EQRSRL: an energy-aware and QoS-based routing schema using reinforcement learning in IoMT

Internet of Medical Thing (IoMT) is an emerging technology in healthcare that can be used to realize a wide variety of medical applications. It improves people's quality of life and makes it easier to care for the sick individuals in an efficient and safe manner. To do this, IoMT leverages the capabilities of some new technologies including IoT, Artificial Intelligence, cloud computing, computer networks and medicine. Combining these technologies to monitor the patient's health conditions in real-time or semi-real-time is a critical challenge in IoMT. In this regard, one of the most crucial components of IoMT are network communication protocols that should provide a fast and reliable communication path between a connected biosensor to a patient and cloud computing environment. In this paper, we propose EQRSRL as an efficient routing mechanism for different types of IoMT applications. The aim of EQRSRL is to provide a reasonable level of Quality of Service (QoS) for IoMT traffics. To achieve this goal, it categorizes the network traffic into three classes and treats them differently concerning their QoS requirements. Moreover, EQRSRL divides the network environment into multiple zones to decrease the number of message exchange between the nodes. In order to compute optimal paths between the nodes, it considers QoS and energy metrics, and makes use of a reinforcement learning approach in path computation process. Simulation results show that the implementation of EQRSRL in IoMT is practical and leads to improvement of 82% in average energy consumption, 25% in end-to-end delay and 7% packet delivery ration in compared to the state-of-the-art routing techniques.