ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm

1. ECE 695 Project PresentationClustering Sensor Network using Genetic Algorithm Karthik Raman Pranav Vaidya Spring 2006

2. Outline • Introduction & Background • Proposed Genetic Algorithm (GA) Solution • Experiment Setup and Results • Demonstration of Application • Conclusion & Future Work

3. Introduction & Background • Sensor Networks • Popular, wide range of applications • Military, environment, health • Small, lightweight, battery powered wireless nodes distributed over large area • large communication distance from nodes to base station drain energy & reduce network life • Our goal • Use GA to cluster sensor network to minimize the total communication distance and prolong the network life.

4. Base Station Cluster Head Sensors Example of Clustered Network

5. Clustering the Network • Partitioning nodes into independent clusters • Various methods for clustering • Ex. K–means, Fuzzy c-means clustering • Drawback • Assume the number of clusters beforehand • Our contribution • Dynamic Sensor Network

6. Background on Genetic Algorithm (GA) • One of the major areas in Evolutionary Computation (EC) • EC consists of machine learning optimization and classification paradigms based on genetics and natural selection • GA mimics survival of the fittest strategy in nature by preferentially selecting a fitter genetic pool so that future generation will have fitter population members

7. GA Terminology • Population: set of points in problem domain, each member being a potential solution. • Generated randomly • Fitness: A value proportional to the function we want to optimize • Fitness value and fitness function • Selection: selecting a pool of high fitness population members • GA Operators: mimic reproduction • Crossover: pass information from one generation to next to guide population to acceptable solution • Mutation: introduce diversity to tunnel through local optima

8. GA Algorithm • The series of operations carried out when implementing a canonical GA paradigm are: 1. Initialize the population (randomly), 2. Calculate fitness for each individual in the population, 3. Reproduce selected individuals to form a new population, 4. Perform crossover and mutation on the population and 5. Loop to step 2 until some condition is met.

9. Proposed GA SolutionProblem Representation Cluster Head ClusterHead ClusterHead • Represent the population member in a binary format • Each bit represents a node • A normal node is represented by a 0 at the specific bit location • If the node is a cluster head then we have a 1 at the corresponding bit position • Nodes N0, N2 and N9 are the cluster heads • Nodes N1, N3 – N8 are the normal nodes.

10. Fitness Function Discussion • To transmit a k-bit message across a distance of d, the energy consumed can be represented E(k,d)=Eelec* k + Eamp * k * d2 Where: • Eelec is the radio energy dissipation • Eamp is a transmit amplifier energy dissipation • To receive a k-bit message, the energy consumed is as follows: • ERx(k) = Eelec * k

11. Our Fitness Function F=w*(D-distancei)+(1-w)*(N-Hi)+α*Battery_State Where: • w is the biasing factor; • D is the total distance of all nodes to the sink; • Distancei is the sum of the distance from regular nodes to cluster heads plus the sum of the distances fro all cluster heads to the sink; • Hi is the number of cluster heads; • N is the total number of nodes; • α is weighting factor for Battery_State; • Battery_State is a measure of current battery life;

12. Selection Method-Roulette Wheel Section

13. GA Operators-Crossover • One-Point Crossover Before Crossover: Crossover Point After Crossover:

14. GA Operators-Mutation Before Mutation: After Mutation:

15. Experiment Setup and ResultsApplication DemoConclusion & Future Work

16. Experiment Setup and Results • Simulation Test Bed • C# and .Net 1.0 Framework

17. Experiment Setup and Results • Description of Experiment • 5 random deployment scenarios using the simulation test bed • 100 sensor nodes and data collector • performed clustering using GA and analyzed the results against the criteria listed below • Performance of GA to maximize distance savings • Performance of GA to minimize number of cluster heads • Performance of GA to minimize energy dissipation in overall network

18. Results • Performance of GA to maximize distance savings

19. Results.. • Performance of GA to minimize number of cluster heads

20. Results.. • Performance of GA to minimize energy dissipation in overall network First Random Walk

21. Results.. Second Random Walk

22. Results.. Third Random Walk

23. Results… • Summary

24. Application Demo

25. Conclusion & Future Work • Our application provides a GA based method to reduce the communication distance in sensor networks via clustering. • We have shown successfully that our algorithm performs better to the order of 2 in almost 99% of the cases.

26. Conclusion & Future Work • Extending the simulation test bed to use other mobility models. • Evaluation of clustering algorithm using Linear Vector Quantization (LVQ) and Particle Swarm Optimization (PSO) and comparison with GA • The fitness function can be based on a lot of other optimization parameters namely battery charge and discharge of the nodes. • routing protocol for the setup, steady state and tear down phase for the sensor networks with cluster head authorization from data collector, cluster head advertisement and fault tolerance techniques.

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