Adaptive Resource Scheduling Algorithm for Multi-Feature Optimization in Personalized Wireless Body Area Networks

Abstract

Wireless Body Area Network (WBANs), as a crucial technology in the field of healthcare monitoring, also plays a significant role in personal consumer electronics (CE). However, existing solutions have yet to effectively address challenges such as personalized demands, data heterogeneity, and dynamic link variations caused by diverse environmental factors. To address these issues, this study proposes a novel approach based on the IEEE 802.15.6 standard, which incorporates personalized node characteristics to cater to individual needs and adapt to node heterogeneity. Furthermore, a dynamic scheduling mechanism for node data is introduced, leveraging multi-feature environmental factors to enhance the practicality of WBAN systems. Finally, for real-time scheduling of emotional anomaly data, this study incorporates skin conductance nodes for the first time to evaluate emotional factors, ensuring the efficient transmission of urgent data. Theoretical analysis and simulation results demonstrate that the proposed approach significantly outperforms traditional methods in terms of energy efficiency, latency, throughput, and resource balance.