Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms

Abstract

Mobile ad hoc networks (MANETs) consist of a set of nodes which can move freely and communicate with each other wirelessly. Due to the movement of nodes and unlike wired networks, the available routes used among the nodes for transmitting data packets are not stable. Hence, proposing real-time routing protocols for MANETs is regarded as one of the major challenges in this research domain. Algorithms compatible with the changes created in the network due to the nodes' movements are of high significance. For reducing data packet transmission time among nodes, not only should route shortness be considered but also route stability should be taken into consideration. Since available factors in different environments have specific behavior patterns especially in human environments, the parameters of link stability and route shortness were taken into consideration and the reinforcement learning was used to propose a method so as to make the best choice among the neighbors at any moment to transmit a packet to the destination. That is, the proposed method was aimed at predicting the behavior pattern of the nodes in relation to the target node through using reinforcement learning. The proposed method used Q-learning algorithm which has more homogeneity to estimate the value of actions. Simulation results in OPNET demonstrate the superiority of the proposed scheme over conventional MANET routing methods.