Double Cluster Head Heterogeneous Clustering for Optimization in Hybrid Wireless Sensor Network

The growth of ubiquitous and pervasive computing is largely derived from the contribution of Wireless Sensor Network (WSN) in several fields such as medicine, surveillance, computing etc. Optimization and load balancing in hybrid architecture is a difficult task in sensor network. Subsequently the significant improvement in energy efficiency has been achieved through clustering, this paper proposes a Mobile Double Cluster Head-Particle Swarm Optimization (MDCH-PSO) algorithm to enhance the network lifetime and load balancing in hybrid WSN. The objective of this paper is to improve lifetime and able to balance the load in the network. It is achieved by reducing the energy spent on monitoring the member nodes by Cluster Head (CH) and the energy spent on handling mobility. The proposed MDCH-PSO algorithm consists of four phases. They are cluster scheduling, CH election, mobility predicting and handover. In cluster scheduling phase, the member nodes are clustered to the heterogeneous sensor node called 'female node' based on the Received Signal Strength Indication. The 'male node' is elected based on the fitness value calculated using PSO algorithm. A fitness value is calculated based on the residual energy, node density, distance to female node and mobile speed of each node by the female node. The latency due to mobility prediction and handover is reduced when compared to LEACH-M algorithm. Simulation results show that MDCH-PSO outperforms than the standard LEACH-C, LEACH-M algorithm in improving the lifetime. The average residual energy has been improved by 13.9% than LEACH-M and 27% than LEACH-C algorithm. Average Delay is reduced by 29.6385% and 35.26% than LEACH-M and LEACH-C in MDCH-PSO algorithm respectively.