Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network

Abstract The constraints on the battery resources of sensor nodes have been the major stumbling block in achieving the network longevity and in exploring the potential of Wireless Sensor Network (WSN) to the maximum level. A plethora of research work has implemented multitudinous optimization techniques for the Cluster Head (CH) selection in homogenous WSN. However, for Heterogeneous WSN (HWSN), the CH selection is still left with a wide scope for further improvement for its exploitation capabilities. In this paper, Genetic Algorithm-based Optimized Clustering (GAOC) protocol is designed for optimized CH selection by integrating the parameters of residual energy, distance to the sink and node density in its formulated fitness function. Furthermore, to pact with the Hot-Spot problem, and to shorten the communicating distance from the nodes to the sink, Multiple data Sinks based GAOC (MS-GAOC) is proposed. The empirical investigations of MS-GAOC is carried out with protocols developed to operate with multiple data sinks so as to have fair comparative analysis. It is inferred from the simulation analysis that the GAOC and MS-GAOC outperform the state-of-the-art protocols on the benchmark of different performance metrics viz. stability period, network lifetime, number of dead nodes against rounds, throughput and network’s remaining energy. The proposed protocols are expected to play a salient role in monitoring of hostile applications, i.e., forest fire detection, early detection of volcanic eruptions, etc.