EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs

Abstract

Wireless sensor networks are widely used in monitoring and managing environmental factors like air quality, humidity, temperature, and pressure. The recent works show that clustering is an effective technique for increasing energy efficiency, traffic load balancing, prolonging the lifetime of the network and scalability of the sensor network. In this paper, a new energy-efficient clustering technique has been proposed based on a genetic algorithm with the newly defined objective function. The proposed clustering method modifies the steady-state phase of the LEACH protocol in a heterogeneous environment. The proposed objective function considers three main clustering parameters such as compactness, separation, and number of cluster heads for optimization. The simulation result shows that the proposed protocol is more effective in improving the performance of wireless sensor networks as compared to other state-of-the-art methods, namely SEP, IHCR, and ERP.