Topology based adaptive hybrid multicast routing in mobile ad hoc networks

In this paper, we propose topology based adaptive hybrid multicast routing (THMR) mechanism in mobile ad hoc networks. With the help of technique derived from Computational Intelligence (CI) discipline, Topology Change Rate (TCR) is predicted based on link behavior and node mobility for both proactive and reactive routing region in MANETs. We design a Reinforcement Learning (LR) based Q-Learning (QL) algorithm to tune the routing period (RP) in accordance with topology change rate. Simulation evaluation for Packet Delivery Ratio (PDR), and control overhead has been performed in NS-2. We observe that THMR outperforms other standard protocols for performance parameters like Packet Delivery Ratio, and Control overhead.