Adaptive Reinforcement Learning-Based Routing Protocol for Wireless Multihop Networks

This paper presents a research on a topic of development of an adaptive packet routing scheme for wireless multihop networks, based on reinforcement learning optimization algorithm. A brief overview of classical approaches for data routing in multihop networks is provided, emphasizing main drawbacks of such algorithms, caused by ineffective hop count routing metric used in traditional multihop routing algorithms. Then, an approach based on reinforcement learning theory is presented, that has a potential to select more effective routes, relying on feedback information from neighboring nodes. An algorithm based on reinforcement learning optimization function is proposed, as well as additional functions are introduced for initial route weights distribution and dynamic route probability selection, depending from the current packet loss ratio (PLR) and receive signal strength indicator (RSSI) factors. The elaborated adaptive routing scheme then has been tested in real wireless multihop topology, where a programming implementation of the proposed algorithm - RLRP protocol, showed better routing performance characteristics in terms of PLR and RRT (Route Recovery Time), compared to a traditional improved proactive scheme of wireless multihop routing, implemented in widely used B.A.T.M.A.N. (Better Approach to Mobile Ad hoc Networking) protocol.