A new particle swarm optimization for wireless mesh routing protocol

Abstract

The Particle swarm optimization is based on a social-psychological model of social influence and social learning for finding optimal regions of search space in particles population. The objectives of this research are to explore and build a new method of particle swarm optimization for wireless mesh routing protocol. Our system is built as an extension to Optimized Link State Routing (OLSR). Among proactive protocols in wireless mesh networks (WMNs), OLSR has been chosen mainly because it is a link state type protocol that guarantees better performances and a better network control than distance-vector protocol. For PSO, we propose a new method of sigmoid increasing inertia weight to improve the speed of convergence and maximum optimum solution in the multidimensional space. Four standard non-linear benchmark functions are used to confirm its validity using MATLAB. From results, it shows that PSO with sigmoid increasing inertia weight (PSO-SIIW) gives better performance. The PSO_SIIW will use for multipoint relays (MPRs) calculation in OLSR. MPRs are used to flood control messages from a node into the network while reducing the number of retransmissions that will occur in a region. Thus, the concept of MPR is an optimization of a classical flooding mechanism. Each node in the network selects, independently, its own set of MPRs among its symmetric 1-hop neighborhood. We test the propose algorithm in network simulator 2 (ns2). Based on the results, we proven that PSO-SIIW algorithm can be applied to MPR calculation in OLSR.