An adaptive intelligent thermal-aware routing protocol for wireless body area networks

Wireless Body Area Networks (WBANs) have gained significant attention due to their widespread applications in healthcare monitoring, sports performance tracking, military communication, and remote patient care. These networks consist of wearable or implanted sensor nodes continuously collecting and transmitting physiological data, requiring an efficient and reliable communication framework. However, the unique challenges of WBANs, such as limited energy resources, dynamic network topology, and high sensitivity to node temperature, necessitate specialized routing strategies. Traditional routing protocols, which often prioritize shortest-path selection, tend to create traffic congestion and overheating in specific nodes, leading to early network failures and reduced overall performance. To address these issues, this paper proposes an intelligent, temperature-aware, and reliability-based routing approach that enhances the overall efficiency and stability of WBANs. The proposed method operates in two phases: (1) network setup and intelligent path selection and (2) dynamic traffic management and hotspot avoidance. In the first phase, sensor nodes exchange vital network status information, including residual energy, node temperature, link reliability, and delay, to build an optimized network topology. Instead of relying solely on shortest-path routing, a multi-criteria decision-making algorithm is employed to select the most efficient paths, prioritizing those that balance energy consumption, temperature regulation, and communication stability. This prevents excessive energy depletion in specific nodes and avoids forming traffic bottlenecks. The system continuously monitors real-time network conditions in the second phase, dynamically rerouting traffic away from overheated or energy-depleted nodes. This ensures that critical sensor data is reliably delivered while extending the network’s lifetime. Simulation results demonstrate the superiority of the proposed approach compared to existing methods. The proposed method improves throughput by 13% and reduces end-to-end delay by 10%. Additionally, it achieves a 25% reduction in energy consumption. The proposed method also significantly reduces the normalized routing load by 30%.