Ayon Chakraborty1 , Kaushik Chakraborty1 ,Swarup Kumar Mitra2 ,M. K. Naskar3

2 Department of ECE, MCKV Institute of Engineering

1 Department of Computer Science and Engineering3 Department of Electronics and Telecommunications Engineering

Jadavpur University

An Optimized Lifetime Enhancement Scheme for Data Gathering in Wireless Sensor Networks

Design Challenges in Wireless Sensor Networks

Data Gathering Algorithms

Proposed Algorithm

Wireless Sensor Networks

Conclusion

Simulation Results

Contents

• Collect data / information from the sensor field.• Ad-hoc nature of WSNs Typically, severely energy constrained

Limited energy sources (e.g., batteries) Trade-off between performance and lifetime

•Self-organizing and self-healing Remote deployments

•Scalable Arbitrarily large number of nodes

GOAL• Lifetime enhancement of sensor nodes

POINTS•Sensor nodes lose power while transmitting or receiving data at the time of data gathering

SOLUTION•Develop efficient algorithm for data gathering

OBJECTIVE OF DEPLOYING SENSOR NODES

DESIGN CHALLENGES

Node Deployment Scenario

BS

C0

C1

C2

C3

•LEACH•PEGASIS

PHILOSOPHYDistribute the energy dissipation by the sensor nodes at the time of data gathering equally around the network.

LEACH protocol randomizes the selection of cluster heads for equal energy dissipation, the PEGASIS protocol uses a greedy chain to the sink.

Optimized Lifetime Enhancement (OLE) SchemePHILOSOPHYIncrease the network performance by ensuring a sub-optimal energy dissipation of the individual nodes despite their random deployment. Use of modern Heuristic Techniques.

DIFFERENT DATA GATHERING SCHEMES

PEGASIS

Particle Swarm Optimization (PSO):Kennedy and Eberhart, 1995

Randomly Scattered Particles over the fitness landscape and their randomly oriented velocities

All Particle

s in

A close vicin

ity of th

e

Global optimum The best Particle

Conquering the Peak

Situation after a few iterations

)()()()1( 21 xgxptvtv

)1()()1( tvtxtx

x

y

v(t)

v(t+1)

x(t)

x(t+1)

p(t)

g(t)

PSO (2) - Visually in 2D

A Close Look at Velocity Update

vid= vid Inertia

Cognitive learning Social learning

Update Position

pbest = the personal best solution fitness) a particle has achieved so far. gbest = the global best solution of all particles.

Initialize particles with random position and zero velocity

Evaluate fitness value

Compare & update fitness value with pbest and gbest

Meet stopping criterion?

Update velocity and position

Start

End

YES

NO

Flow-Chart: PSO algorithm

Evolutionary Algorithms

Use a more complex Evaluation Function:• Do sometimes accept candidates with higher

cost to escape from local optimum• Adapt the parameters of this Evaluation

Function during execution• Based upon the analogy with the simulation of

the annealing of solids

Simulated AnnealingSimulated Annealing

Analogy• Slowly cool down a heated solid, so that all particles

arrange in the ground energy state• At each temperature wait until the solid reaches its thermal

equilibrium• Probability of being in a state with energy E :

Pr { E = E } = 1/Z(T) . exp (-E / kB.T)

E EnergyT TemperaturekB Boltzmann constantZ(T) Normalization factor (temperature dependant)

Metropolis Acceptance

• At a fixed temperature T :• Perturb (randomly) the current state to a new state• E is the difference in energy between current and new

state• If E < 0 (new state is lower), accept new state as current

state• If E 0 , accept new state with probability

Pr (accepted) = exp (- E / kB.T)• Eventually the systems evolves into thermal equilibrium at

temperature T ; then the formula mentioned before holds• When equilibrium is reached, temperature T can be

lowered and the process can be repeated

Simulated Annealing in Combinatorial Optimization (S. Kirkpatrick et al.)

• Same algorithm can be used for combinatorial optimization problems:

• Energy E corresponds to the Cost function C• Temperature T corresponds to control parameter c

Pr { configuration = i } = 1/Q(c) . exp (-C(i) / c)

C Costc Control parameterQ(c) Normalization factor (not important)

PROBLEM MODEL•Total no. of nodes are N•Solution space , collection of arrangements of {1,2,3, … ,n}•Every arrangement Ci represents a chain, where U = {Ci | Ci is a permutation of (1,2,.. n)}DATA GATHERING SCHEME USING PSOConsider nth dimensional system, Ci denotes the ith particle in our n-dimensional systemEnergy Function for Ci is ∆f = f(Cnew) – f(Cold) = ∆E, PROBABILITY FUNCTION P P = 1 if ∆E ≤ 0 = exp (-∆f/ Ө) if ∆E > 0If P > rand(0,1) accept the solution else reject it. COOLING SCHEDULE The Control Parameter is Ө, called the annealing temperature.PROPERTIES OF Ө Decremented every time when the system of particles approach a better solution (or a low energy state)

Өi = initial temperature, Өf = final temperature, t = cooling time

Ө(t) = Өf + (Өi - Өf )*α t α = rate of cooling, usually 0.7 ≤ α < 1.0 Here t is the number of iterations.

OUR APPROACH TO SOLUTION

Step 1: Initialization : •Initialize the m particles C1,C2,C3….Cm , Ci={node[1], node[2],...,node[N]}.•Initialize parameters α, Өi , Өf

Step 2 : Finding a local best chain•At a temperature Ө and for for L iterations, the searching of local best chain is done by a random binary swapping Cold = {n1 ,n2 ,…….,nn}

•Select randomly two nodes say ni, nj , Cold = {n1 ,n2 ,…, ni ,…, nj,….,nn}

•Swap them•Cnew = {n1 ,n2 ,…, nj,…, ni,….,nn}

•Calculate P for the new chain i.e for Cnew

•If P > rand (0, 1), a random number between 0 and 1• Cold = Cnew = Cilpbest= local best solution of particle C

Step 3 : Updating the pbest and gbest values Cipbest = best solution for all particles

•Cipbest = Cilbest if { f(Cilbest) – f(Cipbest) } < 0

• = Cipbest if { f(Cilbest) – f(Cipbest) } ≥ 0

•comparing the all the Cipbest values

•Cgbest = global best solution

•Cgbest = min{ f(Cipbest)}

PROPOSED ALGORITHM

Step 4 : Formation of a new chain : Chain is formed based on Cipbest and Cgbest Cross over technique Suppose Cipbest = {4,5,2,3,6,1} and Cgbest = {5,2,1,4,3,6} The slot {2,1,4} is randomly chosen from Cgbest and inserted in the same Position in Cipbest and the node ids that are repeated are deleted Cinew = {5,2,1,4,3,6}

Step 5 : The temperature Ө(t) is calculated. If its value is less than or equal to Өf or the total number of iterations up to now exceeds the value of t, the algorithm comes to a halt. Thebest chain formed is Cgbest.

Leader selection phase

Formation of sub-optimal chain max[ Eresi /D4 . Here Eresi denotes the residual energy of an individual node before starting a data gathering round and D is the distance of the base station from that node. The node with the maximum value of Eresi /D

4 becomes the leader. Here we consider the multipath fading (distance4 power loss) channel mode, as the leader is concerned with communicating to the distant base station.

PROPOSED ALGORITHM

Simulation Results

NUMBER OF DATA GATHERING ROUNDS FOR VARIOUS SCHEMES WITH PERCENTAGE OF DEAD NODES

Simulation ResultsPerformance analysis of different protocols with Energy/node 1J and base station at (25,150).

The mean packet loss rate versus distance is shown, with error bars indicating one standard deviation from the mean. The model is highly variable at intermediate distances .TOSSIM radio loss model based on

empirical data

Greedy Chain

Chain by OLE Scheme

Simulation Results

CONCLUSION

• Optimal energy utilization occurs thereby increasing network lifetime as is validated by simulation results. • PSO along with Simulated Annealing helps to enhancethe performance of our scheme.

Two major advantages: (i) Development time is much shorter rather than using more traditional approaches. (ii) The systems are very robust, being relatively insensitive

to noisy and/or missing data.

Moreover, the OLE scheme has been coded in nesC, which justifies it to be feasible on real motes. Also, we have considered the TOSSIM interference model, while simulating packet loss rates for the various scheme

Our future goal is to study the problem using Genetic Algorithms and compare it to the OLE scheme

Reference

REFERENCES[1] Clare, Pottie, and Agre, “Self-Organizing Distributed Sensor Networks”,In SPIE Conference on Unattended Ground Sensor Technologies and Applications,pages 229–237, Apr. 1999.[2] Yunxia Chen and Qing Zhao, “On the Lifetime of Wireless Sensor Networks”, Communications Letters, IEEE, Volume 9, Issue 11, pp:976–978,DigitalObjectIdentifier10.1109/ 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. on System Sciences, pp. 1-10,Jan 2000.[5] S. Lindsey, C.S. Raghavendra, “PEGASIS: Power Efficient Gathering in Sensor Information Systems”, In Proceedings of IEEE ICC 2001, pp. 1125-1130,June 2001.[6] Ayan Acharya, Anand Seetharam, Abhishek Bhattacharyya, Mrinal KantiNaskar, “Balancing Energy Dissipation in Data Gathering Wireless Sensor Networks Using Ant Colony optimization” ,10th International Conference on Distributed Computing and Networking-ICDCN 2009, pp437-443, January 3-6,2009.[7] Eberhart, R. C, Kennedy, J. “A new optimizer using particle swarm theory”,1995 .[8] Kirkpatrick S, “Simulated Annealing” , Sci, Vol 220, 1983.[9] David Gay, Philip Levis, David Culler, Eric Brewer, nesC1.1 Language Reference Manual, May 2003.[10] Philip Levis ,TinyOS Programming , June 28, 2006.[11] P. Levis, N. Lee, M. Welsh, and D. Culler. TOSSIM: Accurate and ScalableSimulation of Entire TinyOS,[12] N. Metropolis et. al. J. Chem. Phys. 21. 1087 (1953).[13] Zhi-Feng Hao, Zhi-Gang Wang; Han Huang, “A Particle Swarm Optimization Algorithm with Crossover Operator”, International Conference on Machine Learning and Cybernetics 2007, pp -19-22, Aug.2007.

Particle Swarm OptimizationParticle Swarm Optimization (PSO) Algorithm was developed in 1995 by James Kennedy and Russ Eberhart

It was inspired by social behavior of bird flocking or fish schooling

PSO was applied to the concept of social interaction to problem solving

The Particle Swarm Optimization Algorithm

Homogeneous Algorithminitialize;

REPEAT

REPEAT

perturb ( config.i config.j, Cij);

IF Cij < 0 THEN accept

ELSE IF exp(-Cij/c) > random[0,1) THEN accept;

IF accept THEN update(config.j);

UNTIL equilibrium is approached sufficient closely;

c := next_lower(c);

UNTIL system is frozen or stop criterion is reached

Inhomogeneous Algorithm

• Previous algorithm is the homogeneous variant:

c is kept constant in the inner loop and is only decreased in the outer loop

• Alternative is the inhomogeneous variant:

There is only one loop; c is decreased each time in the loop, but only very slightly

Parameters• Choose the start value of c so that in the beginning

nearly all perturbations are accepted (exploration), but not too big to avoid long run times

• The function next_lower in the homogeneous variant is generally a simple function to decrease c, e.g. a fixed part (80%) of current c

• At the end c is so small that only a very small number of the perturbations is accepted (exploitation)

• If possible, always try to remember explicitly the best solution found so far; the algorithm itself can leave its best solution and not find it again

Pitfalls of PSO

• Particles tend to cluster, i.e., converge too fast and get stuck at local optimum especially in gbest PSO: Premature Convergence

• Movement of particle carried it into infeasible region, unnecessary loss of computational power.

• Inappropriate mapping of particle space into solution space

Other Names

• Monte Carlo Annealing• Statistical Cooling• Probabilistic Hill Climbing• Stochastic Relaxation• Probabilistic Exchange Algorithm