Enhancing Quality of Service in Wireless Mesh Networks through Service Discovery Optimization

Wireless Mesh Networks (WMNs) are an economical solution for achieving continuous connectivity in wide area networks (WANs) with support for mobility. Nonetheless, the increasing number of users in these networks has amplified competition for resources and services. Consequently, the likelihood of encountering issues like link bottleneck, data collisions, and channel interference at the time of locating and accessing services in WMNs has risen, leading to a decline in Quality of Service (QoS). Swift and efficient issue identification is now essential for optimizing the process of locating and accessing services in WMNs. In this upcoming paper, we introduce an innovative service discovery model designed to address the performance challenges in WMNs. Our proposed model seamlessly assimilates the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms, which we collectively denote as PSO-ACO. The PSO algorithm prioritizes network services, while the ACO algorithm efficiently establishes the most economical path for transmitters seeking requested services. Additionally, we develop and implement a Link Congestion Reduction (LCR) algorithm to determine the sum of service receivers have concurrent access to services. We employed a computer network simulator namely Network Simulator 2 (NS2) for evaluating our proposed algorithm in WMNs. In our performance evaluation, the PSO-ACO algorithm, the PSO algorithm, and the ACO algorithm exhibited average throughput performances of 95%, 70%, and 55%, respectively. Furthermore, the average service discovery delay for the PSO-ACO algorithm was 3 seconds, while the PSO and ACO algorithms had delays of 4.6 seconds and 5.5 seconds, respectively. In terms of service availability, the PSO-ACO algorithm, the PSO algorithm, and the ACO algorithm demonstrated average performances of 93%, 80%, and 71%, respectively.