Expectation Maximization and Fuzzy Logic Based Energy Efficient Data Collection in Wireless Sensor Networks with Mobile Elements

Abstract

Wireless Sensor Networks(WSN) importance has increased drastically because of many application areas. However, the main challenge in WSN is to save battery power, as it is very difficult to recharge/replace the battery once it drains. Thus energy efficient algorithms that save battery has to be designed. In the present paper a novel data collection algorithm, Expectation Maximization and Fuzzy Logic based Energy Efficient Data Collection in Wireless Sensor Networks using Mobile Elements (EMFLDC), is designed to reduce the consumption of energy by using Clustering and Mobile elements(ME’s). First, the entire WSN is divided into optimal number of clusters by using Expectation Maximization algorithm. Second, Cluster Heads (CH’s) are selected based on fuzzy logic considering linguistic parameters. Third, Rendezvous Points (RP’s) are obtained by the base station, and these RP’s are selected between the CH’s by considering transmission distance of CH. Finally, ME travels to RP’s in the pre determined optimal path obtained by Ant Colony Optimization for Travel Salesman Problem algorithm (ACO-TSP) and collects data from CH’s and sends collected data to base station. Thus in the present work energy consumption for communication of data from sensor nodes to base station is reduced increasing network life time. The simulated results are compared with the Node Density based Clustering and Mobile Collection (NDCMC) algorithm and found that the proposed algorithm EMFLDC, is better than NDCMC.