A Priority-Based Service Discovery Model Using Swarm Intelligence in Wireless Mesh Networks

Abstract

The ever increasing number of users in Wireless Mesh Networks (WMNs) setups consequently represents an upsurge in competitions for available services. Consequently, services are clogged and ran over WMNs, which further leads to poor Quality of Service (QoS). Quick and timely discovery of available services becomes an essential parameter in optimizing performance of WMNs. In this paper therefore, we present a Priority-based Service Discovery Model (PSDM) using Swarm Intelligence in WMNs. We use the Particle Swarm Optimization (PSO) algorithm to dynamically define and prioritize services supported by the network. Additionally, the Ant Colony Optimization (ACO) algorithm is used to choose the shortest path when each transmitter has to be searched to identify if it possesses the requested services. We have designed and implemented the PSDM using Network Simulator 2 (NS-2) tool. Consequently, we realized throughput of 80%, service availability of 96% in some instances, and an average delay of 1.8 ms.