CHS-GA: An Approach for Cluster Head Selection Using Genetic Algorithm for WBANs

Abstract

Wireless Body Area Networks (WBANs), an advancing technology in the field of pervasive healthcare monitor patients ubiquitously and provide real-time feedback. Data communication consumes more energy than data processing in WBANs. As it is nearly impractical to replace or recharge the dead sensor nodes, it has become a major concern to overcome issues related to data communication in WBANs that affect network lifetime and energy consumption. In this paper, we propose an efficient algorithm for cluster head selection using genetic heuristics for enhancing network lifetime and harnessing energy consumption of the sensor nodes. It uses genetic heuristics and divides the network into clusters. A cluster head is chosen for inter and intra-cluster communication. Clustering is a feasible solution as it reduces the number of direct transmissions from source to sink. It enhances network lifetime and reduces energy consumption as there is inverse relationship between the two, i.e, less the energy consumption more is the network lifetime. The proposed algorithm is also analyzed mathematically in terms of time complexity, overhead and fault tolerance which reveals that our algorithm outperforms the existing techniques such as AnyBody and HIT in terms of energy efficiency and network lifetime.