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Improving the Energy of Wireless Sensor Networks Using Genetic Algorithm


Alaa Al-Shdaifat, Khalid Batiha, and Wafa alsharafat


Vol. 22  No. 1  pp. 684-696


Energy efficiency is a critical issue in wireless sensor networks (WSNs) in which all sensor nodes are equipped with one-time batteries or low-energy batteries. The efficient method for saving energy is clustering network nodes to avoid long-distance communication with the base station. This action preserves energy of the sensor nodes for long and extends their lifetimes. Furthermore, selection of the cluster heads (CHs) for better, energy-efficient routing plays an important role in extending the network lifetime. In this paper, a new centralized clustering algorithm based on the enhanced Genetic Algorithm (CCA-EGA) is used to find an adequate number of CHs in the network. New crossover methods are applied concurrently with the enhanced Genetic Algorithm (EGA) to improve CH selection in terms of energy consumption. These methods are called Simple Arithmetic Crossover (SMX), Single Arithmetic Crossover (SNX), and Discrete Crossover (DX). In line with its goal, this study had two main objectives: (i) analyzing the impact of fitness scaling on improving the convergence efficiency of the genetic solution over that of the traditional approach and (ii) analyzing the impact of the crossover method on the WSN energy efficiency. Performance of the proposed CCA-EGA in terms of energy efficiency was compared with levels of performance of other methods aimed at improving energy efficiency of WSNs. The testing results suggest that the proposed CCA-EGA enhances energy consumption of the WSN by preserving CHs for long and extending their lifetimes.


Genetic Algorithm; Crossover; SMX; SNX; DX; Cluster Head.