Optimization of routing algorithm in wireless mesh networks

Abstract

Wireless mesh networks (WMNs) have attracted increasing attention and deployment as a high-performance and low cost solution to last-mile broadband Internet access. Wireless mesh networks have emerged as a potential technology for next-generation wireless networking. WMNs have been widely accepted in the traditional application sectors of ad hoc networks because of their advantages over other wireless networks. With increasing demand for real-time services in the next generation wireless networks, quality-of-service (QoS) based routing offers significant challenges in WMNs. WMNs consist of mesh routers and mesh clients where fixed mesh routers form the multi-hop backbone of the network and it is assumed that each individual mesh client will follow the prescribed protocols. However, these mobile devices, owned by individual users, will likely do what is the most beneficial to their owners, i.e. act ‘selfishly’. Traffic routing plays a critical role in determining the performance of wireless mesh networks. Routing in any network has a great impact on the overall network performance, thus a routing protocol or an algorithm for WMNs should be carefully designed taking into account the specific characteristics of that network. In addition, in wireless networks, serious unfairness can occur between users if the issue is not addressed in the network protocols or algorithms. In this paper, we formulate the problem of routing as a network optimization problem, and present a general LP (linear programming) formulation for modeling the problem. We propose the optimized algorithm for known traffic demand and then explain the performance ratio for this. The routing algorithms derived from these formulations usually claim analytical properties such as optimal resource utilization and throughput fairness. Our simulation results demonstrate that our statistical problem formulation could effectively incorporate the traffic demand uncertainty in routing optimization, and its algorithm outperforms the algorithm which only considers the static traffic demand.