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An Ant Colony Clustering Routing Algorithm for Wireless Sensor Networks

WANG Guifeng

Network Information Center Guilin University of Electronic Technology

Guilin, China

[email protected]

WANG Yong, Tao Xiaoling

Network Information Center Guilin University of Electronic Technology

Guilin, China

[email protected]

Abstract —On the basis of analyzing the Low-Energy Adaptive

Clustering Hierarchy (LEACH), a clustering routing algorithm

for Wireless Sensor Network (WSN) based on ant colony

algorithm (ACA) is proposed in this paper. We applied the

ACA to inter-cluster routing mechanism and looked for the

best path from cluster heads to base station. Thanks to the

algorithm, the transmission of information, for the cluster

heads node away from the base station (Sink), along the best

path was achieved and the energy consuming of cluster headsnode was decreased. Meanwhile, not only the node residual

energy, but also the distance between the cluster heads was

considered for the selection of cluster heads. It resulted in the

more even distribution of cluster heads. Simulation result

indicates that the new algorithm has a more than 30% increase

in extension of network life compared with LEACH.

Keywords-WSN; ACA; clustering routing

I. I NTRODUCTION

Wireless Sensor Network (WSN) is composed of sensor nodes with the abilities of communication and computation

which construct networks in the form of multi-hopcommunication and self-organization [1]. As the power of sensor node is supplied by the battery and the energy islimited. Therefore how to make rational use of energy andachieve high efficient, as much as possible to prolong thenetwork lifetime, has become the core issue in sensor network research [2, 3]. Undertaking the task of transmittinginformation from the source node to the destination nodethrough the network. The network routing is the basis of highcommunicating efficiency of the network. Therefore, theWSN routing algorithm has become a present research focusas a key technology.

Ant Colony Algorithm (ACA) is a simulation of the behaviors of ants swarm in the Kingdom of insects [4], it has

the advantage of robust, excellent distributed calculatedmechanism, easy to combine with other methods, etc.Literature [5] meet the requirement of WSN by modifyingant colony optimization (ACO) and introduced the data-chiprouting mechanism to optimize path-selecting efficiency,however, the algorithm was only applied to the planar routing. Literature [6] have introduced the energy level of nodes and transmission distance into ACO pheromoneincrement formula which made ACO adapt better to routing protocol in WSN. The algorithm also took into account data

fusion, but did not take the balanced use of energy in thewhole networks into account.

At present, many algorithms which concerned aboutapplying ACA into the routing of WSN were put forward, but all of them were about the planar routing. In this paper, basing on ACA and the characteristics of WSN, applying theACA to clustering routing of WSN, we put forward a novelWSN clustering routing algorithm (ACALEACH). Thealgorithm apply the ACA into inter-cluster routingmechanism to reduce the energy consumption of cluster heads and finally prolong the life span of networks.

II. A N A NT COLONY CLUSTERING R OUTING ALGORITHM

FOR WSN

Figure 1. Flow chart of algorithm

The algorithm determine the cluster heads according tothe remained energy of node and the distance between nodes.Taking ACA's advantage on being implemented easily,according to the exchange of information of respective position, the distance from Sink and the remained energy thewhole network, based on which we can calculate the possibility of each adjacent cluster heads being selected asthe next hop to form the routing of adjacent clusters. The

2009 Third International Conference on Genetic and Evolutionary Computing

978-0-7695-3899-0/09 $29.00 © 2009 IEEE

DOI 10.1109/WGEC.2009.22

670

2009 Third International Conference on Genetic and Evolutionary Computing

978-0-7695-3899-0/09 $29.00 © 2009 IEEE

DOI 10.1109/WGEC.2009.22

670



algorithm has the characteristic of periodical cyclical round.Each round is divided into clustering-forming stage andclustering-stability stage. At the new “round” begining, thecluster is divided and then waits for data transfer. Thealgorithm flow chart is shown in Figure 1.

III. THE R EALIZATION PROCESS OF ALGORITHM

A. Clustering-forming Stage

Since the cluster heads will perform the extra functionssuch as data fusion and transit information, it will consumemore energy than the member nodes. So the remainingenergy value of the nodes must be taken into account whenselecting the cluster heads. In addition, due to the randomchoice of cluster heads, the phenomenon of relativeconcentration of cluster heads may exist. The algorithm refer to the method mentioned in literature [7] to limit minimumdistance (Dis) between cluster heads. However, thedifference between our method and that of the literature [7]is that: when the distance between two cluster heads is lessthan Dis, our algorithm will compare the energy value

between the two, and then choose the cluster head with moreenergy as the new cluster head but with less as the member node, thus, it can better optimize the clustering program.Implementation steps of the process and some pseudo-codesis as follows:

Step1: Initialize parameter values and information table,dispense the nodes dispensed into M×M square region,record the coordinates of the node (x, y) at the same time, asshown in Figure 2 (a).

Step2: R<——rand (0,1)// each node generate a random number

if R<T(n)//To determine whether the random number is less than T

(n), T(n) is calculated out by the equation (1)

S(i).type=cluster head// the node i is cluster headBroadcast//broadcast the information of becoming the cluster headif S(i).type=cluster head

Listeningif Cluster head receive clustering messages

sent by other cluster headsif D(i,j) <Dis

// D(i,j) denote the distance between two cluster headsif s(i).weight<s(j).weightchoose the cluster head with more energy as new cluster

head but with the less as member nodeend

max

0,

( ),

1 [ mod(1/ )

current

n G

T n E pn G

p r p E

∉⎧⎪

= ⎨• ∈⎪ −⎩

(1)

Where P is the percentage of desirable cluster heads in allsensor nodes, r is the round number, G is the node set whichcontain nodes which did not become the cluster head in thefinal 1/p rounds. Ecurrent denote the current energy of node,Emax denote the initial energy of node. The process of

selecting cluster heads is completed, as shown in Figure 2(b).

Step3: The non-cluster head in the network choose theappropriate cluster to join according to their distance fromeach cluster head (select the nearest distance) and theninform the cluster head. The process of cluster division iscompleted. The assumption that the entire region will be

divided into five clusters, namely, S1, S2, S3, S4, S5. Asshown in Figure 2 (c).

Sink

Sink

S1

S2

S3

S4

S5

(a) (b) (c)

cluster member cluster head

Figure 2. The various stages of clustering

B. Clustering-stability Stage

In order to achieve the multi-hop communications between cluster heads, reduce energy consumption of thecluster head away from Sink, to extend the survival time of network, in this paper, we run ACA between the cluster heads to achieve this goal. As shown in Figure (3). Supposethat the cluster head 1 is about to send data to Sink, insteadof sending directly, it will firstly send the data to cluster head2, through which the data will be send to sink. Similarly, the path is 3-4-5-Sink when cluster head 3 send the data to theSink. Concrete process is as follows:

Step1: Place K ants on each cluster head, then set matrixTabu, R_best, A_city and initialize them. Tabu is used tostore and record the generated path, R_best is used to storethe best path between each cluster head and the Sink, A_citystore the nodes which have been visited.

Figure 3. Data transmission from cluster head to Sink

Step2: K ants in each cluster head are transferred to thenext accessable cluster head with possibility P. P can becalculated out by equation (2). Add the accessed node into

Tabu, and then set it as the node not allowed to be access inA_city, and then update pheromone on the ant pathaccording to equation (4). Repeat step 2 until all ants in eachcluster head have accessed to the Sink node.

1 2

1 2

( ) ( )

( ) ( )i

ij ij

ij

ik k k N

p

β β

β β

τ η

τ η ∈

•=∑

(2)

Where Ni denotes the set of neighboring nodes of ch i,including the node can be chosen as next hop. β1 and β2 arerelative important parameter for controling the distance

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between pheromone and the two cluster heads. τij is tmoment, the concentration value of pheromone of (i,j) edge.Equation (3) denote the heuristic value from cluster head i to j.

2 2

1 1( )

( , ) ( ) ( )i j

j i j i

t D i j x x y y

η = =− + −

(3)

( ) 21

ij ij energyα α τ τ ι = + + (4)

Where energy is the remaining energy of neighboringcluster heads, ι denotes the distance between cluster head and base station, α1 denotes the percentage of the energy of neighboring node in the pheromone, α2 denotes the percentage of the distance from neighboring node to basestation in the pheromone.

Step3: For the K ants of each cluster head, select onewhich is the shortest path and with the lowest energyconsumption, store the path which the ant passed by. RepeatStep 2, 3, until each cluster head find the best path to Sink.

Step4: The cluster head will be made to translate data

along the optimal path until the energy of one cluster node isless than that consumed to send k bit data whit distance being d0 in the free space model, and then we will enter intothe next round.

IV. SIMULATION EXPERIMENTS AND A NALYSIS OF

R ESULTS

A. Energy Models and Performance Parameters

This paper adopt the same wireless energy model used inliterature [8]. The wireless transmission module can realizethe transmitting power control or be shut down automaticallyin order to avoid receiving unnecessary data according to thedistance between the nodes. We adopt matlab as thesimulation tool. Specific simulation environment as follows:200 nodes are randomly dispensed in the 200m×200m squareregion, the base station is located at (50,175), as shown inFigure (4). The parameters of algorithm is: Eelec=50nJ/bit,εfs=10 pJ/bit/m2, εmp=0.0013 pJ/bit/m2, EDA=0.5nJ/bit, theinitial energy is Eo=0.5J; The packet length is packetLength= 4000; The Control packet length is ctrPacketLength = 100,α1=0.01, α2=0, β1=2, β2=2, p=0.05.

Figure 4. Random distribution of nodes

B. Analysis of Simulation Results

In order to verify the performance of the algorithm, wemake the simulation comparison between the LEACH andour algorithm. Due to the length of the life cycle of thenetwork directly reflects the performance of WSN, wecompare our algorithm with LEACH in two aspects: theaverage energy consumption; the number of survival nodes.Figure (5) shows the average energy consumption diagramof our algorithm and the LEACH, it reflects the change of the average power consumption of network with timeelapsing. The total energy of 200 nodes in network is 100J.LEACH appears the death of node at the 450 round, whereconsumption of energy is 60J while that in this algorithm is12J. The algorithm can save 48J at this stage; when all of theLEACH node die, energy consumption after adopting thisalgorithm is the 80J, reducing 20J, the performance of network has been greatly improved.

Figure 5. The average energy consumption under the two algorithms

As shown in Figure (6). According to simulation results,LEACH appeared death of node at the 450 round while oursat the 950 round; The death of half of the nodes appeared atthe 750 round using LEACH while ours at the 900 round. All

nodes die at the 1150 round in LEACH while ours at the1500 round. Simulation result indicates that the newalgorithm has a more than 30% increase in extension of network life compared with LEACH.

Figure 6. The number of survival nodes under the two algorithms

V. CONCLUSION

With the characteristics of self-organized, dynamic andmulti-path, ACA make is particularly suited to the routing of WSN. On the basis of analyzing LEACH, a clusteringrouting algorithm for WSN based on ACA is proposed inthis paper. The algorithm has the characteristics of low

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expending on routing, self-organized and multi-path.Simulation result indicates that the new algorithm has aremarkable improvement in the average energy consumptionand extends the life cycle of the networks. As the WSNrouting protocols is vulnerable, how to design and realizerouting protocol with security mechanism will be our futureresearch work.

R EFERENCES

[1] I. F. Akyildiz and I. H. Kasimoglu, “Wireless sensor and actor networks: Research challenges,” Ad Hoc Networks Journal, 2004,2(4): pp. 351-367.

[2] Akyildiz I, and Su W.Sankarasubramaniam Y, “Wireless Sensor networks: a swrvey,” Computer Networks, 2002, 38(4): pp. 393-422.

[3] Heinzelman W, Chandrakasan A, and Balakrishnan H,“Energn_efficient Communication Protocol for wireless Sensor

networks//IEEE Proc of the Hawaii Int Conf System Sciences,”Washington: IEEE Computer Society, 2000. pp. 175 -187.

[4] Haibing Duan, “Ant Colony Algorithms: Theory and Applications,”Beijing: Science and Technology Press, 2007.

[5] J.T. WANG, J.D. XU, and J.C. XU, “Wireless Sensor NetworksRouting Protocol Based on Ant Colony Optimized Algorithm,”Journal of System Simulation, 2008, 20 (18): pp. 4898-4901.

[6] Camilo T, Carreto C, and Silva J S, “An Energy-Efficient Ant-BasedRouting Algorithm for Wireless Sensor Networks//ANTS2006, Int.,”Workshop on Ant Colony Optimization and Swarm Intelligence.Brussels, Bélgica: Springer Verlag, 2006: pp. 49-59.

[7] X.P. GU, Y.J. SUN, and J.S. QIAN, “An Improved ClusteringRouting Protocol for Wireless Sensor Networks,” Microelectronics &computer, 2009, 2, 26(3): pp. 34-37.

[8] Heinzelman W R, Chandrakasan A, and Balakrishnan H, “Energy-efficient communication protocol for wireless microsensor networks,” Hawaii International Conference on System Sciences(HICSS). Hamaii, 2000.

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ant colony wsn

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