Dynamic Cluster Head Selection based Algorithm to Improve Energy Efficiency and Reliability of Wireless Body Area Network for Healthcare Purposes

Wireless Body Area Networks (WBANs) are essential for real-time healthcare applications because they enable the permanent monitoring of patients' vital health indices. However, energy efficiency, reliability and fault tolerance to varying network dynamics still face significant challenges, especially in resource constrained environments, such as health care. A Dynamic Cluster Head Selection (DCHS) algorithm is proposed in this paper that can regulate energy consumption, and enhance data transmission efficiency via dynamic changes in the network conditions. The weighted scoring mechanism of the suggested DCHS algorithm, which incorporates several crucial parameters—node mobility, residual energy, node density, distance to the sink, and the priority of healthcare datasets it apart from other approaches. This novel scoring method balances energy usage among nodes and guarantees that highpriority health data is supplied with the least amount of latency and packet loss. Furthermore, by dynamically redistributing cluster head roles according to energy thresholds and duty cycling, the DCHS algorithm prevents premature node failures and adds an energyaware cluster head rotation mechanism that proactively increases network lifespan. The performance of the proposed algorithm in terms of the network lifetime, data delivery rate, latency, and energy consumption is then assessed, and simulated on the NS-3 platform. With, they also achieve substantial improvements with data delivery rate of 95–98% and latency up to 10–20% lower, increased network lifetime up to 20–30%, and energy saving up to 15–25%. Additionally, the algorithm gets a success rate of 98–99% to retrieve high priority healthcare information and lower packet loss by 2–5%. These results highlight the DCHS algorithm's distinctiveness and potency as a reliable and energy-efficient solution for WBANs in the healthcare industry, addressing important gaps in real-time adaptation, energy management, and reliability. This work paves the way to more intelligent and robust healthcare monitoring systems by using a dynamic and healthcare-focused approach.