Optimizing Energy Efficiency in Wireless Body Area Networks through NEEMA: A Hybrid Meta-Heuristic Approach

Recent developments in Wireless Body Area Network (WBAN) technology have created a wide range of sensors capable of monitoring various physiological parameters in patients. However, the limited battery life of these sensors has constrained the potential of WBAN, highlighting the need for energy-efficient algorithms to address their substantial energy demands. To address this challenge, this paper introduces a novel Energy Efficient Hybrid Meta-Heuristic Approach (NEEMA) for WBANs. NEEMA combines the Tunicate Swarm Algorithm (TSA) and Genetic Algorithm (GA), collectively known as T-GA, to achieve high convergence and broad exploitation and exploration capabilities. The approach incorporates a clustering methodology to determine the Cluster Head (CH) using a fitness function derived from T-GA, which considers innovative parameters for conserving energy across the network. The study underscores the importance of multi-hop communication between patients equipped with sensor nodes, enabling data transmission to healthcare providers through multiple Relay Head (RH) nodes, transmitting data to subsequent RHs and ultimately to the central data sink. The process for selecting RHs mirrors that of CHs. Experimental results demonstrate that NEEMA outperforms existing algorithms and offers advantages for various WBAN applications.