RLENS: RL-based Energy-Efficient Network Selection Framework for IoMT

With the emergence of smart health (s-health) applications and services, several requirements for quality have arisen to foresee and react instantaneously to emergency circumstances. Such requirements demand fast-acting wireless networks while adapting to various types of applications and environment dynamics, encouraging network operators to leverage the spectrum of wireless signals across various radio access networks. Yet, this requires implementing intelligent network selection schemes that account for heterogeneous networks characteristics and applications' QoS requirements. Thus, this paper tackles this problem by adopting an intelligent Reinforcement Learning (RL)-based network selection scheme. Specifically, we leverage edge computing capabilities to implement an efficient user-centric network selection algorithm at the Internet of Medical Things (IoMT) level to adjust the compression ratio and select the most suitable radio access network (RAN) to transfer the acquired data while considering patient state, battery life and networks dynamics. Our results demonstrate the efficiency of the proposed approach in outperforming the state-of-the-art techniques in terms of battery life by more than 500% while reaching almost 85-90% of the optimal algorithm's performance in delay and distortion.