Abstract
The eHealth service has been considered a potential resource issue for industry and academia and is remarkably similar to a promising technology for continuous monitoring of biomedical signals in the human body. Indeed, it deployed modern digital technologies to maintain patient health data in digital environments such as the Internet of Things (IoT). In this vein, Wireless Body Area Networks (WBANs) are essential components of eHealth systems for early detection and successful treatment. Because sensor batteries in WBANs are usually operated and inconvenient to recharge, an energy-efficient resource allocation scheme is critical to extending the length of networks while still meeting the stringent quality of service requirements inherent in WBANs. As a result, this paper investigates resource allocation issues for WBAN. Our objective is to maximize energy efficiency by considering the effect of data transmission, relay selection, power consumption, and each sensor's energy constraints. Due to the current problems' sophistication, we present a Q-learning Agent (QLA) system to obtain the optimal allocation approach. A Q-Sensor Network Management Unit (Q-SNMU) is implemented and designed to synchronize all body sensors appropriately. The results show that the proposed scheme works well and that the proposed Q-SNMU approach is very efficient at running.