Reinforcement Learning and Heuristic Algorithms for Efficient Routing Protocols in Mobile Ad-Hoc Networks: An Advanced Approach

Abstract

In mobile ad-hoc networks nodes are freely move and communicate with each other in its frequency range wirelessly. Due to dynamic topology the routes are not stable. Hence transmitting data packets among nodes is one of the major challenge. The 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, route shortness and also route stability should be taken into consideration. More than a decade ago, that our approach to Artificial Intelligence has been widely accepted as a new development in the field of routing protocols [1,2]. In order to select the robust routing process, the reinforcement learning was used to make the best choice among the neighbor nodes at any moment to transmit data packets from source to destination. It predicts the behavior pattern of the nodes in relation to the target node through using reinforcement learning. The proposed method adopts Q-learning algorithm which has more homogeneity to estimate the value of actions [3].