wireless sensor

1. INTRODUCTION

Wireless Sensor Networks, with the characteristics of low energy consumption, low cost,

distributed and self organization, have brought a revolution to the information perception.

The wireless sensor network is composed of hundreds of thousands of the sensor nodes

that can sense conditions of surrounding environment such as illumination, humidity, and

temperature. Each sensor node collects data such as illumination, humidity, and

temperature of the area. Each sensor node is deployed and transmits data to base station

(BS). The wireless sensor network can be applied to variable fields. For example, the

wireless sensor network can be used to monitor at the hostile environments for the use of

military applications, to detect forest fires for prevention of disasters, or to study the

phenomenon of the typhoon for a variety of academic purposes. These sensor nodes can

self organize to form a network and can communicate with each other using their wireless

interfaces. Energy efficient self organization and initialization protocols are developed in,

[2]. Each node has transmitted power control and an Omni directional antenna, and

therefore can adjust the area of coverage with its wireless transmission. Typically, sensor

nodes collect audio, seismic, and other types of data and collaborate to perform a high-level

task in a sensor web. For example, a sensor network can be used for detecting the presence

of potential threats in a military conflict. Most of battery energy is consumed by receiving

and transmitting data. If all sensor nodes transmit data directly to the BS, the furthest node

from BS will die early. On the other hand, among sensor nodes transmitting data through

multiple hops, node closest to the BS tends to die early, leaving some network areas

completely unmonitored and causing network partition. In order to maximize the lifetime of

WSN, it is necessary for communication protocols to prolong sensor nodes’ lifetime by

minimizing transmission energy consumption, sending data via paths that can avoid sensor

nodes with low energy and minimizing the total transmission power.

M. Tech (ACS), NIT Warangal

Figure 1.1 A typical Wireless Sensor Network.

Figure 1.2: Schematic of a Wireless Sensor Network Architecture


1.1 Architecture of Wireless Sensor Network:

Figure 1.2 shows a typical schematic of a wireless sensor network (WSN). After the initial

deployment (typically ad hoc), sensor nodes are responsible for self-organizing an

appropriate network infrastructure, often with multi-hop connections between sensor

nodes [30]. The onboard sensors then start collecting acoustic, seismic, infrared or magnetic

information about the environment, using either continuous or event driven working

modes. Location and positioning information can also be obtained through the global

positioning system (GPS) or local positioning algorithms. This information can be gathered

from across the network and appropriately processed to construct a global view of the

monitoring phenomena or objects. The basic philosophy behind WSNs is that, while the

capability of each individual sensor node is limited, the aggregate power of the entire

network is sufficient for the required mission.

In general, the wireless sensor networks are deployed for monitoring at a large area so the

wireless sensor networks need many sensor nodes. If the sensor node consumes completely

energy, it is wasted. We do not consider to recharge and to reuse sensor node. Because of

these reasons, the value of the sensor nodes must be inexpensive to practical use. Deployed

in harsh and complicated environments, the sensor nodes are difficult to recharge or

replace once their energy is drained. Meanwhile the sensor nodes have limited

communication capacity and computing power. So how to optimize the communication

path, improve the energy-efficiency as well as load balance and prolong the network

lifetime has became an important issue of designing routing protocols for WSN.

Hierarchical-based routing protocols [6] are widely used for their high energy-efficiency and

good expandability. The basic idea of them is to select some nodes in charge of a certain

region routing. These selected nodes have greater responsibility relative to other nodes

which leads to the incompletely equal relationship between sensor nodes. LEACH (Low

Energy Adaptive Clustering Hierarchy) [7], PEGASIS (Power-Efficient Gathering in Sensor

Information System) [8] are the typical hierarchical-based routing protocols. As an

enhancement algorithm of LEACH, PEGASIS is a classical chain-based routing protocol. It

saves significant energy compared with the LEACH protocol by improving the cluster

configuration and the delivery method of sensing data.


1.2 The differences between WSNs and traditional networks

Wireless sensor networks, on the one hand, share the similarity of self-configuration

without manual management with Mobile ad-hoc networks; on the other hand, they are

different from traditional networks in many aspects due to their strict energy constraints

and application specific characteristics.

NO one-size-fits-all solution: A WSN is organized as a collection of sensor nodes which co-

ordinate with each other to fulfil a certain task. The entire network infrastructure depends

directly on the specific application scenario. It is unlikely that a one-size-fits-all solution

exists for all these different applications. The old fixed protocol stack which applied

successfully to traditional networks is no longer suitable for WSNs. Many new

communication algorithms have been developed for different applications. As one example,

WSNs are deployed with very different network densities, from sparse to dense

deployments. Each case requires unique network configuration.

Environment interaction: The traffic loads relayed in WSNs are generated by the sensors

which interact entirely with the environment. By contrast, the traffic loads of tradition

network are mainly driven by human behaviour. Moreover, the environment plays a key

role in determining the size of the network, the deployment scheme, and the network

topology. The size of the network varies with the monitored environment. For indoor

environments, fewer nodes are required

to form a network in a limited space whereas outdoor environments may require more

nodes to cover a larger area.

Resource constraints: Resource constraints include a limited amount of energy, short

communication range, low bandwidth, and limited processing and storage in each node. For

wireless sensor networks, energy is a scare resource. This is unlike wireless ad-hoc networks

which can recharge or replace batteries quite easily. In some cases, the need to prolong the

lifetime of a sensor node has a deep impact on the entire WSN system architecture.


Reliability and QoS: The WSNs exhibit very different concepts of reliability and quality of

Service from traditional networks. They totally depend on the task assigned. In some

emergency cases, only occasional delivery of packets can be more than enough; in other

cases, very high reliability requirements exist. Packet delivery ratio in WSNs is no longer an

sufficient metric, instead, different applications may take their own requirements into

consideration.

1.3Design challenges:

WSNs distinguish themselves from traditional networks due to their application specific and

energy constraints. Their structure and characteristics depend on their electronic,

mechanical and communication limitations but also on application specific requirements.

One of the major and probably most important challenges in the design of WSNs is their

application specific characteristic. A sensor network is set up to fulfil a specific task and the

data collected from the network may be of different types due to various application

scenarios. Respectively, different types of applications have their own specific requirements.

These requirements are turned into specific design properties of a WSN. In other words, a

WSN's architecture directly depends on the assigned application scenarios. For the

acceptable performance of a given task, the optimal WSN infrastructure should be selected

out of the hundreds of network solutions before the practical deployment.

Equally, an issue that has been frequently emphasized in the research literature is the fact

that energy resources are significantly limited. Recharging or replacing the battery of sensor

nodes may be difficult or impossible. Hence, power efficiency often turns out to be the

major performance metric, directly influencing the network lifetime. Power consumption

according to the functioning of a sensor node can be divided into three domains: sensing,

communication, and data processing. There has been research effort in hardware

improvements to optimize the energy consumed by sensing and data processing. Several

studies of energy efficiency of WSNs have been discussed and several algorithms that lead

to optimal connectivity topologies for power conservation have been proposed [10][18].


Another issue in the design of WSNs is that performance assessment of a WSN always

happens once deployed. The analysis procedure follows the order that people in this field

first put more and more effort into inventing new protocols and new applications; then the

solutions are built, tested and evaluated either by simulation or test beds; even sometimes

an actual system has to be deployed so that researchers can learn by empirical evidence. A

more scientific analysis procedure is ideally required before a WSN is practically deployed.

Current WSN designers are mainly experts in wireless sensor networking and hardware who

could perceive the communication between each node at the bit level. When a new

protocol is developed, they could construct algorithms even if the required simulation tool

did not exist. As WSNs immerse deeper into people's work, they must begin to include less

specialized users.

1.4 Thesis Contributions

The work reported herein investigates chaining mechanism in PEGASIS using evolutionary

algorithms like Ant Colony optimisation and Genetic algorithms and lifetime enhancement

by chain leader selection criteria and maintenance of priority queue at each node if the next

node fails. Lifetime measurement of WSN using various types of PEGASIS variants for both

Homogenous and heterogeneous has been evaluated.

1.5 Thesis Outline

The thesis has been organised in the fallowing manner. Fallowing this chapter, chapter 2

presents extensive literature survey on routing algorithms for WSN. It mainly discusses

energy efficient hierarchical routing protocols for WSN. Evolutionary algorithms are also

presented in this section. Chain forming mechanism using GREEDY algorithm is presented in

chapter 3. It mainly investigates the lifetime of PEGASIS protocol under various scenarios.

Chapter 4 deals with Ant Colony Optimisation technique applied to PEGASIS protocol and

lifetime Measurement. Chapter 5 deals with Genetic algorithm and its lifetime

measurement. Chapter 6 gives the comparative study of all the algorithms proposed.


2 LITERATURE SURVEY OF ROUTING PROTOCOLS FOR WSN

2.1 Introduction

Wireless sensor networks have their own unique characteristics which create new

challenges for the design of routing protocols for these networks. First, sensors are very

limited in transmission power, computational capacities, storage capacity and most of all, in

energy. Thus, the operating and networking protocol must be kept much simpler as

compared to other ad hoc networks. Second, due to the large number of application

scenarios for WSN, it is unlikely that there will be a one-thing-fits-all solution for these

potentially very different possibilities. The design of a sensor network routing protocol

changes with application requirements. For example, the challenging problem of low-

latency precision tactical surveillance is different from that required for a periodic weather-

monitoring task. Thirdly, data traffic in WSN has significant redundancy since data is

probably collected by many sensors based on a common phenomenon. Such redundancy

needs to be exploited by the routing protocols to improve energy and bandwidth utilization.

Fourth, in many of the initial application scenarios, most nodes in WSN were generally

stationary after deployment. However, in recent development, sensor nodes are

increasingly allowed to move and change their location to monitor mobile events, which

results in unpredictable and frequent topological changes [10].

Due to such different characteristics, many new protocols have been proposed to solve the

routing problems in WSN. These routing mechanisms have taken into consideration the

inherent features of WSN, along with the application and architecture requirements. To

minimize energy consumption, routing techniques proposed in the literature for WSN

employ some well-known ad hoc routing tactics, as well as, tactics special to WSN, such as

data aggregation and in-network processing, clustering, different node role assignment and

data-centric methods. In the following sections, introduction to current research on routing

protocols has been presented.


2.2 Routing Challenges and Design Issues in WSNs:

Despite plethora of applications of WSN, these networks have several restrictions, e.g.,

limited energy supply, limited computing power, and limited bandwidth of the wireless links

connecting sensor nodes. One of the main design goals of WSN is to carry out data

communication while trying to prolong the lifetime of the network and prevent connectivity

degradation by employing aggressive energy management techniques. In order to design an

efficient routing protocol, several challenging factors should be addressed meticulously. The

following factors are discussed below:

Node deployment: Node deployment in WSN is application dependent and affects the

performance of the routing protocol. The deployment can be either deterministic or

randomized. In deterministic deployment, the sensors are manually placed and data is

routed through pre-determined paths; but in random node deployment, the sensor nodes

are scattered randomly creating an infrastructure in an ad hoc manner. Hence, random

deployment raises several issues as coverage, optimal clustering etc. which need to be

addressed.

Energy consumption without losing accuracy: sensor nodes can use up their limited supply

of energy performing computations and transmitting information in a wireless environment.

As such, energy conserving forms of communication and computation are essential. Sensor

node lifetime shows a strong dependence on the battery lifetime. In a multi hop WSN, each

node plays a dual role as data sender and data router. The malfunctioning of some sensor

nodes due to power failure can cause significant topological changes and might require

rerouting of packets and reorganization of the network.

Node/Link Heterogeneity: Some applications of sensor networks might require a diverse

mixture of sensor nodes with different types and capabilities to be deployed. Data from

different sensors, can be generated at different rates, network can follow different data

reporting models and can be subjected to different quality of service constraints. Such a

heterogeneous environment makes routing more complex.


Fault Tolerance: Some sensor nodes may fail or be blocked due to lack of power, physical

damage, or environmental interference. The failure of sensor nodes should not affect the

overall task of the sensor network. If many nodes fail, MAC and routing protocols must

accommodate formation of new links and routes to the data collection base stations. This

may require actively adjusting transmit powers and signalling rates on the existing links to

reduce energy consumption, or rerouting packets through regions of the network where

more energy is available. Therefore, multiple levels of redundancy may be needed in a fault-

tolerant sensor network.

Scalability: The number of sensor nodes deployed in the sensing area may be in the order of

hundreds or thousands, or more. Any routing scheme must be able to work with this huge

number of sensor nodes. In addition, sensor network routing protocols should be scalable

enough to respond to events in the environment. Until an event occurs, most of the sensors

can remain in the sleep state, with data from the few remaining sensors providing a coarse

quality.

Network Dynamics: Most of the network architectures assume that sensor nodes are

stationary. However, mobility of both BS‘s and sensor nodes is sometimes necessary in

many applications. Routing messages from or to moving nodes is more challenging since

route stability becomes an important issue, besides energy, bandwidth etc. Moreover, the

sensed phenomenon can be either dynamic or static depending on the application, e.g., it is

dynamic in a target detection/tracking application, while it is static in forest monitoring for

early fire prevention. Monitoring static events allows the network to work in a reactive

mode, simply generating traffic when reporting. Dynamic events in most applications

require periodic reporting and consequently generate significant traffic to be routed to the

BS.

Transmission Media: In a multi-hop sensor network, communicating nodes are linked by a

wireless medium. The traditional problems associated with a wireless channel (e.g., fading,

high error rate) may also affect the operation of the sensor network. As the transmission

energy varies directly with the square of distance therefore a multi-hop network is suitable


for conserving energy. But a multi-hop network raises several issues regarding topology

management and media access control. One approach of MAC design for sensor networks is

to use CSMA-CA based protocols of IEEE 802.15.4 that conserve more energy compared to

contention based protocols like CSMA (e.g. IEEE 802.11). So, Zigbee which is based upon

IEEE 802.15.4 LWPAN technology is introduced to meet the challenges.

Connectivity: The connectivity of WSN depends on the radio coverage. If there exists a

multi-hop connection between any two nodes continuously, the network is connected. The

connectivity is intermittent if WSN is partitioned occasionally, and sporadic if the nodes are

only occasionally in the communication range of other nodes.

Coverage: The coverage of a WSN node means either sensing coverage or communication

coverage. Typically with radio communications, the communication coverage is significantly

larger than sensing coverage. For applications, the sensing coverage defines how to reliably

guarantee that an event can be detected. The coverage of a network is either sparse, if only

parts of the area of interest are covered or dense when the area is almost completely

covered. In case of a redundant coverage, multiple sensor nodes are in the same area.

Data Aggregation: Sensor nodes usually generate significant redundant data. So, to reduce

the number of transmission, similar packets from multiple nodes can be aggregated. Data

aggregation is the combination of data from different sources according to a certain

aggregation function, e.g., duplicate suppression, minima, maxima and average. It is

incorporated in routing protocols to reduce the amount of data coming from various

sources and thus to achieve energy efficiency. But it adds to the complexity and makes the

incorporation of security techniques in the protocol nearly impossible.

Data Reporting Model: Data sensing and reporting in WSNs is dependent on the application

and the time criticality of the data reporting. In wireless sensor networks data reporting can

be continuous, query-driven or event-driven. The data-delivery model affects the design of

network layer, e.g., continuous data reporting generates a huge amount of data therefore,

the routing protocol should be aware of data-aggregation


Quality of Service: In some applications, data should be delivered within a certain period of

time from the moment it is sensed; otherwise the data will be useless. Therefore bounded

latency for data delivery is another condition for time-constrained applications. However, in

many applications, conservation of energy, which is directly related to network lifetime, is

considered relatively more important than the quality of data sent. As the energy gets

depleted, the network may be required to reduce the quality of the results in order to

reduce the energy dissipation in the nodes and hence lengthen the total network lifetime.

Hence, energy-aware routing protocols are required to capture this requirement.

2.3 Classification of Routing Protocols in WSNs:

In general, routing in WSNs can be divided into flat-based routing, hierarchical-based

routing, and location-based routing depending on the network structure. In flat-based

routing, all nodes are typically assigned equal roles or functionality. In hierarchical-based

routing, however, nodes will play different roles in the network. In location-based routing,

sensor nodes' positions are exploited to route data in the network.

A routing protocol is considered adaptive if certain system parameters can be controlled in

order to adapt to the current network conditions and available energy levels. Furthermore,

these protocols can be classified into multipath-based, query-based, negotiation-based,

QoS-based, or routing techniques depending on the protocol operation. In addition to the

above, routing protocols can be classified into three categories, namely, proactive, reactive,

and hybrid protocols depending on how the source sends a route to the destination. In

proactive protocols, all routes are computed before they are really needed, while in reactive

protocols, routes are computed on demand. Hybrid protocols use a combination of these

two ideas. When sensor nodes are static, it is preferable to have table driven routing

protocols rather than using reactive protocols. A significant amount of energy is used in

route discovery and setup of reactive protocols. Another class of routing protocols is called

the cooperative routing protocols. In cooperative routing, nodes send data to a central node

where data can be aggregated and may be subject to further processing, hence reducing

route cost in terms of energy usage.


Figure 2.1 Classification of routing protocols

2.4 Previous Work:

In this section a brief review of the related work on the analysis of PEGASIS protocol is

presented. Cosmin Cirstea [10] provides an up to date evaluation of routing protocols as

well as a description of state of the art routing techniques for Wireless Sensor Networks

(WSNs) that enhance network lifetime through efficient energy consumption methods. The

tradeoffs between energy and communication overhead are studied. The advantages and

disadvantages of each routing protocol with the purpose of discovering new research

directions are highlighted.

Stephanie Lindsey et. al. [11], proposed PEGASIS (Power-Efficient Gathering in Sensor

Information Systems) Protocol which is a near optimal chain-based protocol, an

improvement over LEACH. In PEGASIS, each node communicates only with a close neighbour

and takes turns transmitting to the base station, thus reducing the amount of energy spent

per round.

Dali Wei et. al [12], proposes a distributed clustering algorithm that determines suitable

cluster sizes depending on the hop distance to the data sink, while achieving approximate

equalization of node lifetimes and reduced energy consumption levels. A simple energy-


efficient multi hop data collection protocol to evaluate the effectiveness of Energy Efficient

Clustering. The end to end energy consumption of this protocol is caluculated. EC is suitable

for any data collection protocol that focuses on energy conservation. Performance results

demonstrate that EC extends network lifetime and achieves energy equalization more

effectively than two well known clustering algorithms, HEED and UCR.

Ossama Younis et. al [13], decribed HEED (Hybrid Energy-Efficient Distributed clustering),

that periodically selects cluster heads according to a hybrid of the node residual energy and

a secondary parameter, such as node proximity to its neighbours or node degree. HEED

terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform

cluster head distribution across the network. With appropriate bounds on node density ,

intra cluster and inter cluster transmission ranges; HEED can asymptotically almost surely

guarantee connectivity of clustered networks.

A three-layered routing protocol for WSN based on LEACH (TL-LEACH) is given by Deng

Zhixiang et. al. [14]. The improved LEACH protocol is simulated and the simulation results

show that TL-LEACH protocol has greatly improved WSN lifetime than LEACH protocol.

Indu Shukla [15], has discussed PEGASIS protocol. PEGASIS protocol forms a chain of sensor

nodes, where each sensor node only communicates with their neighbours. Sensor nodes are

deployed in harsh physical environment. Sensor nodes have very limited computation

capability because they are limited by the battery power. It has been a challenge to

maximize the use of energy of these sensor nodes to extend the network lifetime. The

implementation of PEGASIS protocol is also presented.

Jian Wan et. al [16], presented a review of recent routing protocols in WSNs and classified

them into three categories based on the network structure in WSNs. A description of the

existing routing protocols is presented and discussed their advantages and disadvantages.

Finally, paper is concluded with open research issues and challenges.

Tao Liu et. al [17], proposes a new type of routing protocol for WSN called PECRP (Power-

efficient Clustering Routing Protocol), which is suitable to long-distance and complex data


transmission (e.g. patient-surveillance or chemical detection in agriculture), and for fixed

sensor nodes of WSN. PECRP combines the advantages of some excellent cluster-based

routing protocols together, such as HEED (Hybrid Energy efficient Distributed Clustering

Approach), PEGASIS (Power Efficient Gathering in Sensor Information Systems) and so on.

PECRP improves the mechanism in electing CHs (cluster heads) of LEACH, and elects more

appropriate nodes to be CHs, which could prolong the lifetime of WSN obviously. In data

transmission, PECRP uses multi-hop transmission that is called “circle domino effect based

on distance to BS (Base Station)” to balance the energy consumption in nodes. multi-hop

transmission can prolong the lifetime of WSN in narrow sense situation is proved based on

mathematical proofs

Zheng Gengsheng et. al [18], described a two layer hierarchical routing protocol called Chain

Routing Based on Coordinates-oriented Clustering Strategy (CRBCC), which gives a good

compromise between energy consumption and delay. First, CRBCC makes balanced

clustering according to y coordinates where each cluster has approximately equal number of

nodes. Second, CRBCC makes chain routing by simulated annealing algorithm (SA) inside the

cluster and elects chain leader in the order of x coordinates. Third, CRBCC makes chain

routing again by SA method among chain leaders. Simulation results show that CRBCC

performs better than PEGASIS in terms of energy efficiency and network delay.

Hao Wu et. al. [19], proposes a Chain-based Fast Data Aggregation Algorithm Based on

Suppositional Cells (CFDASC) to solve this problem. In this algorithm, Author attributed each

node to one suppositional cell according to the node location information. The nodes which

are in one suppositional cell act as the cluster head of data collection in turn, then the head

gathers and transmits data along the cells chain to the sink node. As a result, it accelerates

the data aggregation process. Simulation shows that COSEN noticeably give a good

compromise between energy efficiency and latency.

Considerable amount of energy may be wasted when nodes which are far away from sink

node act as the head. DERP (Distance-based Energy-efficient Routing Protocol) is proposed by

Hyunduk Kim et. al. [20] to address the problem of making far away as head. DERP is a chain-based

protocol that improves the greedy-algorithm in PEGASIS by taking into account the distance from the


HEAD to the sink node. The main idea of DERP is to adopt a pre-HEAD (P-HD) to distribute the energy

load evenly among sensor nodes. In addition, to scale DERP to a large network, it can be extended to

a multi hop clustering protocol by selecting a “relay node” according to the distance between the P-

HD and SINK. Analysis and simulation studies of DERP show that it consumes up to 80% less energy,

and has less of a transmission delay compared to PEGASIS.

M. Tabibzadeh et. al.[21], proposed a hybrid protocol, called collectively Chain-based LEACH

(CBL) that improves the Low-Energy Adaptive Clustering Hierarchy (LEACH) to significantly

reduce energy consumption and increase the lifetime of a sensor network. CBL protocol

uses LEACH and the advantages of Power-Efficient Gathering in Sensor Information Systems

(PEGASIS) and avoids their disadvantages. LEACH technique improves energy efficiency of a

sensor network by selecting a cluster-head, and having it aggregate data from other nodes

in its cluster, and PEGASIS is a near optimal chain-based protocol used for communication

and extra aggregation between cluster-heads that are neighbours and takes turns

transmitting to the sink.

Wenjing Guo et. al. [22], presented a routing protocol for the applications of Wireless

Sensor Network (WSN). It is a protocol based on the PEGASIS protocol but using an

improved ant colony algorithm rather than the greedy algorithm to construct the chain.

Compared with the original PEGASIS, this one, Pegant, can achieve a global optimization. It

forms a chain that makes the path more even-distributed and the total square of

transmission distance much less. Moreover, in the constructing process, the energy factor

has been taken into account, which brings about a balance of energy consumption between

nodes. In each round of transmission, according to the current energy of each node, a

leader is selected to directly communicate with the base station (BS). Simulation results

have shown that the proposed protocol significantly prolongs the network lifetime.

In order to reduce energy consumption, Young-Long Chen et. al. [23] first shows ideal

energy mathematical model of PEGASIS topology, since the distance between nodes is the

same, this energy mathematical model is the longest network lifetime of WSNs. To achieve

this objective, Intra- Grid PEGASIS topology architecture is proposed, which is an

architecture based on PEGASIS topology. In this architecture, the sensor area is divided into


several network grids, meanwhile, the nodes of each network grid is deployed in random,

then the nodes within the network grid are connected, finally, all the network grids are

connected.

Yongchang Yu et. al. [24], proposed EECB (Energy-Efficient Chain-Based routing protocol)

which is an improvement over PEGASIS. EECB uses distances between nodes and the BS and

remaining energy levels of nodes to decide which node will be the leader that takes charge

of transmitting data to the BS. Also, EECB adopts distance threshold to avoid formation of LL

(Long Link) on the chain.

Feng Sen et. al. [25], propose EEPB (Energy-Efficient PEGASIS-Based protocol). It is a chain-

based protocol which has certain deficiencies including the uncertainty of threshold

adopted when building a chain, the inevitability of long link (LL) when valuing threshold

inappropriately and the non-optimal election of leader node. Aiming at these problems, an

improved energy-efficient PEGASIS-based protocol (IEEPB) is proposed in this paper. IEEPB

adopts new method to build chain, and uses weighting method when selecting the leader

node, by assigning each node a weight so as to represent its appropriate level of being a

leader which considers residual energy of nodes and distance between a node and base

station (BS) as key parameters.

Young-Long et al. [26], discussed the PEGASIS topology architecture with the PBCA (Phase-

Based Coverage Algorithm) to find the redundant nodes which can enter to sleep mode.

Therefore, proposed algorithm can reduce the energy consumption of nodes and extend the

network lifetime. Simulation results show that the performances of this algorithm

outperformance the LEACH topology architecture, the PEGASIS topology architecture, and

the LEACH with PBCA topology architecture in terms of energy consumptions, number of

nodes alive, and sensing areas.


3. PEGASIS (Power Efficient Gathering in Sensor Information Systems)

Wireless sensor nodes sense data and send it directly to the base station or they perform a

clustering procedure as in LEACH. LEACH is known for cluster formation which contains

cluster members sensing the data and the cluster head which gathers the data collected in a

fused manner (all the data is sent as a single packet) to the base station. This procedure has

gained in conserving a lot of energy that would otherwise be wasted. PEGASIS is an

extension to LEACH; it has better ways of conserving energy which last even more than

using cluster mechanism in LEACH [30].

When the nodes in the network which are at some distance from the base station, the

easiest and the simplest way of transmitting the sensed data to the base station is to

transmit it directly, which may lead to quicker depletion of energy in all the nodes. The

nodes at a large distance away from the base station are depleted quicker than the nodes

which are closer to the base station as they need some extra energy to reach the farthest

base station. Another approach where energy is consumed in low amounts is by forming

cluster heads and cluster members using the sensor nodes in the network. Cluster members

perform the sensing and computing the data (Data Fusion) and the cluster heads transmit

the fused data to the base station. All the nodes in the network take their chance to act as

cluster heads to send the fused data to the base station; again the farthest cluster head

needs some extra energy to send the data to the base station.

The key idea in using PEGASIS is that it uses all the nodes to transmit or receive with its

closest neighbor nodes. This is achieved by the formation of a chain as shown in the Figure

below. All the nodes which collect the data fuse it with the data received by the neighbor

node and transmit it to the next-nearest neighbor. In this way all the nodes receive and fuse

their data, and pass it to the next neighbor in a chain format till they all reach the base

station. Every node in the network takes turns as a leader of the chain and the one

responsible to transmit the whole fused data collected by the chain of nodes to the base

station [31].


Figure 3.1: Chain formation in PEGASIS

In this way the average amount of energy spent by each node is reduced. Greedy algorithms

are used to see that all nodes are used during the chain formation. PEGASIS assumes that all

the nodes with varying or low energy levels can be compensated in order to calculate the

energy cost of the transmissions with the remaining energy they are left with. It is not

necessary that all the nodes need to know its neighboring nodes, the base station can

determine the path or form the chain for all nodes, or all the nodes can determine their

neighboring nodes by sending a signal. Depending upon the signal strength, the nodes

adjust their signal such that they hear only the nearest neighbors in the network.

From Figure 3.2 below, the operation of PEGASIS is clearly understood. A greedy algorithm is

applied to form a chain among all the best nodes that are at a one-hop distance from each

other and to the base station. If the farthest node is selected, it starts transmitting the data.

For example, if node 4 start the chain formation process and it sends the signal to the nodes


in the network to find the nearest neighbor, node 3 is the nearest, so it transmits the sensed

data to node 4.

Upon receiving the data from node 4 node 3 starts finding the nearest neighbor by sending

signals and when it finds that node 2 is the nearest, it fuses its own data with the data

received from node 4 and transmits all this data to node 2. Node 2 finds node 1 as the

nearest and transmits the sensed data with the fused data (the whole data is formatted a

single packet). Now node 1 is the nearest node to the base station, so it acts as a leader and

transmits all the data. Only the first node in the chain have nothing to fuse except the data it

has during the chain formation, the remaining nodes all have some data to append with the

received data from other nodes [31].

This approach will distribute the energy load evenly among the sensor nodes in the network

as it uses all the nodes of the network to form the chain and perform simple data

forwarding operations. If any node dies in the chain, a new chain is formed, eliminating the

dead nodes.


From the simulation reported in [13], it is clear that PEGASIS improves on LEACH by saving

energy at different stages, such as for example cluster-member forming and cluster heads.

Here all the nodes have an equal chance of becoming the leader once and transmit data to

the base station in one round. An energy balance is estimated on the nodes in the network

which conserves lot of energy. The amount of nodes that die during the chain process is

reduced when compared to LEACH for all types of network sizes and topologies. The

network lifetime is increased, as all the nodes actively participate and deplete the equal

amount of energy on the whole [13]. A simulation analysis of PEGASIS is reported in [13],

comparing it with the LEACH protocol using different network topologies. Many

experimental results proved that PEGASIS is supporting longer network lifetime, more

balanced energy dissipation and higher performance.

PEGASIS uses a greedy algorithm to form a chain using the nodes in the network to transmit

Data to the base station; it has no location awareness of the sensor nodes in the network

and looks only for the closest neighbour that it can reach. Discovering a new route is difficult

if a node fails, as it has a fixed path every time before it starts a new route towards the sink

for transmission. Though its approach in conserving energy is better, it lacks in maintaining

focus on quality-of-service factors. For instance, it cannot resist uneven traffic distribution

for all those nodes which are not in the single-hop range; it has to make a multi-hop

structure for adding such nodes

3.1 Greedy Algorithm Chain Formation:

Greedy chain algorithm begins at a farthest node from the sink, which is the only node in

the chain at first. Each terminal node of the chain finds a closest node from the remaining

nodes set which are not in the chain. Then the closest node will join the chain and be the

new terminal node of the chain. The process repeats till all the nodes join the chain. The

greedy chain algorithm in PEGASIS is as follows.

The main advantages of PEGASIS are:

The transmission distances between nodes are minimized.

The number of sensor nodes that must send packets to the sink is minimized


The main drawbacks of PEGASIS are:

It has excessive delay introduced by the single chain.

Greedy algorithm using in PEGASIS is a local search, which cannot provide a global

optimal route.

3.2 Data Aggregation in PEGASIS

In cluster-based sensor networks, sensors transmit data to the cluster head where data

aggregation is performed. However, if the cluster head is far away from the sensors, they

might expend excessive energy in communication. Further improvements in energy

efficiency can be obtained if sensors transmit only to close neighbours. The key idea behind

chain based data aggregation is that each sensor transmits only to its closest neighbour. In

PEGASIS, nodes are organized into a linear chain for data aggregation. The nodes can form a

chain by employing a greedy algorithm or the sink can determine the chain in a centralized

manner. Greedy chain formation assumes that all nodes have global knowledge of the

network. The farthest node from the sink initiates chain formation and at each step, the

closest neighbour of a node is selected as its successor in the chain. In each data gathering


Procedure ConstructGreedyChain(N,END)

1. Begin

2. N={all nodes};

3. END = farthest node from SINK;

4. Chain= {END};

5. N=N-{END};

6. if (N!=NULL)

7. {

8. END=FindCloseNode(N,END);

9. Append(chain,END);

10. goto 5.

11. }

12. END

round, a node receives data from one of its neighbours, fuses the data with its own and

transmits the fused data to its other neighbour along the chain. Eventually the leader node

which is similar to cluster head transmits the aggregated data to the sink. Figure 3.3 shows

the chain based data aggregation procedure in PEGASIS. Nodes take turns in transmitting to

the sink. The greedy chain formation approach used in [33] may result in some nodes having

relatively distant neighbours along the chain. This problem is alleviated by not allowing such

nodes to become leaders.

Figure 3.3 Chain based organization in a sensor network. The ovals indicate sensors and

the arrows indicate the direction of data transmission

The PEGASIS protocol has considerable energy savings compared to LEACH. The distances

that most of the nodes transmit are much less compared to LEACH in which each node

transmits to its cluster head. The leader node receives at most two data packets from its

two neighbours. In contrast, a cluster head in LEACH has to perform data fusion of several

data packets received from its cluster members. The main disadvantage of PEGASIS is the

necessity of global knowledge of all node positions to pick suitable neighbours and minimize

the maximum neighbour distance. In addition, PEGASIS assumes that all sensors are

equipped with identical battery power and results in excessive delay for nodes at the end of

the chain which are farther away from the leader node. In [29], two other protocols viz., a

binary chain based scheme and a three-level chain based scheme have been proposed. In

the binary chain based protocol, each node transmits data to a close neighbour in a given

level of the hierarchy. The nodes that receive data at each level form a chain in the next

higher level of the hierarchy. At the highest level, the leader node transmits the aggregated


data to the sink. In the three level schemes, the protocol starts with the formation of a

linear chain among all nodes and then it divides them into G groups. Each group has N/G

successive nodes of the chain where N is the total number of nodes. Only one node from

each group participates in the second level of the hierarchy. The G nodes in the second level

are further divided into two groups so that only three levels are maintained in the hierarchy.

3.3 Simulation Parameters

One Hundred Wireless Sensor Nodes are deployed randomly in 100m x 100m area each with

initial energy of 1 or 2 Joule. Packet length of 1000 bits is assumed. The energy consumed in

processing of one bit of data both in transmitting and receiving electronics (Eelec) is taken as

50nJ/bit. The energy consumed in transmitting amplifier (Eamp) for transmitting a bit for unit

distance is taken as 100pJ/bit/m2. The sink or gateway is assumed at the coordinates

(25,150) so that a minimum distance of at least 50m from any node is present.

The fallowing figure shows the random deployment of WSN nodes in 100m X 100m area.

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100100nodes random deployment

length in meters

leng

th in

met

ers

Figure 3.4 Random deployment of WSN Nodes


Figure 3.5 shows the chain formed in PEGASIS using greedy algorithm described earlier. The

total length of the chain is 903meters.

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100100nodes network chain formation using greedy algorithm

lenght in meters

leng

th in

met

ers

903.834

Figure 3.5 Chain formations in PEGASIS using Greedy algorithm.


3.3.1 WIRELESS SENSOR NETWORK RADIO POWER MODEL

A Wireless Sensor Network will comprise of the fallowing

A fixed base station (BS) and N wireless sensor nodes

BS has high-energy

Figure 3.6: WSN forming PEGASIS chain and Base Station

Figure 3.7: Transmitter and Receiver Energy Model diagram

For simulation of different WSN scenarios, we use a radio energy model in [33], in which the

energy dissipation ET (k, d) of transmitting k-bit data between two nodes separated by a

distance of d meters is given as follows:

(1)


Where Eelec denotes electronic energy and Eta denotes transmit amplifier parameters

corresponding to the free space model.

The energy cost incurred in the receiver of the destination sensor node is given as follows:

(2)

For simulation a simple model where the radio dissipates Eelec = 50 nJ/bit to run the

transmitter or receiver circuitry and Eamp = 100 pJ/bit/m2 for the transmit amplifier to

achieve an acceptable Eb/N0.

We know the energy resources are mainly consumed by radio and CPU components [8]. We

use the above radio power model to compute energy dissipation of radio. However, the

energy dissipation of CPU is more difficult to compute in the simulator, because CPU is

driven by the software running on it.


3.4 Homogenous PEGASIS with Greedy Chain

In homogenous network all the nodes Wireless Sensor Network are having same energy of

1Joule each.

3.4.1 Max Energy Node as Cluster Head

In this case the node having the maximum energy is selected as the cluster head. For this all

the nodes while transmitting the data to the chain leader, also indicate their current energy

and expected next state energy after the present transmission is also inserted. Thus

increasing the packet length and unnecessary, which directly drains the battery of both

transmitting and receiving nodes. Also chain leader has to transmit this high data length

packet to the base station which consumes a lot of energy. To overcome the disadvantages

of this cluster head selection in original Pegasis a new cluster head selection criteria is

proposed.

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000100Node Network LifeTime Measurement greedy homo max smooth

number of dead nodes in percentage

num

ber

of r

ound

s

Figure 3.8: Life time of Greedy Homogenous Max Energy Node as cluster head.


3.4.2 Cluster Head selected sequentially

In this case the present chain leader selects the next chain leader by just passing to the

immediate neighbour in the chain. This mechanism greatly eliminates the need for larger

packet size and conserves a lot of energy in both transmission and reception electronics.

The figure 3.7 shows the lifetime of the Wireless sensor network in this scenario.

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000100Node Network LifeTime Measurement greedy hom seq smooth


num

ber

of r

ound

s

Figure 3.9: Life time of Greedy Homogenous WSN, cluster head selected sequentially

3.4.3 Comparison of Max Energy and Sequential cluster Head scenarios

From the above figures it can be concluded that the WSN with cluster head selected

sequentially has more lifetime than the one having the max energy node as cluster head.

10% of nodes are dead at around 5000 rounds in former case and around 7000 rounds in

the latter case, an improvement of nearly 40% of the network lifetime. Similarly for 50% of


dead nodes case it is 6700 for former case and it is 8200 for later case. For 100% dead node

case it is 6700 and 9000 round respectively. This can be summarised in a table as fallows.

GREEDY HOMO Max Energy Sequential % improvement

10% 5000 7000 40

50% 6700 8200 23

100% 6700 9000 34

Table 3.1 Lifetime comparison of Max Energy and Sequential for Homogenous WSN

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


num

ber

of r

ound

s

100comparision of greedy hom max and sequence smooth

greedy homo max lifetime

greedy homo sequence lifetime

Figure 3.10 Lifetime comparison of Greedy max energy and sequential cluster head

Hence selecting the cluster head sequentially greatly enhances the lifetime of the network,

although few nodes which are far away from base station die sooner than others in this

case.


3.5 Heterogeneous PEGASIS with Greedy Chain

In heterogeneous network 80% of the nodes in Wireless Sensor Network are having energy

of 1Joule each and the remaining 20% are having 2Joule of Energy.


In this case since 20% of nodes are having more energy, only these nodes will become

cluster head more often in the beginning of the network, soon they deplete their energy in

transmission to the base station.

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000100Node Network LifeTime Measurement greedy hetero max smooth


num

ber

of r

ound

s

Figure 3.11 Lifetime of Greedy Heterogeneous – Max Energy

Thus from the figure it can be observed that nodes having more energy are alive till the end

of the lifetime of the network.



In this case although some nodes are having higher energy, all nodes become cluster head

equally. So the nodes having high energy tend to stay alive till the end of network lifetime

and also the nodes which are far away from base station die soon. Hence network operation

is not feasible after 50% of nodes die.

0 10 20 30 40 50 60 70 80 90 1000

2000

4000

6000

8000

10000

12000

14000100Node Network LifeTime Measurement greedy hetero seq smooth


num

ber

of r

ound

s

Figure 3.12 lifetime of Greedy Heterogeneous – Sequential cluster head

3.5.3 Comparison of Hetero Max and Sequential scenarios

It can be observed from the above figures that 10% of node die at around 5800 round for

Max Energy where as it is 7300 round for Sequential cluster head selection. 50 % of nodes

die in Max Energy case at around 6400 and it is 8400 for sequential cluster head selection.

Max energy network Is completely down at 7950 rounds whereas it is 14000 for Sequential.


GREEDY HETERO Max Energy Sequential % improvement

10% 5800 7300 25.9

50% 6400 8400 31.25

100% 7950 14000 76.1

Table 3.2 Lifetime comparison of Max Energy and Sequential for Heterogeneous WSN

0 10 20 30 40 50 60 70 80 90 1000

2000

4000

6000

8000

10000

12000

14000


num

ber

of r

ound

s

100comparision of greedy hetero max and sequence smooth

greedy hetero max lifetime

greedy hetero sequence lifetime

Figure 3.13 Lifetime Comparison of Hetero Max and Sequential

3.6 Conclusion:

Hence it can be concluded that for Ant Colony Optimisation for both Homogenous and

Heterogeneous sequential cluster head selection maximises the lifetime of Wireless Sensor

Network lifetime.


4. PEGASIS USING ANT COLONY OPTIMISATION

The chain formation using greedy algorithm does not give minimum distance path since it

only takes the node which is nearer to seed node in formation of chain but it will not

consider the total length of the chain formed. To solve this kind of optimization,

evolutionary algorithms like Ant Colony Optimization are used which has good performance

w.r.t to global optimization of NP hard problems.

Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for

combinatorial optimization problems. The first algorithm which can be classified within this

framework was presented in 1991 [35] and, since then, many diverse variants of the basic

principle have been reported in the literature. The essential trait of ACO algorithms is the

combination of a priori information about the structure of a promising solution with a

posterior information about the structure of previously obtained good solutions.

ACO [34] is a class of algorithms, whose first member, called Ant System, was initially

proposed by Colorni, Dorigo and Maniezzo [34]. The main underlying idea, loosely inspired

by the behavior of real ants, is that of a parallel search over several constructive

computational threads based on local problem data and on a dynamic memory structure

containing information on the quality of previously obtained result. The collective behavior

emerging from the interaction of the different search threads has proved effective in solving

combinatorial optimization (CO) problems.

A combinatorial optimization problem is a problem defined over a set C = c1, ... , cn of basic

components. A subset S of components represents a solution of the problem; F ⊆ 2C is the

subset of feasible solutions, thus a solution S is feasible if and only if S ∈ F. A cost function z

is defined over the solution domain, z : 2C ->R, the objective being to find a minimum cost

feasible solution S*, i.e., to find S*: S*∈ F and z(S*) ≤ z(S), ∀ S∈ F.

Given this, the functioning of an ACO algorithm can be summarized as follows [9]. A set of

computational concurrent and asynchronous agents (a colony of ants) moves through states

of the problem corresponding to partial solutions of the problem to solve. They move by

applying a stochastic local decision policy based on two parameters, called trails and


attractiveness. By moving, each ant incrementally constructs a solution to the problem.

When an ant completes a solution, or during the construction phase, the ant evaluates the

solution and modifies the trail value on the components used in its solution. This

pheromone information will direct the search of the future ants.

Furthermore, an ACO algorithm includes two more mechanisms: trail evaporation and,

optionally, daemon actions. Trail evaporation decreases all trail values over time, in order to

avoid unlimited accumulation of trails over some component. Daemon actions can be used

to implement centralized actions which cannot be performed by single ants, such as the

invocation of a local optimization procedure, or the update of global information to be used

to decide whether to bias the search process from a non-local perspective .

4.1 The Ant Colony Optimisation Metaheuristic Framework

A framework can be defined as the skeleton upon which various objects are integrated for a

given solution. In other words it is a generic structure which is further specialised for a

particular application. ACO is a generic algorithmic structure responsible for the scheduling

of three processes:

1. Ants generation & activity

2. Pheromone trail evaporation

3. Daemon actions

This section defines these processes as well as other data structures required for the

implementation of an ACO algorithm for a specific optimisation problem. A visual

representation of the organisation of these processes is provided as Fig. 4.1

Figure 4.1: Process organisation of the Ant Colony Optimisation Metaheuristic Framework


4.1.1 Pheromone mapping

The pheromone mapping is the means by which solution components are able to be ranked

and selected based on past usefulness. The pheromone mapping connects pheromone

values from a pheromone map (usually a matrix structure) to specific solution components.

The assumption usually being that if a prior solution is good then at least some of its parts

(solution components) should also be good and therefore a remixing of these components

with other good components may lead to an optimal or near-optimal solution. A first step in

defining an ACO algorithm is to define the pheromone mapping.

The problem domain will dictate how the pheromone mapping should be defined. In

applying an ACO algorithm to a combinatorial optimisation problem such as the travelling

salesman problem (TSP) it is not of interest which specific components are included, as any

feasible solution will include every city once (and only once), it is the order of these

components which is important in finding an optimal solution. For the TSP the transition

points (edges/arcs) between the specific components can be assigned a specific pheromone

value in order to reflect which order of cities works the best. That is, that if a solution

included an edge connecting city A to city B and the solution is good then this should be

reflected in the pheromone level on this specific edge and the other edges included in the

solution.

4.1.2 Ants Generation and Activity

This process is responsible for the creation of new candidate solutions to the optimisation

problem being addressed by the algorithm. A temporary population of (artificial) ants is

used to construct feasible solutions to the problem being addressed. Each ant is evaluated

upon the completion of a feasible solution and the solution information encoded into a

global pheromone mapping. Each individual ant is discarded after entering their specific

solution information into the pheromone mapping and a new ‘empty’ ant is created in its

place, until some stopping criterion is met.


An ant has the following properties :

1. An ant searches for a minimum (or maximum) cost solution to the optimisation

problem being addressed.

2. Each ant has a memory used to store all solution components used to date, so that

the candidate solution can be evaluated at the completion of solution construction;

the memory can be used as a tabu list such as in the case of the TSP so that no

component is reused.

3. An ant can be assigned a starting position, for example an initial city in a TSP.

4. An ant can include any feasible solution component (an example of a feasible

solution component in a TSP would be a city which has not already been included in

the candidate solution) until such time that no feasible components exist or a

termination criterion is met (usually correlating to the completion of a candidate

solution).

5. Ants include solution components according to a combination of a pheromone value

and a heuristic value which are associated with every solution component in the

problem, the choice of which solution component is usually a probabilistic one.

6. When including a new solution component in the growing candidate solution the

pheromone value associated with the transition between these components

(arc/edge in a TSP), or the solution component itself can be altered (online step-by-

step pheromone update).

7. An ant can retrace a candidate solution at the completion of a solution, updating the

pheromone values of all transitions and/or solution components used in the solution

(online delayed pheromone update).

8. Once a candidate solution is created, and after completing online delayed

pheromone update (if required) an ant dies, freeing all allocated resources

4.1.3 Pheromone Trail Evaporation

Like the biological ant colony, the artificial ant colony employs a pheromone evaporation

mechanism. This mechanism serves as a useful way of ‘forgetting’ older search bias [35]. As

ACO uses positive reinforcement, if pheromone was allowed to accumulate without decay

the system would very quickly converge on a single solution since this solution would

continue to be reinforced.


4.1.4 Daemon Actions

Daemon actions can be used to perform specialised functions which often require more

knowledge than an individual ant is allowed [35]. For example, a daemon action could

inspect all solutions generated in one search cycle, identify the best solution and increment

the pheromone values of its solution components more than the regular pheromone update

(offline pheromone update). An alternative daemon action could be the application of a

local search procedure.

4.2 ACO Algorithms

4.2.1 Ant System for the Travelling Salesman Problem

In this instance pheromone values correspond to transitions between cities (edges) and are

Uniformly initialised to an amount slightly higher than what is expected to be added in one

iteration of the algorithm as in Eqn. 4.1. After initialisation the AS algorithm runs a

pheromone trail evaporation procedure which is implemented by applying the rule Eqn. 4.5

for every pheromone value. This procedure is followed by ants generation & activity which

is implemented in the following steps:

1. A temporary population of m ants are placed at randomised starting cities.

2. Each ant k applies the random proportional rule Eqn. 4.2 to decide which city to

add to its current tour.

3. Step 2 is repeated until every ant k constructs a complete solution.

4. Every individual solution is evaluated and the edges used in this specific solution

have their pheromone value adjusted according to Eqn. 4.4. This equation allocates a

higher proportion of new pheromone to better solutions in order to ‘reinforce’ good

decisions and is an implementation of an online delayed pheromone update

strategy.

5. The temporary population of ants is discarded.


The pheromone trail evaporation and ants generation & activity procedures are continually

repeated until a termination criterion is reached, such as an amount of computation time, or

alternatively by implementing a daemon action to observe the similarity of the solutions

obtained over several iterations of the algorithm to test the convergence of the algorithm.

(4.1)

(4.2)

(4.3)

(4.4)

(4.5)

: Pheromone value for edge connecting city i & j

M : Number of ants

: Length of path found using a nearest neighbour heuristic

: Probability of ant k selecting the edge connecting city i & j

: Magnitude of pheromone influence on probabilistic decision

: Heuristic value for edge connecting city i & j

: Magnitude of heuristic influence on probabilistic decision

: The distance between city i & j

: Pheromone evaporation rate

Q: Amount of pheromone to deposit


L : Path length

4.2.2 Ant Colony Systems

Ant Colony Systems [37](initially introduced as Ant Q]) differs from AS in three

areas:

1. Introduction of a local pheromone update.

2. Modification of the global pheromone update.

3. Modification of the random proportional rule to become the pseudo-random

proportional rule.

The local pheromone update is applied by all ants during the solution construction phase.

Every ant continually applies the update rule to the last solution component used as in Equ.

3.4.6. The aim of this pheromone update rule is to attempt to diversify the search process as

much as possible during the solution construction phase. Without it most ants will simply

create the same solution which will lead the search into a stagnation behaviour.

(4.6)

The global pheromone update is modified so that only the best-so-far or iteration-best

solution updates the pheromone map at the completion of solution construction. This

means that unless a solution component has been included in the best solution it will not

receive any modification from the global pheromone update.

(4.7)

The value of reflects the utility of the solution and is dependent on the problem e.g.

for the TSP as in Sec. 4.1 it can simply be the inverse of the path length of the solution. The

final and perhaps most important difference between ACS and AS is the modification of the

random proportional rule to become the pseudo-random proportional rule. This rule

introduces a new parameter q0. When a uniformly random value q in the range [0, 1] is less


than q0, the largest transition probability value generated by Equ.3.4.2 is used, rather than

using a roulette wheel selection of all generated probabilities.








The fallowing figure shows the random deployment of WSN nodes in 100m X 100m area

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90


length in meters

leng

th in

met

ers



The fallowing figure shows the chain formed in PEGASIS using Ant Colony optimisation

described earlier. The total length of the best chain is 834m.

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100aco chain formation

20 834.0646

length in meters

leng

th in

met

ers

Figure 4.3: Chain formations in PEGASIS using Ant Colony Optimisation.


The fallowing shows the average length of all the ants vs. the best ant tour length. It can be

easily observed that the chain length falls drastically after very few iterations and becomes

almost constant after initial iterations. This testifies that Ant Colony Optimisation gives very

good performance in NP hard problem optimisation.

0 2 4 6 8 10 12 14 16 18 20800

850

900

950

1000

1050

1100

1150

1200

iterations

length

in m

ete

rs

lenght of the chain

best route length

averaga lenghth

Figure 4.4: The average length vs. Best length of each iteration.


4.4 Homogenous PEGASIS with Ant Colony Optimisation

In homogenous network all the nodes Wireless Sensor Network are having same energy of

1Joule each.









proposed.

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000100Node Network LifeTime Measurement aco homo max smooth


num

ber

of r

ound

s

Figure 4.5: Lifetime of Max Energy PEGASIS using ACO.







0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000100Node Network LifeTime Measurement aco hom seq smooth


num

ber

of r

ound

s

Figure4.6: Lifetime of Sequential PEGASIS using ACO.






dead nodes case it is 6800 for former case and it is 8200 for later case. For 100% dead node

case it is 6800 and 9100 round respectively. This can be summarised in a table as fallows.


ACO HOMO Max Energy Sequential % improvement

10% 5300 7000 32

50% 6800 8200 20.6

100% 6800 9100 33.8


0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


num

ber

of r

ound

s

100comparision of ACO hom max and sequence smooth

ACO homo max lifetime

ACO homo sequence lifetime

Figure 4.7 Lifetime comparison of ACO max energy and sequential cluster head



case.


4.5 Heterogeneous PEGASIS with Ant Colony Optimisation







0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000

9000100Node Network LifeTime Measurement aco hetero max smooth


num

ber

of r

ound

s

Figure 4.8 Lifetime of ACO Heterogeneous – Max Energy









0 10 20 30 40 50 60 70 80 90 1000

2000

4000

6000

8000

10000

12000

14000

16000100Node Network LifeTime Measurement aco hetero seq smooth


num

ber

of r

ound

s

Figure 4.9 Lifetime of ACO Heterogeneous – Sequential cluster head



Max Energy where as it is 7200 round for Sequential cluster head selection. 50 % of nodes


Max energy network Is completely down at 8000 rounds whereas it is 15400 for Sequential.


ACO HETERO Max Energy Sequential % improvement

10% 5100 7200 41.2

50% 6400 8400 31.25

100% 8000 15400 92.5


0 10 20 30 40 50 60 70 80 90 1000

5000

10000

15000


num

ber

of r

ound

s

100comparision of greedy hetero max and sequence smooth

ACO hetero max lifetime

ACO hetero sequence lifetime


4.6 Conclusion:

Hence it can be concluded that for Ant Colony Optimisation for both Homogenous and


Network lifetime.


5. PEGASIS USING GENETIC ALGORITHM

5.1 Introduction

Genetic algorithms are an optimization technique based on natural evolution. They include

the survival of the fittest idea into a search algorithm which provides a method of searching

which does not need to explore every possible solution in the feasible region to obtain a

good result. Genetic algorithms are based on the natural process of evolution. In nature, the

fittest individuals are most likely to survive and mate; therefore the next generation should

be fitter and healthier because they were bred from healthy parents. This same idea is

applied to a problem by first ’guessing’ solutions and then combining the fittest solutions to

create a new generation of solutions which should be better than the previous generation.

We also include a random mutation element to account for the occasional ’mishap’ in

nature

The genetic algorithm process consists of the following steps:

• Encoding

• Evaluation

• Crossover

• Mutation

• Decoding

A suitable encoding is found for the solution to our problem so that each possible solution

has a unique encoding and the encoding is some form of a string. The initial population is

then selected, usually at random though alternative techniques using heuristics have also

been proposed. The fitness of each individual in the population is then computed; that is,

how well the individual fits the problem and whether it is near the optimum compared to

the other individuals in the population. This fitness is used to find the individual’s probability

of crossover. If an individual has a high probability (which indicates that it is significantly

closer to the optimum than the rest of its generation) then it is more likely to be chosen to

crossover. Crossover is where the two individuals are recombined to create new individuals

which are copied into the new generation. Next mutation occurs. Some individuals are


chosen randomly to be mutated and then a mutation point is randomly chosen. The

character in the corresponding position of the string is changed. Once this is done, a new

generation has been formed and the process is repeated until some stopping criteria has

been reached. At this point the individual which is closest to the optimum is decoded and

the process is complete.

Genetic algorithm can be diagrammatically be represented as fallows

Figure5.1 General Scheme of Genetic Algorithm


The pseudo code for Genetic algorithm is presented in the table below.

Table 5.1: The General Pseudo-code for a Genetic Algorithm

5.2 Basic Explanation

Genetic algorithms range from being very straightforward to being quite difficult to

understand. Before proceeding, a basic explanation is required to understand how genetic

algorithms work. We will use the following problem throughout this section. We want to

maximize the function f = −2x2 + 4x − 5 over the integers in the set {0, 1, . . . , 15}. By

calculus or brute force we see that f is maximized when x = 1.

5.2.1 Encoding

The encoding process is often the most difficult aspect of solving a problem using genetic

algorithms. When applying them to a specific problem it is often hard to find an appropriate

representation of the solution that will be easy to use in the crossover process. Remember

that we need to encode many possible solutions to create a population. The traditional way


Procedure genetic algorithm

Begin

Initilize population with randomly generated candidate solutions;

Evaluate each candidate solution;

While (TERMNINATION CONDITION not satisfied) do

Select parents

Crossover pairs of parents to create a offspring

Mutate the offspring

Evaluate the new candidate

Replace the new candidate generating a new population

End while

End;

to represent a solution is with a string of zeros and ones. However genetic algorithms are

not restricted to this encoding. For now we will use a binary string representation.

Consider the problem defined above. Our possible solutions are obviously just numbers, so

our representation is simply the binary form of each number. For instance, the binary

representations of 12 and 7 are 1100 and 0111 respectively. Note that we added a zero to

the beginning of the string 0111 even though it has no real meaning. We did this so that all

the numbers in the set {0, . . . 15} have the same length. These strings are called

chromosomes and each element (or bit) of the string is called a gene.

We now randomly generate many chromosomes and together they are called the

population.

5.2.2 Evaluation

The evaluation function plays an important role in genetic algorithms. We use the

evaluation function to decide how ’good’ a chromosome is. The evaluation function usually

comes straight from the problem. In our case the evaluation function would simply be the

function f = −2x2 + 4x − 5, and because we are trying to maximize the function, the larger

the value for f, the better. So, in our case, we would evaluate the function with the two

values 7 and 12.

f (7) = −71

f (12) = −241

Obviously 7 is a better solution than 12, and would therefore have a higher fitness. This

fitness is then used to decide the probability that a particular chromosome would be chosen

to contribute to the next generation. We would normalize the scores that we found and

then create a cumulative probability distribution. This is then used in the crossover process.

The stopping criteria are used in the evaluation process to determine whether or not the

current generation and the best solution found so far are close to the global optimum.


Various stopping criteria can be used, and usually more than one is employed to account for

different possibilities during the running of the program: the optimal solution is found, the

optimal solution is not found, a local optimum is found, etc. The standard stopping criteria

that is used stops the procedure after a given number of iterations. This is so that if we do

not find a local optimum or a global optimum and do not converge to any one point, the

procedure will still stop at some given time. Another stopping criterion, is to stop after the

“best” solution has not changed over a specified number of iterations. This will usually

happen when we have found an optimum - either local or global - or a point near the

optimum. Another stopping criteria is when the average fitness of the generation is the

same or close to the fitness of the ’best’ solution.

5.2.3 Crossover

Crossover can be a fairly straightforward procedure. In our example, which uses the simplest

case of crossover, we randomly choose two chromosomes to crossover, randomly pick a

crossover point, and then switch all genes after that point. For example, using our

chromosomes

V1 = 0111

v2 = 1100

we could randomly choose the crossover point after the second gene

V1 = 01 | 11

V2 = 11 | 00

Switching the genes after the crossover point would give

V1’ = 0100 = 4

V2’ = 1111 = 15


We now have two new chromosomes which would be moved into the next population,

called the next generation.

Not every chromosome is used in crossover. The evaluation function gives each

chromosome a ’score’ which is used to decide that chromosome’s probability of crossover.

The chromosomes are chosen to crossover randomly and the chromosomes with the

highest scores are more likely to be chosen. We use the cumulative distribution created in

the evaluation stage to choose the chromosomes. We generate a random number between

zero and one and then choose which chromosome this corresponds to in our distribution.

We do this again to get a pair, then the crossover is performed and both new chromosomes

are moved into the new generation. This will hopefully mean that the next generation will

be better than the last - because only the best chromosomes from the previous generation

were used to create this generation. Crossover continues until the new generation is full.

It is possible to check each new chromosome to make sure it does not already exist in the

new generation. This means that we will get a variety of possible solutions in each

generation, but also that once we have found the optimal solution in one chromosome, the

other chromosomes will probably not be optimal. That means that the average fitness of the

generation can never be as good as the fitness of the optimal chromosome, which could

make deciding when to stop difficult.

It is also possible to move the best solution from the previous generation directly into the

new generation. This means that the best solution can never get any worse since even if on

average the generation is worse, it will still include the best solution so far.

We can also have two point crossovers. In this case we randomly choose two crossover

points and switch the genes between the two points. In our problem we could pick the

points after the first gene and after the third gene.

V1 = 0 | 11 | 1

V2 = 1 | 10 | 0


to get

V1’’ = 0101 = 5

V2’’ = 1110 = 14

There are many different crossover routines. We often need to change the crossover routine

to make sure that we do not finish with an illegal chromosome - that is, an infeasible

solution. In this way, crossover is very problem specific.

5.2.4 Mutation

Mutation is used so that we do not get trapped in a local optimum. Due to the randomness

of the process we will occasionally have chromosomes near a local optimum but none near

the global optimum. Therefore the chromosomes near the local optimum will be chosen to

crossover because they will have the better fitness and there will be very little chance of

finding the global optimum. So mutation is a completely random way of getting to possible

solutions that would otherwise not be found.

Mutation is performed after crossover by randomly choosing a chromosome in the new

generation to mutate. We then randomly choose a point to mutate and switch that point.

For instance, in our example we had

V1= 0111

If we chose the mutation point to be gene three, v1 would become

V1’=0101

We simply changed the 1 in position three to a 0. If there had been a 0 in position three

then we would have changed it to a 1. This is extremely easy in our example but we do not

always use a string of zeros and ones as our chromosome. Like crossover, mutation is

designed specifically for the problem that it is being used on.


Inversion is a different form of mutation. It is sometimes used in appropriate cases later.

Here inversion operator on our basic example.

The inversion operator consists of randomly choosing two inversion points in the string and

then inverting the bits between the two points. For example

V2 = 1100

We could choose the two points after gene one and after gene three.

V2 = 1 | 10 | 0

Now, since there are only two genes between our inversion points, we then switch these

two genes to give

V2’=1010

If we had a larger chromosome, say

V3 = 110100101001111

we could choose the inversion points after the third point and after the eleventh point.

V3 = 110 | 10010100 | 1111

Now, we start at the ends of the ’cut’ region and switch the genes at either end moving in.

So we get

V3’ = 110001010011111

Essentially we are just reversing (or inverting) the order of the genes in between the two

chosen points.









The fallowing figure shows the random deployment of WSN nodes in 100m X 100m area

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90


length in meters

leng

th in

met

ers


The fallowing figure shows the chain formed in PEGASIS using Ant Colony optimisation

described earlier. The total length of the best chain is 3175 meters.


0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100gentic algorithm chain formation

200 3175.5676

length in meters

leng

th in

met

ers

Figure 5.3: Chain formations in PEGASIS Genetic Algorithm.

The fallowing figure shows the average length of all the ants vs. the best ant tour length. It

can be easily observed that the chain length falls drastically after very few iterations and

becomes almost constant after initial iterations.


0 20 40 60 80 100 120 140 160 180 2002800

3000

3200

3400

3600

3800

4000

4200

4400

4600

4800

iterations

leng

th in

met

ers

lenght of the chain

best route length

average lenghh in the iteration

Figure 5.4: Average length vs. Best length of each iteration.

It can be observed from the figure the length of the chain decreases slowly and there is no

guarantee for the convergence to optimum solution.


5.4 Homogenous PEGASIS with Ant Colony Optimisation

In homogenous network all the nodes Wireless Sensor Network are having same energy of 1

Joule each.









proposed.

0 10 20 30 40 50 60 70 80 90 1000

500

1000

1500

2000

2500

3000

3500

4000100Node Network LifeTime Measurement gen hom max smooth


num

ber

of r

ound

s

Figure5.5: Lifetime of Max Energy PEGASIS using Genetic Algorithm.







0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000100Node Network LifeTime Measurement gen hom seq smooth


num

ber

of r

ound

s

Figure 5.6: Lifetime of Sequential PEGASIS using Genetic Algorithm.






ead nodes case it is 3100 for former case and it is 4300 for later case. For 100% dead

nodecase it is 3850 and 7000 round respectively. This can be summarised in a table as

fallows.


GENETIC HOMO Max Energy Sequential % improvement

10% 1700 2500 47

50% 3100 4300 20.6

100% 3850 7000 81.8


0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000


num

ber

of r

ound

s

100comparision of GENETIC hom max and sequence smooth

GENETIC homo max lifetime

GENETIC homo sequence lifetime

Figure 5.7 Lifetime comparison of GENETIC Max Energy and Sequential cluster head



case.


5.5 Heterogeneous PEGASIS with Genetic Algorithm







0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000100Node Network LifeTime Measurement gen hetero max smooth


num

ber

of r

ound

s

Figure 5.8 Lifetime of GENETIC Heterogeneous – Max Energy









0 10 20 30 40 50 60 70 80 90 1000

2000

4000

6000

8000

10000

12000100Node Network LifeTime Measurement GENETIC hetero seq smooth


num

ber

of r

ound

s

Figure 5.9 Lifetime of GENETIC Heterogeneous – Sequential cluster head



Max Energy where as it is 2900 round for Sequential cluster head selection. 50% of nodes


Max energy network is completely down at 7800 rounds whereas it is 10300 for Sequential.


Genetic HETERO Max Energy Sequential % improvement

10% 2500 2900 16

50% 5200 5600 7.7

100% 7800 10300 32


0 10 20 30 40 50 60 70 80 90 1000

2000

4000

6000

8000

10000

12000


num

ber

of

rounds

100comparision of GENETIC hetero max and sequence smooth

GENETIC hetero max lifetime

GENETIC hetero sequence lifetime


5.6 Conclusion:

Hence it can be concluded that for Genetic Algorithm for both Homogenous and


Network lifetime. In Genetic there is not much increase in improvement, since the length of

the chain is very high, so the distance between the node is playing dominant role rather

than the packet data size. But Genetic algorithm is very simple to understand and to

formulate the process.


6. COMAPRATIVE STUDY OF GREEDY, ACO AND GENETIC ALGORITHMS

The chain formed using the Wireless Sensor Node is crucial in deciding the lifetime of the PEGASIS protocol. The length of the chain formed using Greedy, Ant Colony Optimisation and Genetic algorithm is compared in the fallowing figure.

0 20 40 60 80 100 120 140 160 180 200500

1000

1500

2000

2500

3000

3500

4000

4500

iteration number

leng

th in

met

ers

100comparision of chain length

GREEDY

Ant Colony OptimisationGENETIC ALGORIYHM

Figure 6.1: Chain length comparison of GREEDY, ACO and GENETIC Algorithm.

From the figure it can be seen that the chain formed using Ant Colony Optimisation is less compared to others.


6.1 Lifetime Comparison of Homogenous WSN

Lifetime comparison of both MAX Energy and Sequential cluster head selection for GREEDY, ACO and GENETIC Algorithms is done.

6.1.1 Lifetime Comparison of MAX Energy

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000


num

ber

of r

ound

s

100comparision of HOMO max lifetime

GREEDY HOMO MAX lifetime

ACO HOMO MAX lifetimeGENETIC HOMO max

Figure 6.2 Lifetime Comparison of MAX Energy

From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any dies.


6.1.2 Lifetime Comparison of Sequential Energy

0 10 20 30 40 50 60 70 80 90 1000

2000

4000

6000

8000

10000

12000


num

ber

of r

ound

s

100comparision of HOMO SEQUENTIAL lifetime

GREEDY HOMO SEQUENTIAL lifetime

ACO HOMO SEQUENTIAL lifetimeGENETIC HOMO SEQUENTIAL

Figure 6.3 Lifetime comparison of Sequential

From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any node dies.


6.2 Lifetime Comparison of Heterogeneous WSN

Lifetime comparison of both MAX Energy and Sequential cluster head selection for GREEDY, ACO and GENETIC Algorithms is done.

6.2.1 Lifetime Comparison of MAX Energy

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

7000

8000

9000


num

ber

of r

ound

s

100comparision of hetero max lifetime

GREEDY hetero MAX lifetime

ACO hetero MAX lifetimeGENETIC hetero max

Figure 6.4 Lifetime Comparison of MAX Energy

From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any dies


6.2.2 Lifetime Comparison of Sequential Energy

0 10 20 30 40 50 60 70 80 90 1000

5000

10000

15000


num

ber

of r

ound

s

100comparision of HETERO Sequentail lifetime

GREEDY HETERO SEQUENTIAL lifetime

ACO HETERO SEQUENTIAL lifetimeGENETIC HETERO SEQUENTIAL

Figure 6.3 Lifetime comparison of Sequential

From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any dies

6.3 Conclusion

Chain formed by Ant Colony Optimisation is of least length compared to Greedy and Genetic Algorithm, because of good global optimisation characteristics of the Ant Colony Optimisation. The rate of convergence of Ant Colony optimisation is also very fast compared to other algorithms. The chain is optimised to a great extent within little iteration itself.

The WSN Lifetime is high in case of Sequential cluster head selection since the length of the data packet is less compared to Max Energy cluster head selection criteria.


7. CONCLUSION

7.1 Introduction

The research carried out for this thesis, investigates energy efficient routing algorithms

related to WSNs. A new cluster head selection criteria and maintenance of priority queue at

each node is proposed. This increases the life of WSNs. Ant Colony Optimisation and Genetic

Algorithms are used in making the chain of PEGASIS. Lifetime of WSN under various

scenarios has been investigated. These chapter summaries the work reported in this thesis,

specifying the limitations of the study and provides some suggestions to future work.

Following this introduction, section 7.2 lists the achievements of the research work. Section

7.3 presents some of the future research area that can be extended to this thesis.

7.2 Contribution of Thesis

The first chapter of the thesis introduced to Wireless Sensor Networks, literature survey and

its architecture. It also provides a brief overview of the thesis. The second chapter discussed

routing algorithms in Wireless Sensor Networks. It presented the literature survey of

PEGASIS routing protocol and of Evolutionary algorithms. The chapter 3 described the

PEGASIS protocol and chain formation using GREEDY algorithm. It also gave radio power

model of WSN and various simulation parameters. The chapter 4 described Ant Colony

Optimisation for minimisation of chain length in PEGASIS, thus contributing to the lifetime of

the WSN. The chapter 5 described Genetic Algorithm. The results of studies have been

presented. The chapter 6 presented the comparative study of GREEDY, ACO and Genetic

algorithms. The chain length and Lifetime of PEGSIS using GREEDY, ACO and Genetic

Algorithm has been compared.

The first contribution of the thesis related to use of sequential cluster head for PEGASIS by

eliminating the overhead, enhances the lifetime of the WSNs. Instead of sending the Energy

status of all the nodes to base station, the next cluster head is selected by the present chain

leader, thus contributing to higher lifetime of Wireless Sensor Network by reduced packet


size. To validate the algorithm Simulations had been carried out using MATLAB. Simulation

results showed better performance of Sequential as compared to MAX energy in terms of

performance metrics like number of alive nodes and total WSN lifetime.

ACO provides better lifetime for nodes compared to other models. It is also seen that ACO is

able to provide high percentage of nodes live for maximum duration. The chain length

formed ACO is least and converges to the optimum in very little iteration. Hence, it suits

most of application of WSNs which require constant monitoring and sending sensed data

packets to a sink at regular intervals of time.

7.3 Future Directions

To conclude the thesis, the following are some suggestions for the future work which can be

done. In this thesis, ACO and Genetic algorithms have been used. Other bio-inspired

algorithms like Stimulated Annealing, Bacterial Forage optimization, artificial Immune

system (significant time and power consuming) can also be compared to ACO and Genetic

algorithm, but the challenge of reducing computational complexity still remains.

Comparable study of computational complexity of different algorithm need to be analysed.

Secondly, security parameter has not been evaluated in this thesis. So, new security based routing

protocols for Wireless Sensor Networks and its validation can be a field of study. Further, the

proposed protocols have to be dumped into WSN nodes and can be tested in a real time

application.


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edge connecting

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onlythesenodeswillbecome