QQAR: A Q-learning-based QoS-aware routing for IoMT-enabled wireless body area networks for smart healthcare

Recently, the Internet of Medical Things (IoMT) has gained broad acclaim in the academic and industrial sectors due to its use in integrating medical devices and healthcare systems. Wireless body area networks (WBANs) are a promising technology in smart healthcare for real-time patient monitoring and health data analysis by medical professionals. Efficient data transmission is essential in ensuring reliable and timely delivery of healthcare facilities. Healthcare services require reliable, delay -sensitive, energy- and congestion -aware communication, which makes it challenging to design a routing protocol. To address quality of services (QoS) in IoMT-enabled WBANs for healthcare, we propose Q -learning -based quality of services (QoS) aware routing (QQAR). Firstly, we used two -hop -based link reliability estimation to ensure link reliability from sources to sink nodes. Routing decisions using two -hop neighbor information guarantees successful packet delivery. Secondly, we differentiated the packets based on traffic priority, which assures the delivery of critical packets even in a congested network. Particularly, considering twohop node velocity with energy balancing enhanced the guarantee of timely packet delivery before deadlines. In addition, the multi -sink approach enhanced network reliability. Finally, we utilized Q -learning to select suitable neighbor nodes for packet forwarding in a decentralized fashion. A multi -sink load balance and congestion control mechanism adjusts the routing decisions in the QQAR to avoid congestion. Our simulation and comparison results showed that QQAR outperforms the existing routing protocols across various performance metrics under distinct network conditions.