Clustering for Energy Efficient and Redundancy Optimization in WSN using Fuzzy Logic and Genetic Methodologies a Review

Abstract

a wireless sensor network is a distributed sensing node to monitor physical or environmental conditions. Each node consists of sensors, wireless communication, limited processing capabilities and amount of energy reserved. The energy dissipation is an important problem to deal with, because of its effect on the network lifetime and availability. Recently, several clustering-based methods have been used for optimizing the redundancy and energy consumption. The low energy adaptive clustering hierarchy divided the wireless sensor network into a cluster with a cluster head. But its problem is on which parameter cluster heads are elected. This paper aims to provide a guideline for researchers whose aim to optimize data redundancy and energy consumption in the wireless sensor networks using clustering hierarchical depending on different parameters including distance to base station, node degree and node centrality to be used for electing the cluster head in addition to data redundancy. The electing is being achieved by using two techniques the Fuzzy Logic and genetic algorithm. The paper compared the techniques to provide a classification of the significant parameters that affects the lifetime of the network. Genetic Algorithm achieved by mixing the good solutions using a crossover and mutation operators while Fuzzy Logic using if-then rules and probability between 0 and 1.