A New Method To Optimize The Localization Of Sensor Node Using Derivative Harmony Search Algorithm-Based K-Means Clustering Protocol For Extended Coverage Area And Energy Efficiency In Wireless Sensor Network

Norhafizah, Bohari (2015) A New Method To Optimize The Localization Of Sensor Node Using Derivative Harmony Search Algorithm-Based K-Means Clustering Protocol For Extended Coverage Area And Energy Efficiency In Wireless Sensor Network. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

In this project, the optimization in Wireless Sensor Network (WSN) is using MATLAB software. The optimization is focused on the fundamental problems in WSN which are coverage area and energy efficiency. WSN consists of a large number of sensor nodes used to gather information from an unattended location and transmit it to particular users. The coverage problems deal with how well the sensor node to monitor a certain area. The sensor nodes should be able to monitor the whole area of the network. Energy is used when sensor nodes send data to the base station. Each sensor node is powered by limited energy source then, optimization is needed to solve this problem to prolong the lifetime of the WSN. The problem occurs in WSN can be solved using Derivative Harmony Search Algorithm (DHSA) and implement together with K-means clustering algorithm. By using this method the data transferred have low energy consumption and the covered area is optimal. The performance of DHSA will be compared with other algorithms which such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of coverage area and energy efficiency. From the simulation, it shows that by using a Derivative Harmony Search algorithm-based K-means clustering algorithm, it has a better solution compared to PSO and GA in terms of energy efficiency which is the energy consumption for communication is low.

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