<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-2.838901pt" id="M1" height="14.2383pt" version="1.1" viewBox="-0.0657574 -11.3994 12.5675 14.2383" width="12.5675pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-82" d="M699 368C699 549 574 666 407 666C186 666 23 488 23 277C23 113 129 -3 288 -13L307 -26C431 -111 501 -139 533 -147C559 -154 613 -163 658 -164L666 -141C597 -111 507 -66 430 -11L416 -1C580 42 699 190 699 368ZM601 371C601 227 518 54 381 22L354 40L278 24C175 47 120 145 120 269C120 451 235 631 398 631C540 631 601 521 601 371Z"/></g> -Learning-Based High Credibility and Stability Routing Algorithm for Internet of Medical Things - Wei, Kefeng; Zhang, Lincong; Jiang, Xin; Guo, Yi (Wireless Communications and Mobile Computing, 2020)
With the outbreak of COVID-19, people’s demand for using the Internet of Medical Things (IoMT) for physical health monitoring has increased dramatically. The considerable amount of data requires stable, reliable, and real-time transmission, which has become an urgent problem to be solved. This paper constructs a health monitoring-enabled IoMT network which is composed of several users carrying wearable devices and a coordinator. One of the important problems for the proposed network is the unstable and inefficient transmission of data packets caused by node congestion and link breakage in the routing process. Based on these, we propose a Q-learning-based dynamic routing selection (QDRS) algorithm. First, a mathematical model of path optimization and a solution named Global Routing selection with high Credibility and Stability (GRCS) is proposed to select the optimal path globally. However, during the data transmission through the optimal path, the node and link status may change, causing packet loss or retransmission. This is a problem not considered by standard routing algorithms. Therefore, this paper proposes a local link dynamic adjustment scheme based on GRCS, using the Q-learning algorithm to select the optimal next-hop node for each intermediate forwarding node. If the selected node is not the same as the original path, the chosen node replaces the downstream node in the original path and so corrects the optimal path in time. This paper considers the congestion state, remaining energy, and mobility of the node when selecting the path and considers the network state changes during packet transmission, which is the most significant innovation of this paper. The simulation results show that compared with other similar algorithms, the proposed algorithm can significantly improve the packet forwarding rate without seriously affecting the network energy consumption and delay.