the

nodes to the sink. In particular, we intend to evaluate the performance of PRIMAC

in such scenarios and introduce the use of PRIMAC priority sensor nodes as relay

devices. The multi-hop topology creates a number of challenges at the network

layer such as route formation, traffic load balancing and wireless signal propagation

phenomenons that need to be addressed. Some of these challenges have been con-

sidered in the existence literature [3]. Specifically, the research questions that we

will address includes

1. Guarantee end-to-end quality of service in the communication between scalar

sensors and the main sink.

7.2. FUTURE WORK 125

2. Determine the best methods to achieve multiple route communication. We

would consider using multiple transmission frequencies and time schedule

transmission.

3. Find the data gathering limitations of a network that uses PRIMAC as its

primary medium access control protocol.

A second limitation of our study is to assume that the communication is half du-

plex with symmetrical wireless transmission links where every node is assumed to

be in range of communication with all others. In chapter 5 we carried out experi-

ments with real sensor devices that proved that the maximum normalised channel

throughput was achievable with optimal sensor networks when the nodes are in close

proximity. We also found the hidden and exposed terminal effects could appear if

the distance between the nodes increased. In the data gathering scenario that will

study in the future we expect to find a higher degree of adverse wireless transmission

conditions. To address these issues, we will evaluate the use of multiple channels for

the different data routes. In the literature, we could find studies on how to achieve

optimal normalised channel throughput by using different frequencies in a multiple-

hop network [39, 47, 48, 49, 81, 107] which considered CSMA-based protocols for

the MAC sublayer. In our case, we will evaluate the performance of PRIMAC which

is a CSMA-based protocol with priority access for designated nodes.

Finally, PRIMAC and PRIMAC-Uniform for homogeneous data-intensive WSNs

have been tested with sensor nodes whose radio transmission capability is still

limited at around 250kbps, and therefore we recommended to design optimal data-

intensive networks of up to 20 nodes. In the next phase, we will intend to remove

this limitation. We could achieve this objective by introducing a new generation of

wireless sensor devices that are less limited in terms of resources, and specifically

data transmission rate. Additionally, we consider that data-intensive applications

could be feasible by using multi-radio devices as it has been proposed in [5, 104] for

wireless mesh networks and multi-hop wireless networks.


PRIMAC has been the first step in the realisation of data-intensive wireless sensor

networks. The application scenarios where our medium access control protocol

could be used are numerous and we plan to extend that range to higher levels with

the introduction of multi-hop data gathering networks.

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