ABSTRACT Wireless Body Area Networks (WBANs) play a vital role in real‐time healthcare monitoring by enabling continuous acquisition and transmission of physiological data through wearable and implantable sensor nodes. However, their practical deployment is constrained by critical challenges such as limited energy resources, frequent topology variations caused by body movements, high communication overhead, and reduced network lifetime, which collectively affect reliable data transmission in long‐term medical applications. To address these issues, this work proposes an energy‐efficient hybrid clustering and routing protocol for WBANs based on the integration of an Adaptive Binary Bird Swarm Optimization Algorithm (ABBSOA) and an Enhanced Golden Eagle Optimization Algorithm (EGEOA). ABBSOA is employed for optimal cluster formation and dynamic cluster head (CH) selection by jointly considering residual energy, link quality, and communication cost, thereby ensuring balanced energy utilization among sensor nodes. Subsequently, EGEOA is utilized to establish reliable and energy‐aware multihop routing paths, minimizing transmission overhead and improving data delivery reliability. The effectiveness of the proposed ABBSOA–EGEOA protocol is validated through extensive simulations under two distinct scenarios and compared with state‐of‐the‐art WBAN protocols, including MT‐MAC, DHCO, ALOC, DECR, EHCRP, and MGWO. Simulation results demonstrate that the proposed approach achieves higher throughput, improved packet delivery ratio, reduced end‐to‐end delay, lower energy consumption, and a significantly extended network lifetime. Overall, the proposed protocol enhances energy efficiency, reliability, and scalability, making it well‐suited for sustainable and long‐term WBAN‐based healthcare monitoring applications. The ABBSOA‐EGEOA framework significantly extends network longevity, surpassing ALOC, DECR, EHCRP, and M‐GWO by 25%, 16.27%, 11.11%, and 6.38%, respectively. These results confirm that the proposed protocol enhances energy efficiency, reliability, and scalability in WBAN environments.
Dinesh et al. (Thu,) studied this question.