An Optimized Weighted Node Ranking Scheme for Channel Assignment in Wireless Mesh Networks using the Genetic Algorithm

Abstract

The widespread of Internet of Things (IoT) increases the reliance on Multi-Radio Wireless Mesh Networks (MR-WMN). This kind of networks provides a support to many IoT applications like smart grids, intelligent transportation systems and health monitoring systems. Therefore, the Quality of Service (QoS) becomes of great importance. This can be achieved by assigning the channels among links within the WMNs in a way that improvs the network throughput and capacity while maintaining a fair resource allocation. Although several studies have tried to make such trade-off, they overlook the dynamic nature of WMNs. Such dynamicity is resulted from user mobility which causes rapid changes in network topology and invalids the channel assignment that is currently in place. The static calculation of different network parameters involved in the channel assignment becomes invalid which leads to unfair channel allocation. As such, this study addresses the compatibility issue by bringing about a dynamic approach for weighing the network parameters calculation relying on the current state of the topology. The study proposes a Genetic Algorithm-Based Node Weighting scheme for channel assignment in the WMN (GA-WNR-CA). If a change is happened, the proposed algorithm can adapt and recalculate the weights accordingly. Weights calculation is carried out intelligently using the Genetic Algorithm that search for the optimal value. Once calculated, the network parameters are updated, and based on which the need for new assignment is determined. The experimental results show that the proposed algorithm outperformed those that have been proposed by related works, which conforms the efficacy of the dynamic approach adopted by the algorithm.