Presentation on theme: "ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm Karthik Raman Pranav Vaidya Spring 2006."— Presentation transcript:
ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm Karthik Raman Pranav Vaidya Spring 2006
Outline Introduction & Background Proposed Genetic Algorithm (GA) Solution Experiment Setup and Results Demonstration of Application Conclusion & Future Work
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.
Cluster Head Base Station Sensors Example of Clustered Network
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
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
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
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.
Proposed GA Solution Problem Representation NodesN0N1N2N3N4N5N6N7N8N9 Bits 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
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
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;
Selection Method-Roulette Wheel Section
GA Operators-Crossover One-Point Crossover Before Crossover: Indv1: Indv2: Crossover Point After Crossover: Child1: Child2:
GA Operators-Mutation Before Mutation: Indv: After Mutation: Indv:
Experiment Setup and Results Application Demo Conclusion & Future Work
Experiment Setup and Results Simulation Test Bed C# and.Net 1.0 Framework
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
Results Performance of GA to maximize distance savings
Results.. Performance of GA to minimize number of cluster heads
Results.. Performance of GA to minimize energy dissipation in overall network First Random Walk
Results.. Second Random Walk
Results.. Third Random Walk
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%
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.
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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