Energy-aware and trust-based cluster head selection in healthcare WBANs with enhanced GWO optimization

This paper describes a comprehensive methodology for improving Wireless Body Area Networks (WBANs) in healthcare systems using Enhanced Gray Wolf Optimization (GWO). The methodology begins with WBAN initialization and the configuration of critical network parameters. To improve network performance and trustworthiness, direct trust calculations, historical trust , and energy trust, as well as energy consumption models based on distance and transmission type, are integrated. The use of an Enhanced GWO approach makes it easier to select optimal cluster heads, guided by a customized fitness function that balances trust and energy efficiency. This work has been carried on a PC with 16 GB RAM using MATLAB R2022b tool for simulation purpose. The methodology outperforms existing methods in terms of throughput, computation time, and residual energy. This promising methodology provides improved data routing, energy efficiency, and trustworthiness, making it a valuable asset in WBAN-based healthcare systems.