ipccc’111 assessing the comparative effectiveness of map construction protocols in wireless sensor...

IPCCC’11 1

Assessing the Comparative Effectiveness of Map Construction

Protocols in Wireless Sensor Networks

Abdelmajid Khelil, Hanbin Chang, Neeraj Suri

IPCCC 2011

IPCCC’11 2

Maps

Maps are an intuitive data representation technique provide a visual representation of an attribute in a certain area; street map, typographic map, world map, etc.

Maps for Wireless Sensor Networks (WSN) applications help users to understand sensed physical phenomena help users to make a decision

Sensor location Sensor value(112, 209) 145(218, 163) 163(617, 783) 158(530, 745) 163(477, 625) 165(936, 423) 157(745, 817) 155(653, 237) 168... ...

0 200 400 600 800 1000X

Y

1000

800

600

400

200

0

IPCCC’11 3

Sink

Map Construction in WSN

Naive approach for map construction

Energy-efficient approachesfor map construction

Data collection and processing

centrally at sink in-network

Energy efficiency(Comm. complexity on sensor nodes)

high comm. overheadLower comm. overhead

Map accuracynode-level accuracy, may

decrease because of comm. failures

may lose detailed information of each in

dividual node

Naive Approach Example of Available Approaches

IPCCC’11 4

Problem statement and Objectives

Several approaches have been proposed. However,

Evaluation in carefully selected application scenarios

No assessment of the comparative effectiveness of existing approaches:

Which is outperforming in Which application/scenario

for Which network configuration?

IPCCC’11 5

Outline

Motivation

Classification of Existing Map Construction

Approaches

Performance Comparison in a Wide Range

Scenarios

Conclusions

IPCCC’11 6

Data Collection Scheme

Classification of Map Construction Approaches

Map construction approaches for WSN

Region Aggregation

Data Suppression

Tree-based data

collection

eScan [9]

Isobar [8]

Iso-node based data collection

Cluster-based data collection

Isolines [14]

Iso-map [10,11]

Contour Map [18]

CME [19]


CREM [7]

Multi-path data

collection

INLR [16]

In-network Processing Technique

IPCCC’11 7

Region Aggregation Class

Basic idea Sensor nodes are ordered hierarchically (clusters, tree ..) Every sensor reports to a dedicated node (cluster head,

parent ..) Dedicated node aggregates adjacent similar data to regions

3 Phases:Region Segmentation At each sensor Non-overlapping polygons Vertex representation

Data Collection Aggregator determination

Region Aggregation At aggregator Regions formation Aggregation function, e.g. average

m m+1 m+2

Tree-based Cluster-based Ring-based

36 37

37 3837

IPCCC’11 8

Basic idea A subset of sensor nodes (iso-

nodes) report their value to the sink suppress similar data to be reported

2 PhasesIso-node Identification what is an iso-node?

• has a neighbor with different value how to identify?

• broadcast • snoop

Isoline Report Generation iso-node based

• generated at Iso-node• routed directly to the sink

cluster based• generated at cluster-head• Iso-node reports to cluster-head• a local map

Data Suppression Class

38 42 43

36 41 42

37 41 45

Isoline

Nodes report to the sink

Nodes suppress reports to the sink

IPCCC’11 9

Data Collection Scheme

Classification of Map Construction Approaches

Map construction approaches for WSN

Region Aggregation

Data Suppression

Tree-based data

collection

eScan [9]

Isobar [8]

Iso-node based data collection


Isolines [14]

Iso-map [10,11]

Contour Map [18]

CME [19]


CREM [7]

Multi-path data

collection

INLR [16]

In-network Processing Technique

IPCCC’11 10

Selected Map Construction Algorithms

The eScan approach [9] Nodes ordered as an aggregation-tree Polygon regions Aggregation function: Average

The Isoline approach [14] Local flood to label border nodes Each iso-node reports to the sink Map constructed at the sink

[9] Y. Zhao et al. Residual Energy Scan for Monitoring Sensor Networks. In IEEE WCNC, 2002.[14] I. Solis and K. Obraczka. Isolines: Energy-efficient Mapping in Sensor Networks. In ISCC, 2005.

IPCCC’11 11

Outline

Motivation

Classification of Existing Map Construction

Approaches

Performance Comparison in a Wide Range

Scenarios

Conclusions

IPCCC’11 12

Evaluation Framework: Methodology Selected map construction protocols

Region aggregation class: eScan Data suppression class: Isoline

Simulations using OMNet++ Network

• Area : 300 x 300 m²• Topology: Grid or random

Tree-based routing protocol

Performance metrics Map accuracy: The ratio of false classified sensors to all

sensor nodes. Energy efficiency: Network traffic

IPCCC’11 13

Evaluation Framework: Comparative Studies

Compare for a wide range of parameters:

Impact of physical phenomena properties Hotspot effect range : limited vs. diffusive Hotspot number : 1 vs. n

Impact of protocol parameters Sensor value range [0, 60], classes: [0, GV[, [GV, 2GV[ ...

• Signal discretization (Granularity value: GV) GV=5…25

Impact of network properties Node density N=256(16x16)...1225

(35x35) Communication failures BER=0…10-2

Communication range CR=60m

IPCCC’11 14

Granularity increases # Isolines and #Iso-nodes decrease

-> lower msg overhead Region size increase -> lower msg

overhead Accuracy

Isoline always outperforms eScan Efficiency

Isoline outperforms eScan for lower granularities

50 40 30 20 10

50 25

(b) Step value = 25 unit

Comparison: Impact of Granularity

0

0.2

0.4

0.6

0.8

1

5 10 15 20 25

Acc

ura

cy

Granularity Value

BER=1E-4, N=256, CR=60m

eScan_grideScan_random

Isoline_gridIsoline_random

0

5000

10000

15000

20000

25000

30000

35000

40000

5 10 15 20 25

Netw

ork

Tra

ffic

[byte

]

Granularity Value

BER=1E-4, N=256, CR=60m



(a) Step value = 5 unit

IPCCC’11 15

Comparison: Impact of BER

BER increases Loss of messages -> lower

msg overhead Overhead reduction is

higher for eScan

Higher BER decreases map accuracy Loss of messages -> gaps in

the map• Higher accuracy drop for

eScan

IPCCC’11 16

Comparison: Impact of Node Density

Node density increases #Iso-nodes increases ->

higher msg overhead #Region and “region border

information” increase -> higher msg overhead

0

20000

40000

60000

80000

100000

120000

140000

160000

300 400 500 600 700 800 900 1000 1100 1200

Netw

ork

Tra

ffic [

byte

]

#Nodes

BER=1E-4, CR=60m, GV=5



0

0.2

0.4

0.6

0.8

1

300 400 500 600 700 800 900 1000 1100 1200

Accura

cy

#Nodes

BER=1E-4, CR=60m, GV=5



Node density has low impact on map accuracy Region border precision

increases -> provide a more detailed map

IPCCC’11 17

Conclusions

Region aggregation class Data suppression class

＋High accuracy with reliable comm.

- Less suitable for less reliable comm.

＋high accuracy for reliable comm.

＋performs also well for less reliable comm.

＋accuracy increases with increasing granularity value

＋Small granularity value

＋Low density network

- Small granularity value

＋ low density network

Acc

ura

cyE

ffici

en

cy

IPCCC’11 18

Thanks for Your Attention!

ipccc’111 assessing the comparative effectiveness of map construction protocols in wireless sensor...

Documents