Minimizing energy consumption in wireless sensor networks using modified genetic algorithm and an energy balance filter

Abstract

Clustering model in wireless sensor network is an efficient way to reduce the consumed power during the transmission of sensed data to the Base Station (BS). In this paper, an intelligent clustering protocol is proposed to minimize energy consumption and prolong network lifetime. The proposed protocol performs clustering with dynamic number of clusters depending on the nodes distribution and the field dimensions. The process of selecting the optimum number of clusters and electing the suitable cluster heads (CH) is done by using modified genetic algorithm. This work proposed a method to modify the genetic algorithm. The target of the genetic algorithm (fitness function) is to minimize the total energy consumed by all nodes in the round. Balancing the residual energy between the nodes is an important factor to prolong network lifetime. To ensure balancing in the sensors network, an energy filter was proposed to block low energy nodes from acting as cluster heads and select the remaining nodes as candidate CHs then the modified genetic system chooses the optimum CHs from the candidate CHs. The simulation results demonstrate that the proposed algorithm outperform the common clustering protocol in terms of network lifetime and energy consumption.