A Double Q-Learning Routing in Delay Tolerant Networks

Abstract

Delay tolerant networks (DTNs) are wireless mobile networks, where the nodes are sparse and end-to-end connectivity is rare. The intermittent connectivity in DTNs makes it challenging to efficiently deliver messages. Research results have shown that the routing protocol based on reinforcement learning can achieve a reasonable balance between routing performance and cost. However, how to predict the next hop of messages more accurately is still open. In this paper, Double Q-Learning Routing (DQLR) protocol is proposed, which investigates the routing selection of the next hop in a distributed manner and solves the overestimation problem by Double Q-Learning algorithm. Further, the intermediate value and dynamic reward mechanisms are proposed to adapt node mobility and network topology change, which improve the network performance. The simulation results show that DQLR protocol can increase the delivery ratio with a low overhead.