Energy Efficient Clustering-Based Mobile Routing Algorithm on WSNs

Abstract

In this paper, we propose and discuss two types of algorithms to improve energy efficiency in Wireless Sensor Networks. An efficient approach for extending the life of a network is known as "sensor clustering" in wireless sensor networks. In proposed algorithms, the study area where sensor nodes are randomly distributed is divided into clusters. In each cluster, the sensor that is the closest to the cluster center and has the highest residual energy is chosen as the cluster head. To make this choice, a greedy approach and artificial neural network methods are applied. In addition, to reduce the energy consumption of cluster heads, a mobile sink is used. The list of routes to be used by the mobile sink is calculated with the genetic algorithm. According to the route information, the mobile sink moves to the clusters and initiates the data collection process for each cluster. We compared our models according to the round value at which all sensor nodes run out of energy and the energy consumption by the network per round. Simulation results show that the proposed models increase the energy efficiency and extend the network lifespan.