Abstract
Wireless Body Area Networks (WBANs) have the potential to absolutely change the healthcare enterprise through enabling remote patient care and continuous fitness monitoring. However, restrained power assets and device malfunctions offer giant challenges to their actual use. This survey explores the potential of federated gaining knowledge of (FL) and device getting to know (ML), extra specifically DQNERP (Deep Q-Network Enhanced Routing Protocol), as treatments.
We gift an in depth evaluation of cutting-edge electricity-performance approaches in WBANs and look into how DQNERP would possibly maximize battery existence through optimizing records processing, commune, and resource allocation at the same time as taking packet loss, put off, and node energy degrees into consideration. We then discover FL, a unique approach to privacy safety that helps go-device collaborative mastering and enhances fault tolerance via anomaly detection and predictive protection. We advise a future research path that harnesses the combined strengths of ML, significantly DQNERP, and FL to together optimize power performance and fault tolerance in WBANs by using severely analyzing the modern traits and identifying essential research gaps. In end, we discuss the limitations and interesting prospects on this fascinating area, clearing the direction for reliable, long-lasting, and expandable WBAN implementations and in the end figuring out the actual capacity of "It's Okay" WBANs to convert healthcare ultimately.