A Comprehensive Review of Node Coordinator Placement Strategies in WBANs

Abstract

Wireless Body Area Networks (WBANs) have emerged as a transformative technology for healthcare, fitness monitoring, and biomedical applications, enabling real-time collection and transmission of physiological data through low-power sensor nodes deployed on or inside the human body. The performance and efficiency of WBANs heavily depend on the optimal placement of the coordinator node, which serves as a central hub for data aggregation and communication. Inefficient placement may lead to high energy consumption, reduced reliability, increased latency, and frequent communication failures due to dynamic body movements. This paper presents a comprehensive review of node coordinator placement strategies in WBANs, focusing on static, dynamic, hybrid, optimization-based, and machine-learning-driven approaches. Each strategy is analyzed in terms of energy efficiency, reliability, latency reduction, and adaptability to body posture changes. Comparative insights are drawn from simulation results and practical implementations, highlighting trade-offs between algorithmic complexity and real-time adaptability. The review also explores open challenges such as security, scalability, and interoperability, while identifying promising research directions involving artificial intelligence, energy harvesting, WBAN ecosystems. The study aims to provide researchers and practitioners with a structured understanding of existing algorithms and to inspire innovative solutions for next-generation WBANs.