Research Article Intercluster Ant Colony …downloads.hindawi.com/journals/ijdsn/2014/457402.pdfResearch Article Intercluster Ant Colony Optimization Algorithm for Wireless Sensor

Research ArticleIntercluster Ant Colony Optimization Algorithm for WirelessSensor Network in Dense Environment

Jung-Yoon Kim,1 Tripti Sharma,2 Brijesh Kumar,3 G. S. Tomar,4,5

Karan Berry,2 and Won-Hyung Lee1

1 Department of Image Engineering, Chung-Ang University, ChungAng Cultural Arts Center, Office No. 503, Dongjak-gu,Seoul 156-756, Republic of Korea

2Department of Information Technology, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi 110058, India3 Department of Information Technology, Lingaya’s University, Faridabad, Haryana 121002, India4Machine Intelligence Research Labs, 223 New Jiwaji Nagar, Gwalior 474011, India5 Department of Electrical and Computer Engg, University of West Indies, St. Augustine 1000, Trinidad and Tobago

Correspondence should be addressed to Won-Hyung Lee; [email protected]

Received 27 December 2013; Accepted 22 February 2014; Published 2 April 2014

Academic Editor: Tai-hoon Kim

Copyright © 2014 Jung-Yoon Kim et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wireless sensor networks have grown rapidly with the innovation in Information Technology. Sensor nodes are distributed anddeployed over the area for gathering requisite information. Sensor nodes possess a negative characteristic of limited energy whichpulls back the network from exploiting its peak capabilities. Hence, it is necessary to gather and transfer the information in anoptimized way which reduces the energy dissipation. Ant Colony Optimization (ACO) is being widely used in optimizing thenetwork routing protocols. Ant Based Routing can play a significant role in the enhancement of network life time. In this paper,Intercluster Ant Colony Optimization algorithm (IC-ACO) has been proposed that relies upon ACO algorithm for routing of datapackets in the network and an attempt has been made to minimize the efforts wasted in transferring the redundant data sent bythe sensors which lie in the close proximity of each other in a densely deployed network. The IC-ACO algorithm was studiedby simulation for various network scenarios. The results depict the lead of IC-ACO as compared to LEACH protocol by indicatinghigher energy efficiency, prolonged network lifetime, enhanced stability period, and the elevated amount of data packets in a denselydeployed wireless sensor network.

1. Introduction

AWSN (wireless sensor network) consists of 100–1000 smallbattery operated, self-organizing, localized decision making,self-coordinating wireless sensors spread throughout the area[1]. These sensor nodes have various negative characteristicssuch as limited energy, limited analytical and computa-tional ability, and limited memory storage. These devicesalso have low data rate as well as short range for wirelessradio transmission. These sensor nodes are composed ofmotes or sensors, transceiver, battery, and the processorfor performing local processing. The processor converts theanalog information which is sensed by the sensors abouttheir environment in which they are deployed in to digitalformat [2]. These wireless sensors can also perform simple

calculations and communicate locally over a small area.Theyhave multiple applications in detecting volcanic eruptions,military, monitoring medical condition of patients, vehicletracking, and so on. But typically, they are designed to collectand report data to the base stations [2]. AlthoughWSNs havevarious applications in different domains, they have severallimitations such as limited energy and limited computationand communication abilities as already discussed [1]. Insensor networks, minimization of energy consumption isconsidered to be a major performance criterion, in order toprovide maximum network lifetime.

ACO (Ant Colony Optimization). Routing is categorized to bea combinatorial optimization problem in finding the shortestpath from source to the destination [3]. From millions of

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 457402, 10 pageshttp://dx.doi.org/10.1155/2014/457402

2 International Journal of Distributed Sensor Networks

years of survival, ants use stigmergy in successfully detectingthe shortest path between the nest and the food sources [4].Ant ColonyOptimization (ACO), a Swarm Intelligence basedoptimization technique, is inspired from the ant behavior. Anant secretes a volatile chemical substance called pheromonewhich helps to converge over the shortest path amongmultiple paths. While moving, ants secrete pheromone overthe ground and follow the path with maximum pheromoneconcentration. This mechanism has been proved to be anoptimum way to mark paths which guide other ants andgenerate optimum paths from the overall behavior of theant colony [5]. This behavior of ants has been successfullymapped in electronic devices for solving various combinato-rial problems. It includes asynchronous agents or ants thatproduce partial solutions of the problem while traversingthrough different phases of the problem. While traversing,these agents follow a greedy local decision policy which relyover two parameters, namely, attractiveness and trail infor-mation. Each ant while traversing through different phasesof the problem incrementally produces partial solution to theproblem. Search of the future ants are directed by the trailvalue which is updated by the ants which earlier traversedthrough the same path [6].

In this paper an approach Intercluster Ant ColonyOptimization algorithm (IC-ACO) has been proposed thatbacks upon ACO algorithm for routing of data packetsin wireless sensor networks. Performance of IC-ACO issignificantly improved as compared to LEACH protocol interms of higher energy efficiency, prolonged network lifetime,enhanced stability period, and the higher amount of datapackets transmitted to base station in a densely deployedwireless sensor network.

In Section 2 we have mentioned various research andalgorithms related to ant colony optimization. Section 3describes the radio model used for the proposed algo-rithm. Section 4 describes the proposed algorithm IC-ACO(Intercluster Ant Colony Optimization). Further Section 5portrays the simulation results. Finally Section 6 concludesthe research and states the future work that can be carriedout over the proposed algorithm.

2. Related Work

ACO (Ant Colony Optimization). Asynchronous agents orants are constantly released from different nodes to producepartial solution to the problem while traversing throughdifferent phases of the problem. While traversing, theseagents follow a greedy local decision policy which rely overtwo parameters, namely, attractiveness and trail information[7]. Each ant while traversing through different phases ofthe problem incrementally produces a partial solution to theproblem. Search of the future ants are directed by the trailvalue which is updated by the ants which earlier traversedthrough the same path [8]. Furthermore, an ACO algorithminvolves two mechanisms which enhance the capabilitiesof the algorithm which are trail evaporation and daemonactions. Trail evaporation decreases the trail values over timeto abstain unlimited accumulation of trail over a specific

component [6]. Daemon actions are used to implement thecentralized actions or the actions which cannot be performedby a single ant, such as update of global information. AsEnergy is proved to be a major shortcoming in WSN, ACOprovides us a minimum cost path in terms of energy.

2.1. Basic Ant Based Routing (BABR). The ACO has beensuccessfully applied to various combinatorial optimisationproblems [9]. The ant based approach has been successfullyapplied in wireless sensor network. A basic based algorithmcan be described as follows.

(1) A forward ant is launched at regular intervals fromeach network node with an aim to find the optimumpath from the node to the destination at regularintervals.

(2) Each forward ant tries to position the destinationwithequal probability by using neighboring nodes withminimum cost joining its source and sink.

(3) Each agentmoves step-by-step towards its destinationnode. At each intermediate node a greedy stochasticpolicy is applied to choose the next node to move to.

(4) During the movement, the agents collect informationabout the time length, the congestion status, and thenode identifiers of the followed path.

(5) Once destination is reached, a backward ant is createdwhich takes the same path as the forward ant, but inan opposite direction.

(6) During this backward travel, local models of thenetwork status and the local routing table of eachvisited node are modified by the agents as a functionof the path they followed and of its goodness.

(7) Once they have returned to their source node, theagents die.

2.2. LEACH Protocol. LEACH (Low-Energy Adaptive Clus-tering Hierarchy) employs the technique of random rotationof job of a cluster head among all the sensor nodes deployedin the network. The operation of LEACH is organized inrounds where each round consists of a setup phase anda transmission phase [10]. In the setup phase, the nodesclassify themselves into clusters with one node selected asthe cluster head in each cluster as shown in Figure 1. Duringthe transmission phase, cluster heads collect data from thenodes within their respective clusters and they transfer theprocessed information to the base station. LEACH providessignificant energy savings and prolonged network lifetimeover fixed clustering and other conventional algorithms [10].Although LEACH protocol has been proved to provide goodperformance, it suffers from many drawbacks too, suchas random selection of CH and not considering energyconsumption, not suitable for large area, not suitable fordensely deployed network nonuniform distribution of CHs.

2.3. Sensor Driven and Cost-Aware Ant Routing (SC). SC [11]is based on the assumption that ants having sensors can smell

International Journal of Distributed Sensor Networks 3

X

Figure 1: Cluster formation in LEACH.

the food and sense the most appropriate direction that theant would precede initially. Also the estimated cost of path todestination from each neighboring node and their respectiveprobability distribution is stored by eachnode.This algorithmmight produce misleading solutions due to obstacle or loss ofvisibility of the node, which proves to be biggest disadvantageof this algorithm.

2.4. Flooded Forward Ant Routing (FF). FF [11] is based onflooding of agents (ants) from the source node to the sinkand strongly contends that even the ants equipped withsensors might get mislead due to loss of visibility of nodesor obstacles. There exists a situation when the destinationis not known to the ants or the cost of the path cannot beestimated. In this situation a problem of wandering aroundthe network to find the destination has occurred [5]. FF solvesthis problem by employing the broadcast method for routingthe data packets overwireless sensor network. For this FF usesflooding of forward ants to the destination.

2.5. Flooded Piggyback Ant Routing (FP). FP [11] defines anew type of ant called data ant which carries the forward list,whereas basic functioning of the forward ant is same as it wasin FF [5]. FPminimizes the energy dissipation of the networkwhile sending data packets to the destination by coupling dataant carrying the forward list to the forward flooding ant. Dataants perform the dual task of forwarding the data along withstoring the node identity which come across the path to thedestination which can be later used by the backward ants.

2.6. Energy Efficient Ant Based Routing (EEABR). EEABR[12] is considered to be an improvised version of ant basedrouting. This algorithm considers the energy level alongwith distance while selecting the node over the path to betraversed. In the basic ant algorithm, the routing table of thenodes stored the identity of each neighboring node and theircorresponding pheromone information,which require a largeamount ofmemory in order to store this information. EEABRfixes this problem, by storing the information about onlythe last two nodes which significantly reduces the memoryrequirement [12]. This algorithm possesses a drawback ofdelay in packet delivery.

2.7. ACO-Based Quality-of-Service Routing (ACO-QoSR).ACO-QoSR deals with the problem of limited energy and thedelay requirements of the data packets [13]. This algorithmuses a threshold bounding parameters for both end to enddelay and the energy. Whenever a sensor node has its datato be sent to the sink, it checks its routing table and if nosuch path exists, then the procedure for finding a new pathis initiated [5]. For finding these new paths, forward antsare deployed which selects the next hop to traverse using aprobabilistic approach.

This algorithm stores the heuristic information in theform of a ration of residual energy and the summationof residual energy of all the nodes. 𝜂

𝑖𝑗is the heuristic

information which is defined as

𝑛𝑖𝑗=𝐸residual (𝑗)

∑𝑘∈𝑁𝑖(𝑘)𝐸residual (𝑘)

. (1)

ACO-QoSR [13] embeds one of the features of Max-Min AntSystem [14]. ACO-QoSR [13] on comparisonwith AODV [15]and SDV [16] proves to be better on the grounds of higherpacket delivery and time constraint applications.

2.8. Self-Organizing Data Gathering for Multisink SensorNetworks (SDG). This protocol proposes an algorithm inwhich only backward ants are produced and that too bythe sink. These ants update the pheromone information ofeach node using a pheromone value. When the backward antreaches a node, then that node stores the pheromone value,generating sink ID and the neighboring nodes.The algorithmwas able to achieve the reliability of 90% even in the presenceof lossy channels. The main disadvantage of this algorithm isthat significant amount of energy is wasted due to the packetexchange by hello ants and the proactive nature.

2.9. Many-to-One Improved Ant Routing (MO-IAR).MO-IAR, a two-phase algorithm, was established byGhasemaghaei et al. [17]. The first phase establishes theshortest path and the second phase provides the procedurefor actual data routing. For minimizing the packet loss,proactive congestion control mechanism is adopted by thesecond phase. It is presumed that the neighboring nodes aswell as the destination are already known to the sensor.

2.10. AntChain. AntChain, an efficient algorithm, is pro-posed by Ding and Xiaoping Liu, with an objective of energyoptimization and minimizing the delay [5]. In AntChain,identities of the nodes along with their respective locationsare known in advance. This information is used for efficienttransmission of data. AntChain is proved to be a betteralgorithm in comparison to its counter parts LEACH [10] andPEGASIS [18] on the grounds of energy efficiency.

Table 1 shows the comparison of various routing pro-tocols discussed above based on energy efficiency, dataaggregation, location awareness, route selection, and eitherbeing query based or not [5].


Transmitelectronics

TX amplifier Receiveelectronics

ETX (d) ERX

d

k bit message k bit message

∈amp ∗k∗d𝜆Eelec ∗k Eelec ∗k

Figure 2: Heinzelman first order radio model used for simulation.

Table 1: Comparison of routing protocols in WSNs.

Routing protocol Energy efficiency Data aggregation Location awareness Route selection Query basedBABR Weak No No Proactive NoLEACH Strong Yes No Proactive NoSC Strong No No Hybrid NoFF Weak No No Hybrid NoFP Weak No No Hybrid NoEEABR Very strong No No Proactive NoACO-QoSR Strong No No Reactive NoSDG Very strong Yes No Proactive NoMO-IAR Moderate No No Proactive NoAntChain Strong Yes Yes Reactive Yes

3. Radio Model

There has been a considerable amount of research in thefield of radio and electronics in the last decade. In theproposed approach simple first order radio model proposedby Heinzelman et al. has been used, because it suits ourpurpose for thematter presented and is easier to simulate [10].The model consists of transmitting and receiving electronicsand a transmitting amplifier as shown in Figure 2.

Using the model described above, we find that to achievea suitable SNR for transmission, the energy expended by thesystem is represented mathematically as

𝐸TX (𝑘, 𝑑) = 𝐸elec ∗ 𝑘 + ∈amp ∗ 𝑘 ∗ 𝑑2 if 𝑑 < 𝑑

0

= 𝐸elec ∗ 𝑘 + ∈amp ∗ 𝑘 ∗ 𝑑4 if 𝑑 ≥ 𝑑

0,

𝐸RX (𝑘) = 𝐸elec ∗ 𝑘,

(2)

where 𝐸TX(𝑘, 𝑑) is the energy dissipated per bit to run thetransmitter circuit, 𝐸RX(𝑘) is the energy expended per bit torun the receiver circuit, 𝑘 is the number of bits in themessage,∈amp is a constant dependent on the transmitter electronics,and 𝑑 is the distance of the node from the base station.

The free space model and the multipath fading channelmodel are used in the construction of the radio model [10].When the distance between the transmitter and receiver isless than threshold value 𝑑

0, the algorithm adopts the free

spacemodel (𝑑2 power loss). Otherwise, the algorithmadoptsthe multipath fading channel model (𝑑4 power loss).

4. IC-ACO (Intercluster Ant ColonyOptimization Algorithm)

ACOalgorithms have been devoted in solving various combi-natorial and optimization problems effectively and efficiently[4].Whenever awireless sensor network protocol is designed,it is important to consider the energy efficiency and networklifetime of the underlying algorithm since these are thelimitations of WSN. In this section proposed algorithm IC-ACO has been discussed, which is based on the Ant ColonyOptimization heuristic and focuses on the primary WSNconstraints. In IC-ACO, Ant Colony Optimization is appliedwithin the cluster to transmit the data packets from the sourcenode to the sink in densely deployed network. An effort hasbeen made to minimize the redundant data transmission.

Section 4.1 summarizes the concept of basic ant basedrouting protocol for wireless sensor network. Section 4.2describes the concept of proposed approach IC-ACO (Inter-cluster Ant Colony Optimization).

4.1. Basic Ant Based Routing for Wireless Sensor Network.Agents which are considered as ants are deployed by thesource sensor node and these ants iteratively traverse throughdifferent phases of the solution and produce the partialsolution to the problem. For traversing through these phases,these ants use the probabilistic approach for selection ofparticular route which in turn depends on the heuristic andthe pheromone information (goodness of path). This processis repeated until the termination condition is achieved, thatis, when whole network system die out. The flowchart for thegeneralized algorithm for the ACO approach is as shown inFigure 3.


Start

End

Initialization of parameters

Preparation of partial solution

Update pheromone value

until all nodes are dead

Final solutionIteration ≤

Figure 3: Flowchart of ACO (Ant Colony Optimization).

End

Start

Initialization of parameters

Election of cluster head

Minimization of redundancy

all nodes are dead

Apply IC-ACO to route date packets

Iteration ≤ until

Figure 4: Flowchart of proposed algorithm (Intercluster ACO).

4.2. IC-ACO (Intercluster Ant ColonyOptimizationAlgorithm)for Wireless Sensor Network. The flowchart of the proposedapproach IC-ACO is shown in Figure 4. In this approach,LEACH algorithm is used as the basis for the randomizedselection of cluster heads. The proposed approach works intwo phases. In the first phase, the cluster heads have beenselected and nodes classify themselves into clusters. In thesecond phase, minimization of redundant data transmissionand routing of data based on ant colony optimization are per-formed with in the cluster. The following steps are repeateduntil all the nodes are dead in the sensor network.

(1) Selection of Cluster Heads. Clusters are built aroundthe cluster heads which are randomly selected amongthe live sensor nodes. The selection procedure ofcluster heads in this work is similar to LEACHprotocol.

(2) Minimization of Redundancy. The nodes which lie inclose proximity of each other are highly probable tosend the redundant information; in order to save theenergywasted in sending this redundant information,a radius has been chosen experimentally. Five ischosen as the optimum radius in the experimentalsetup of (100∗100) network. Out of the nodes whichlie within the chosen radius, the nodewith the highestenergy is selected for the transmission of data to thecluster head, provided the selected node is closer tocluster head than the base station. Remaining nodeswithin the radius will be in sleep mode in currentround and they will not participate in transmission.If the selected node is closer to the base station,then it will not send data to the cluster head butit will participate with remaining nodes which donot lie within the radius for routing the data. Theinformation received and processed by the clusterhead is then transmitted to base station.

(3) Apply IC-ACO to Route the Data Packets within theCluster. Within the cluster, the nodes which are nei-ther in sleepmode nor selected for the transmission ofsensed information to the base station will then initi-ate the routing task by transferring the sensed data tothe different neighbor nodes. Each node then choosesits neighbor node. Optimum paths towards the basestation are formed by the agents (ants) to achieve theefficient routing. This routing algorithm follows theprobabilistic approach for constructing the solution,that is, selection of suitable path for transmissiontowards the base station. In this routing approach,the probabilistic selection is based on pheromone andheuristic information, which is updated continuously.While performing this operation of routing basedon these two pieces of information, ants choosetheir optimal path towards the base station. Theconstructing solution for this operation is explainedin Section 4.3.

4.3. Constructing Solution for IC-ACO (Intercluster Ant ColonyOptimization). AnACO algorithm is an artificial intelligencetechnique based on the pheromone-laying behavior of ants[7]. In AS (ant system), ants start from a source node andmove through neighbor nodes and reach a final destinationnode (base station), when the data packets need to be sent bythe source nodes launching of ant will be performed. Afterlaunching of ant, probability of each adjacent node for theselection of next hop is calculated using pheromone value ofthe path between nodes and the heuristic value of these nodes[19]. Each ant tries to find the optimum path on the basisof heuristic value that results in minimum cost in terms ofenergy dissipation. In IC-ACO, only the nodes that lie outsidethe radius participate in sending information directly to basestation. Equation (3) given below calculates the probabilitythat is used for calculation of optimum path between node𝑖 and node 𝑗. In the proposed approach, the ants check the


(1) Initialize all the parameters.(2) Repeat Steps 3 to 13 while the network is alive.(3) Repeat Step 3 for each node in the system.(4) If (Node.energy < 0)

Set Node.status = deadElseSet Node.status = alivereturn

(5) Randomly select cluster head nodes with Node.Status = alive and repeat Step 6 for each selected node.(6) Set CH[𝑖] = node // 𝑖 is the number of cluster heads (CHs) elected.(7) Make clusters around these selected cluster head nodes by assigning nodes to a cluster with minimum distance.(8) Repeat Steps 8 to 12 for each cluster.(9) Repeat Steps 9 to 12 for each node lying within the cluster.(10) If (distance of the node from other nodes < radius)(11) If (distance of node to base station > distance of node to cluster head)

(Node[𝑖].flag = 0) /∗transmission nodeSend the data packet from node to cluster head.elseSet (Node[𝑚 − 1].flag = 1) //𝑚 − 1 nodes in sleep mode, total𝑚 nodes lies within the radius.

(12) If (distance of the node from other node > radius)Apply ACO in routing the data packet to the base station from the nodes.

(13) Send the data packet received by the cluster head to the base station.(14) return

Algorithm 1

probability of their adjacent node for being selected as nexthop for data transmission to sink;

𝑃𝑖𝑗=(𝜏𝑖𝑗)𝛼

(𝜂𝑗)𝛽

∑𝑗∈𝑁(𝜏𝑖𝑗)𝛼

(𝜂𝑗)𝛽, (3)

where𝑁 represents the adjacent nodes, which the node 𝑖 canselect as the next jump.𝜏𝑖𝑗is the pheromone value between the source node and

the adjacent node. It is given as

𝜏𝑖𝑗=1

𝑑𝑖𝑗

, (4)

where 𝑑𝑖𝑗is the distance between source node 𝑖 and the base

station.𝜂𝑗is the heuristic value that represents the energy of

nodes and it is calculated on the basis of the residual energyof the node. This heuristic value helps in decision makingaccording to the energy levels of neighbor nodes, implyingthat if a node has a lower energy, then it has lower probabilityto be chosen. The heuristic value for IC-ACO algorithm iscalculated as given as follows:

𝜂𝑗=𝐸0− 𝐸residual∑𝑘∈𝑁𝐸𝑘

, (5)

where 𝐸0is the initial energy and 𝐸residual is the residual

energy of the node.The 𝛼 and 𝛽 are two parameters that control the relative

weight of the pheromone trail and heuristic value.

One has

𝛽 =1

𝜇, (6)

where 𝜇 is a constant value.Each node selects the next adjacent node with the

maximum probability for the optimum path for sending thesensed information to the sink or base station.

Algorithm 1 represents the steps to be followed for theimplementation of proposed approach (IC-ACO) for findingthe shortest path to the destination or base station.

5. Simulation Results

In simulation performance of LEACH algorithm is comparedwith the proposed algorithm. These sensor nodes may bedistributed randomly in 100∗100 square. All the nodes havesame transmission range. The sink node, that is, base station,lies at the center of this square area (50, 50). The nodes havetheir horizontal and vertical coordinates located between0 and maximum value of the dimension which is 100.Simulation results depict that IC-ACO has more stabilityperiod than LEACH protocol and higher energy efficiencyin dense environment than the existing LEACH protocol. InLeachwhen the number of nodes increases from 100 to 200 or300, the performance of LEACHprotocol decreases while theperformance of IC-ACO either increases or remains stable.

These two algorithms are compared on the basis offollowing parameters:

(1) Stability period: stability period is the time intervalfrom the start of network operation until the death of


Table 2: Parameter values used for simulation.

Parameters Value𝑋 and 𝑌 coordinate of the sink 50, 50Radius 5Optimal election probability of anode to become cluster head (𝑃) 0.1

Initial energy (𝐸0) 0.5 Joules

ETX = ERX 50 ∗ 0.000000001 JoulesTransmit amplifier types

Efs 10 ∗ 0.000000000001 JoulesEmp 0.0013 ∗ 0.000000000001 Joules

Data aggregation energy (EDA) 5 ∗ 0.000000001 JoulesPheromone control parameter 𝛼 1Heuristic control parameter 𝛽 2Maximum number of rounds(𝑟max)

3000

the first sensor node; we also refer to this period as“stable region”;

(2) network lifetime: network lifetime is the time intervalfrom the start of operation (of the sensor network)until the death of the last alive node;

(3) total number of packets transmitted to the basestation at different rounds of iterations.

5.1. Network Simulation Parameters. For simulation 100, 200,and 300 nodes are deployed within a region of 100∗100.Table 2 shows the various parameter values used for simula-tion.

Figures 5, 6, and 7 show the improvement in the stabilityperiod and enhanced network life time of the proposed algo-rithm in comparison to the existing LEACH algorithm.Thereis significant improvement in stable period and networklife time. Table 3 shows values of FND (first node dead) ofrespective algorithms when the numbers of nodes are 100,200, and 300. Figures 8, 9, and 10 depict the comparison onthe basis of total energy remaining within the system and thetotal number of rounds. It is clearly seen that the proposedalgorithm has higher energy efficiency as compared to theLEACHalgorithm. Figures 11, 12, and 13 show the informationreceived by the base station and it is clear from these figuresthat total number of packets received by the base station incase of proposed algorithm is much greater than the existingLEACH algorithm.

Figure 5 illustrates the total number of nodes alive overthe time which indicates the lifetime of network when 100sensor nodes are deployed in the network; the figure showsthat IC-ACO performs much better than LEACH. First nodeis dead at 436 rounds in LEACH protocol and at 930 roundsin IC-ACO, which shows the significant improvement in thestability period.

Figure 6 depicts the total number of nodes alive overthe time which indicates the lifetime of network when 200sensor nodes are deployed in the network; the figure showsthat the IC-ACO performs much better than LEACH in

Table 3: Number of nodes at which first node dies for LEACH andIC-ACO (stability period).

Algorithm IC-ACO LEACHalgorithm

FND when the number of nodes is 100 930 436FND when the number of nodes is 200 948 222FND when the number of nodes is 300 895 145

0 500 1000 1500 2000 2500 3000 35000

102030405060708090

100

Number of rounds

Num

ber o

f nod

es al

ive

LEACHIC-ACO

Figure 5: Total number of nodes alive within the system at differentrounds of iterations (number of nodes is 100).

dense environment also. First node is dead at 222 roundsin LEACH protocol and at 948 rounds in IC-ACO whichshows that the performance of LEACH protocol degrades indense environment and there is significant improvement inthe stability periodof IC-ACO algorithm.

Figure 7 shows the total number of nodes alive over thetime which indicates the lifetime of network when 300 sensornodes are deployed in the network; the figure illustrates thatthe IC-ACO performs much better than LEACH. First nodeis dead at 145 rounds in LEACH protocol and at 895 roundsin IC-ACO which shows that the performance of LEACHprotocol degrades rapidly in dense environment or when thenumber of sensor nodes increased. It is clearly seen from thefigure that the IC-ACO algorithm outperforms the existingLEACH protocol.

Figure 8 depicts the comparison on the basis of totalenergy remaining within the system and the total numberof rounds and it is clearly seen that the proposed algorithmhas higher energy efficiency as compared to the LEACHalgorithm. The figure shows the total energy remainingwithin the system when 100 sensor nodes are deployed inthe network considering all nodes have homogeneous initialenergy 0.5 Joules.

Figure 9 depicts the comparison on the basis of totalenergy remaining within the system and the total numberof rounds and it is clearly seen that the proposed algorithmhas higher energy efficiency as compared to the LEACHalgorithm. The figure shows the total energy remaining


020406080

100120140160180200

0 500 1000 1500 2000 2500 3000 3500Number of rounds

Num

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f nod

es al

ive

LEACHIC-ACO


0

50

100

150

200

250

300

0 500 1000 1500 2000 2500 3000 3500Number of rounds

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LEACHIC-ACO


05

101520253035404550

Tota

l ene

rgy

of al

l the

clus

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LEACHIC-ACO

Figure 8: Total energy of the system at different rounds of iterations(number of nodes is 100).

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rgy

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0102030405060708090

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LEACHIC-ACO


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nerg

y of

all t

he cl

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0 500 1000 1500 2000 2500 3000 3500Number of rounds

LEACHIC-ACO


within the system when 200 sensor nodes are deployed in thenetwork.

Figure 10 depicts the comparison on the basis of totalenergy remaining within the system and the total numberof rounds and it is clearly seen that the proposed algorithmhas higher energy efficiency as compared to the LEACHalgorithm. The figure shows the total energy remainingwithin the system when 300 sensor nodes are deployed in thenetwork.

Figure 11 shows that the information received by thebase station when 100 wireless sensor nodes are deployed inthe network. It is clearly visible that even if the redundantinformation has not been transmitted in IC-ACO approach,the total number of packets received by the base station isincreased because IC-ACO has increased network life timecompared to the existing LEACH protocol.


0

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LEACHIC-ACO

×103

Figure 11: Total number of packets transmitted to the base stationat different rounds of iterations (number of nodes is 100).

0123456789

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LEACHIC-ACO

×103


Figure 12 shows the information received by the basestation when 200 wireless sensor nodes are deployed inthe network. It is clearly seen that in IC-ACO approach,the total number of packets received by the base stationis substantially increased compared to the existing LEACHprotocol in densely deployed network.

Figure 13 shows the information received by the basestation when 300 wireless sensor nodes are deployed inthe network. It is clearly seen that in IC-ACO approach,the total number of packets received by the base station isincreased compared to the existing LEACHprotocol in denseenvironment also.

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e sta

tion

0 500 1000 1500 2000 2500 3000 3500Number of rounds

LEACHIC-ACO

×103


From the simulation, following results are obtained.

(1) There is an improvement in stability period as com-pared to existing LEACH algorithm in dense environ-ment.

(2) IC-ACO algorithm has higher energy efficiency ascompared to existing LEACH algorithm.

(3) Information received by the base station has increasedconsiderably in the IC-ACO algorithm.

6. Conclusion and Future Work

Finding optimal path in a dynamic changing environmentof WSN is a challenging issue. The primary goal of theproposed work is to prolong the network life time in adense environment, since in a densely deployed network, itis highly probable that the sensor nodes in close proximitytransmit redundant data to the base station and energy iswasted.Thus the overall life time of the network gets reduced.In the proposed framework, the application of Ant Colonymetaheuristic approach to identify the optimal path betweena sensor node and the base station, in a densely deployednetwork, has been studied. The optimal path has been cal-culated based on pheromone concentration in homogeneousenvironment. The experimental results show that despite theextra overhead of selecting intermediate nodes, proposedalgorithm is able to give better results in terms of elevatedamount of packet transmitted, prolonged network lifetime,enhanced stability period, and higher energy efficiency ina densely deployed network. The proposed protocol wasstudied for several wireless sensor network scenarios byvarying the number of nodes for a dense environment andthe results clearly capture the prolonged network lifetime,improved energy efficiency, and higher number of packettransmission.

In this algorithm, cluster heads are selected randomly.In future work, cluster head selection mechanism can be


applied along with ACO to get the optimal cluster head. Inthe proposed algorithm, the experiments have been carriedout for the homogeneous environment; further result canbe improved for the heterogeneous environment. In futurevarious approaches such as integration ofmultiple sink nodesandmobility context of sensors in dense environment shall bestudied, which are considered as a big challenge in wirelesssensor network.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

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