Energy-Efficient Clustering in Wireless Sensor Network: A Comparison between Genetic Algorithm and Bacteria Conjugation

Abstract

Wireless Sensor Networks are resource restrained in terms of energy consumption, lifetime, and processing speed. The prevalent goals of many types of research in Wireless Sensor Networks are to lessen the energy consumption among the sensor nodes and improve the life time of the network. Of all the measures, designing an efficient clustering protocol stands out. As a result, this research focuses on energy-efficient clustering in Wireless Sensor Networks. In this work, Genetic Algorithm is used to optimize clustering using the Mobile LEACH protocol. It is observed that Genetic Algorithm has been used to optimize LEACH in the previous works, but little attention is made on Mobile LEACH. LEACH and Mobile LEACH with Genetic Algorithm are implemented and simulated in MATLAB. The results show that the introduction of mobility and optimization using LEACH increases the stability period of the network. This accounts for about a 25% decrease in the energy consumed by the nodes of the network. It is therefore a rule of thumb in optimization that using algorithm A, successfully in a particular problem does not guarantee its effectiveness, it is   then compared with other optimization techniques.  As a result, adaptive clustering of Mobile LEACH optimized with Bacteria Conjugation Operator (BCO) is also implemented and the result obtained outperforms the adaptive clustering obtained with Genetic Algorithm.