Develop A Particle Swarm Optimization-Based K-Means Clustering Protocol For Extended Coverage Area And Energy Efficiency In WSN

Neo, Ker Fang (2015) Develop A Particle Swarm Optimization-Based K-Means Clustering Protocol For Extended Coverage Area And Energy Efficiency In WSN. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 pages) Develop A Particle Swarm Optimization-Based K-Means Clustering Protocol For Extended Coverage Area And Energy Efficiency In WSN 24 Pages.pdf - Submitted Version Download (335kB)

Abstract

In this information technology era, wireless communications are getting more and more popular. The demand of everything to go wireless is not possible and it is definitely not a thing only in future. Wireless sensor network (WSN) is getting more attention since the mid-1990s. It is a network which consists of sensor nodes that is used in various industries such as security. The developers of WSN usually faced challenges such as limited energy and coverage area. Therefore, this project is made to improve the coverage area and energy efficiency in WSN by using an algorithm called Particle Swarm Optimization-based K-means Clustering Protocol. Particle Swarm Optimization algorithm is used to locate the cluster heads and K-Means Clustering Protocol is used to cluster the sensor nodes. This project is simulated using Matlab program. The energy efficiency and coverage area for three different size of area are obtained by using Particle Swarm Optimization-based K-means Clustering Protocol is compared with Derivative Harmony Search algorithm-based K-means Clustering Protocol and Genetic Algorithm-based K-means Clustering Protocol. The results show that Particle Swarm Optimization-based K-means Clustering Protocol has better coverage area but Derivative Harmony Search algorithm-based K-means Clustering Protocol and Genetic Algorithm-based K-means Clustering Protocol have better energy efficiency.

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