A Q-Routing Protocol Using Self-Aware Approach for Mobile Ad hoc Networks

Abstract

Mobile Ad hoc Networks (MANET) are self-organized networks that are characterized by dynamic topologies in time and space. This creates an instable environment, where classical routing approaches cannot achieve high performance. Thus, adaptive routing is necessary to handle the challenges in MANETs. Furthermore, it is necessary for nodes to be self-aware i.e., able to discover neighbors, links and paths when needed. This paper proposes a new adaptive Mobile Ad hoc Networks (MANET) routing algorithm to find and maintain paths that provide the needed Quality of Service (QoS) for network traffic using a low-complexity bio-inspired learning paradigm. It combines the self-aware approach in Cognitive Packets Network (CPN) with a Q-routing inspired path selection mechanism. CPN is a distributed adaptive routing protocol that uses three types of packets: Smart Packets for route discovery, Data Packets for carrying data payload and Acknowledgments to bring back feedback information for the Reinforcement Learning reward function. The research defines a Q-routing reward function as a combination of high stability and low delay path criteria to discover long-lived routes without disrupting the overall delay. The algorithm uses Acknowledgment-based feedback for Q-routing to make routing decisions that adapt on line to network changes allowing nodes to learn efficient routing policies. Simulation Results show how the reward function handles the network changing topology to select paths that improve QoS delivered.