An Energy Efficient Scheme for Data Gathering in Wireless Sensor Networks

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Journal of Applied Computer Science, no. 6 (3) /2009, Suceava

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An Energy Efficient Scheme for Data Gathering in Wireless Sensor Networks

Using Particle Swarm Optimization

Ayon Chakraborty 1, Kaushik Chakraborty 1, Swarup Kumar Mitra 2, M.K. Naskar 3

1 Department of Computer Science and Engineering , Jadavpur University, Kolkata – 32, India.

2Department of Electronics &Communication Engineering, MCKV Institute of Engineering, Howrah.

3 Advanced Digital and Embedded Systems Lab, Department of ETCE, Jadavpur University, Kolkata– 32,India.

[email protected], [email protected]@gmail.com, [email protected]

Abstract-Energy efficiency of sensor nodes is a sizzling issue,

given the severe resource constraints of sensor nodes and

pervasive nature of sensor networks. The base station being

located at variable distances from the nodes in the sensor field,

each node actually dissipates a different amount of energy to

transmit data to the same. The LEACH [4] and PEGASIS [5]

protocols provide elegant solutions to this problem, but may not

always result in optimal performance. In this paper we have

proposed a novel data gathering protocol for enhancing the

network lifetime by optimizing energy dissipation in the nodes.

To achieve our design objective we have applied particle swarm

optimization (PSO) with Simulated Annealing (SA) to form a

sub-optimal data gathering chain and devised a method for

selecting an efficient leader for communicating to the base

station. In our scheme each node only communicates with a

close neighbor and takes turns in being the leader depending on

its residual energy and location. This helps to rule out the

unequal energy dissipation by the individual nodes of thenetwork and results in superior performance as compared to

LEACH and PEGASIS. Extensive computer simulations have

been carried out which shows that significant improvement is

over these schemes.

Keywords: Wireless sensor network, Data Gathering Cycle,

Greedy Algorithm, Swarm intelligence, Simulated Annealing,

Network Lifetime.

I. INTRODUCTION

Wireless sensor networks consist of sensor nodes that are

randomly deployed in a large area, collecting importantinformation from the sensor field. Applications of sensor networks include climatic data gathering, underwater monitoring, battlefield surveillance, national security, health

care etc. These sensor nodes have very limited energyresources and hence, the energy consuming operations suchas data collection, transmission and reception, must be keptat a minimum [1]. Also, it is often infeasible to replace or recharge the sensors nodes deployed in inaccessible terrains.The sensor networks are required to transmit gathered data toa base station (BS) or sink, often distantly located from thesensor field. Network lifetime thus becomes an important

metric for sensor network design and efficiency. We have

taken network lifetime to be the time from inception to thetime when the network becomes nonfunctional, which we

have assumed to be the time when a single node dies.Moreover it is widely accepted that balancing the energy

dissipation among the nodes of the network is a key factor for prolonging the network lifetime [2]. Each sensor node isprovided with transmit power control and omni-directionalantenna and therefore can vary the area of its coverage [3]. Ithas been established in [4] that communication requiressignificant amount of energy as compared to computations.In this paper, we consider a wireless sensor network wherethe base station is fixed and located far off from the sensed

area. Furthermore all the nodes are static, homogeneous andenergy constrained and capable of communicating with the

BS.

The LEACH protocol [4] presents an elegant solution tothis energy utilization problem where nodes are randomlyselected to collaborate to form small number of clusters andthe cluster heads take turn in transmitting to the base stationduring a data gathering cycle. The PEGASIS protocol [5] is a

further improvement upon the LEACH protocol where agreedy chain of nodes is formed which take rounds intransmitting data to the base station.

In this paper, we approach the problem from a newviewpoint. In our scheme a chain is formed, but instead of allowing all nodes to become the leader, to communicatewith the base station the same number of times, the network lifetime is increased by allowing the individual nodes to

transmit unequal number of times to the base stationdepending on their residual energy and location.Furthermore, instead of forming a greedy chain, which maynot always ensure minimum energy dissipation, we make useof modern heuristic optimization techniques like particleswarm optimization (PSO) [6] and simulated annealing(SA)[7]. This results in an enhanced network performance as

balanced energy dissipation by the individual nodes isachieved in the network.

The rest of the paper is organized as follows: Section 2describes the energy dissipation model and Section 3 judgesthe emergence of an energy-efficient data gathering protocol.Section 4 begins with a brief idea about PSO and SA



Computer Science Section

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followed by the gradual development of our algorithm. Our

scheme is evaluated by results obtained from extensivesimulation in Section 5. Finally, we conclude in Section 6.

II. THE ENERGY DISSIPATION MODEL

Our aim in this paper is to minimize the energy usage inthe sensor nodes by formation of an optimal chain throughwhich the data gathering will take place. For this purpose, we

assume the radio model as discussed in [4]; for the radiohardware dissipation. This is one of the most widely usedmodels in sensor network simulation analysis. The energyspent in transmitting a k-bit packet over a distance of dmeters, is given by:

Etx( k, d) =( ξelec + ξamp * d2) *k (1)

and that for receiving the packet is,

Erx(k)= ξ elec * k (2) Here ξ elec (50nJ/bit) is the energy dissipated per bit to run

the radio electronics and ξ amp is the energy expended to runthe power amplifier for transmitting a bit over unit distance.

n is the path loss exponent, whose value enhances withincreasing channel non-linearities (usually, 2.0≤ n ≤4.0). In

our approach, we have used both the free space (distance2

power loss) and the multipath fading (distance4

power loss)channel modes. In our model, we assume, that inter-nodaldistances are small compared to distance between the nodesand the Base Station (BS). Thus for communication amongsensors we take n = 2, and that between the leader and BS,we take n = 4, in equation (1). Value of ξ amp = 100pJ/bit/m2

for n = 2 and 0.0013pJ/bit/ m4

for n = 4.Now for all practical purposes, we can assume that the

computational energy is much less than the communicationalenergy and thus can be neglected. Thus for the chain of length n, the total energy expended in data gathering is thesummation of the energy used by the individual sensor nodes

and the leader. Assuming a constant packet size of k = 2000bits,

Etotal={

1

1

n

i

−

=∑ ( ξelec+ξamp*di

2)+( ξelec + ξamp* D4)}*k (3)

In equation (3) di denotes the distance between the (i+1)th

node and the i th node in the data gathering chain. D is the

distance between the leader and the base station. The valuesof ξ elec and ξ amp are stated earlier. Here we impose a thresholdvalue on di as dTH . It is also assumed that the channel issymmetric so that the energy spent in transmitting from nodei to j is the same as that of transmitting from node j to i for any given value of SNR.

III. PROPOSED ENERGY EFFICIENT DATA GATHERING

SCHEME FOR LIFETIME ENHANCEMENT

The PEGASIS scheme [5] depends upon a greedy chain

formation whereas the LEACH scheme [4] randomizes theleader selection in the network. While the greedy chain cannot always guarantee minimal energy consumption, therandomized leader selection does not take into account thenode's capability in being the leader, in terms of its energy

content and transmit distance. Keeping the above drawbacksin mind, we proceed to form a suboptimal chain for datagathering and device a scheme to choose an efficient leader for communicating to the base station.

A. Basic PSO algorithm and Simulated Annealing Particle Swarm Optimization (PSO), a kind of

evolvement-computation technology based on the concept of swarm intelligence, was raised by Kennedy and Eberhart in1995 [6], who were inspired by the social behavior of flocking birds. A “swarm” is an apparently disorganizedcollection (population) of moving individuals that tend tocluster together while each individual seems to be moving ina random direction. It uses a number of agents (particles) thatconstitute a swarm moving around in the search spacelooking for the best solution. Each particle is treated as a

point in a D-dimensional space which adjusts its “flying”according to its own flying experience as the overallexperience of the swarm. Each particle keeps track of its

coordinates in the problem space which are associated withthe best solution (fitness) that has been achieved so far. Thisvalue is called pbest. Another best value that is tracked by

the PSO is the best value obtained so far by any particle inthe neighbors of the particle. This value is called gbest. The

PSO concept consists of changing the velocity (or accelerating) of each particle toward its pbest and the gbestposition at each time step. The PSO formulae defines eachparticle in the D-dimensional space as Xi = (xi1, xi2, xiD).Each particle has a little memory to store its pbest value(previous best position) as Pi = (pi1,pi2, .. piD) and avelocity along each dimension as

Vi = (vi1,vi2, …viD). The updating equations are,

xid = xid + vid (position update)vid = vid + c1r 1(pbest – xid) + c2r 2(gbest – xid)

(velocity update)The basic idea of simulated annealing (SA) proposed by

Metropolis in 1953 was used in compounding optimizationby Kirkpatrick in 1983 [7]. It is a stochastic process thataccepts the current optimal solution at a probability after searching, which is called the Metropolis Law [8]. Theacceptance probability is determined by two factors, theEnergy factor, which can be thought of to be similar to the







Journal of Applied Computer Science, no. 6 (3) /2009, Suceava

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nodes, with the no. varying from 50 to500

Fig3. SA-PSO algorithm applied in a random network of 100 sensor

nodes (figure obtained by Java Simulation).

V. CONCLUSION AND FUTURE WORKS

The protocol considered in this paper ensures that a near

optimum energy utilization occurs thereby increasingnetwork lifetime as is validated by the simulation results.

Fig.4. Variations in percentage improvement in E*D with SA-PSO over

PEGASIS with number of nodes.

The SA-PSO algorithm also helps to enhance theperformance of our scheme. Reports of applications of eachof these tools have been widely published, thus forming asolid background. Developing solutions with these tools

offers two major advantages:

Development time is much shorter than when using more

traditional approaches.The systems are very robust, being relatively insensitive

to noisy and/or missing data.

We have already developed the chain using SA-PSO, andalso have compared it to the ACO technique. Our future goalis to study the problem using Genetic Algorithms (GA)compare it to the SA-PSO and ACO techniques.

R EFERENCES

[1] Clare, Pottie, and Agre, “Self-Organizing Distributed Sensor

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[2] Yunxia Chen and Qing Zhao, “On the Lifetime of WirelessSensor Networks”, Communications Letters, IEEE, Volume 9, Issue11, pp:976–978, DigitalObjectIdentifier 10.1109/ LCOMM.

2005.11010., Nov. 2005.[3] S. Lindsey, C. S. Raghavendra and K. Sivalingam, “Data

Gathering in Sensor Networks using energy*delay metric”, In

Proceedings of the 15th International Parallel and Distributed Processing Symposium, pp 188-200, 2001.

[4] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor

Networks”, IEEE Proc. Of the Hawaii International Conf. onSystem Sciences, pp. 1-10,January 2000

[5] S. Lindsey, C.S. Raghavendra, “PEGASIS: Power EfficientGathering in Sensor Information Systems”, In Proceedings of IEEE ICC 2001, pp. 1125-1130, June 2001. [6] Eberhart, R. C, Kennedy, J. “A new optimizer using particle

swarm theory”, 1995 . [7] Kirkpatrick S, “Simulated Annealing“ , Sci, Vol 220, 1983.

[8] N. Metropolis et. al. J. Chem. Phys. 21. 1087 (1953).[9] Zhi-Feng Hao, Zhi-Gang Wang; Han Huang, “A Particle SwarmOptimization Algorithm with Crossover Operator”, International

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An Energy Efficient Scheme for Data Gathering in Wireless Sensor Networks