Fog-Assisted Energy-Efficient Framework for Wireless Body Area Network

Le résumé fourni par la source

Wireless Body Area Networks (WBAN) constitute a vital technology in modern healthcare practices as they enable continuous patient monitoring and facilitate intelligent medical applications. A lot of research has been done for energy-efficient communication protocols, clustering approaches and cloud-based data analysis for improving the WBAN performance. Despite all of the efforts, the current WBAN architectures are still facing challenges related to high energy consumption, increased latency, and limited scalability due to continuous data transfer as well as lack of intelligent-based decision making at the edge of the network. One major research gap is the lack of energy-aware clustering algorithms, adaptive data aggregation and machine learning-based frameworks for joint optimization of energy efficiency, reliability and security, thus this work proposes a fog-assisted energy-efficient WBAN framework, which incorporates hybrid cluster head (CH) selection, based on least distance and residual energy, adaptive data aggregation, anomaly detection based on Random Forest, and heuristic optimization for better energy operations. The proposed methodology includes a simulation-based evaluation of WBAN communication, which is based on energy consumption, delay and throughput. this study offers insights into (i) the design of an intelligent, energy-aware hybrid clustering model, accompanied by machine learning, for energy management. (ii) Additionally, it evaluated energy consumption, performance enhancement, and fog-based processing. The simulation findings demonstrated that the hybrid clustering model is effective in considerably reducing energy costs, reducing latency, and improving throughput in comparison to traditional WBAN schemes, resulting in a sustainable and smart solution for ongoing healthcare monitoring.

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