Genetic-Algorithm-Based Energy-Efficient Clustering (GAEEC) for Homogenous Wireless Sensor Networks

摘要

In wireless sensor networks, energy consumption of sensors by radio communication is a critical design issue that is needed to address to improve the stable period and overall lifetime of the network. Sensors are usually operated with limited battery cells and their energy is majorly depleted due to the radio communication rather than sensing operations. Clustering algorithms are commonly used for improving the energy efficiency of the network. But, due to uneven transmission distances for different static sensors in both inter-cluster and intra-cluster communications in clustering algorithms, there is uneven energy consumption in these sensor nodes, the networks become energy-heterogeneous over the passage of time, which may lead to reduced network's stable period and lifetime if data transmission is not handled judiciously. In this paper, we propose a novel Genetic-Algorithm-Based Energy-Efficient Clustering (GAEEC) which uses the genetic algorithm twice with different parameters and operators to perform static and optimal clustering and then, improve the cluster head election by picking up one of the best cluster head in each cluster by considering the current remaining energy and overall transmission cost to improve the overall network lifetime of the network. The performance of this proposed and implemented protocol has been analysed through simulations in terms of stability period, throughput, energy dissipation, and the number of nodes alive in comparison with the state-of-the-art algorithm LEACH. Simulation results show that GAEEC achieves longer stable region, improved throughput, and better energy conservation than LEACH.