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ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm Karthik Raman Pranav Vaidya Spring 2006.

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Presentation on theme: "ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm Karthik Raman Pranav Vaidya Spring 2006."— Presentation transcript:

1 ECE 695 Project Presentation Clustering 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 Cluster Head Base Station 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 Solution Problem Representation NodesN0N1N2N3N4N5N6N7N8N9 Bits1010000001  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. Cluster Head

10 Fitness Function Discussion  To transmit a k-bit message across a distance of d, the energy consumed can be represented E(k,d)=E elec * k + E amp * k * d 2 Where:  E elec is the radio energy dissipation  E amp is a transmit amplifier energy dissipation  To receive a k-bit message, the energy consumed is as follows:  E Rx (k) = E elec * k

11 Our Fitness Function F=w*(D-distance i )+(1-w)*(N-H i )+α*Battery_State Where: w is the biasing factor; D is the total distance of all nodes to the sink; Distance i 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; H i 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: Indv1:11100101 Indv2:10111110 Crossover Point After Crossover: Child1:11101110 Child2:10110101

14 GA Operators-Mutation Before Mutation: Indv:11111101 After Mutation: Indv:11101111

15 Experiment Setup and Results Application Demo Conclusion & 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 Scenarioperformance % cases performance of order 2 1 st random walk> order 299% 2 nd random walk> order 290% 3 rd random walk> order 299%

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.

27 REFERENCES  [1] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks. In Proceedings of the Hawaii International Conference on System Science, Maui, Hawaii, 2000.  [2] Selim, S. Z. and Ismail, M. A. K-means type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell. 6, 81–87, 1984.  [3] Russell C. Eberhart and Yuhui Shi “Computational Intelligence: Concepts to Implementations”. Indiana  [4] J. C. Bezdek (1981): "Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York, http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/cmeans.html  [5] Tracy Camp, Jeff Boleng and Vanessa Davies: “A Survey of Mobility Models for Ad Hoc Network Research”, Golden, CO, 2002  [6] Seapahn Meguerdichian, Farinaz Koushanfar, Miodrag Potkonjak and Mani B. Srivastava: “Coverage Problems in Wireless Ad-hoc Sensor Networks”, Los Angeles, CA, 2001  [7] F. L. LEWIS: “Wireless Sensor Networks”, Ft. Worth, Texas, 2004  [8] Jason Lester Hill: “System Architecture for Wireless Sensor Networks”, University of California, Berkeley, 2000  [9] Silvia Nittel, Kelvin T. Leung, Amy Braverman: “Scaling Clustering Algorithms for Massive Data Sets using Data Streams”, Los Angeles, CA, March 2004  [10] Xiaohui Cui, Thomas E. Potok and Paul Palathingal: “Document Clustering using Particle Swarm Optimization”, Oak Ridge, TN, 2005  [11] Wendi Heinzelman, Anantha Chandrakasan and Hari Balakrishnan: “Energy-efficient Communication Protocols for Wireless Microsensor Networks”, Maui, HI, January 2000Hari Balakrishnan  [12] A. Bruce McDonald and Taieb F. Znati: “A Mobility-Based Framework for Adaptive Clustering in Wireless Ad Hoc Networks”, 1999  [13] Guolong Lin, Guevara Noubir and Rajmohan Rajaraman: “Mobility Models for Ad Hoc Network Simulation”, Boston, MA, 2004  [14] Greg Badros: “Evolving Solutions: An Introduction to Genetic Algorithms”, http://www.duke.edu/vertices/update/win95/genalg.html, 1995 http://www.duke.edu/vertices/update/win95/genalg.html


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