Optimal Routing With Traffic Prediction for Wireless Mesh Networks

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best routing solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. The main objective of this paper is to analyze the dynamic behavior of aggregated traffic in the wireless mesh backbone network and investigate throughput optimization routing strategies under dynamic traffic. The goal is to maximize the throughput of aggregated flows while maintaining the fairness among different local access points. By studying the traces from operational wireless networks, this paper presents a traffic prediction method based on time-series analysis. This method derives a statistical model for future traffic demand based on its historical data. This paper further identifies an optimization framework which integrates the statistical traffic model into a tractable throughput optimization routing problem. The trace-driven simulation study demonstrates that our integrated prediction and optimization framework of mesh network routing can effectively incorporate the traffic demand uncertainty in routing optimization.