Optimized Reinforcement Learning Based Multipath Transfer Protocol in Wireless Mesh Network

Abstract

Multiple radios working on different channels are used in Wireless Mesh Networks (WMNs) to improve network performance and reduce Energy Consumption (EC). Effective routing in Backbone WMNs is where each cross-section switch is well-organized with multiple Radio Interfaces (RI), and a subset of hubs is occupied as a gateway to the Internet. Most routing methods decrease the forward overheads by evolving one dimension, <i><i>e.g</i>.</i>, hop count and traffic proportion. With that idea, while considering these dimensions together, the complexity of the routing issue increases drastically. Consequently, an effective EC routing method considers a few performances simultaneously, and the requirement of MRC around the gateways is also considered. In this paper, the proposed Reinforcement Learning (RL) method based routing selection on MPR communication directs the network traffic in WMNs. Here the radio routing path selects the channel depending on the optimized node where optimization is agreed by Particle Swarm Optimization (PSO) technique. This aims to reduce the EC by switching states and utilizing efficient routing with the reduction in traffic demand. Experimental results showed better performance of throughput and EC compared with existing work.