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INTRODUCTION

Wireless mesh networks (e.g., Seattle wireless, MIT roofnet) have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. In a wireless mesh network, local access points and stationary wireless mesh routers communicate with each other and form a backbone structure which forwards the traffic between mobile clients and the Internet. Traffic routing plays a critical role in determining the performance of a wireless mesh network. Thus it attracts extensive research recently. The proposed approaches usually fall into two ends of the spectrum. On one end of the spectrum are the heuristic routing algorithms. Although many of them are adaptive to the dynamic environments of wireless networks, these algorithms lack the theoretical foundation to analyze how well the network performs globally (e.g., whether the traffic shares the network in a fair fashion). On the other end of the spectrum, there are theoretical studies that formulate mesh network routing as optimization problems. The routing algorithms derived from these optimization formulations can usually claim analytical properties such as resource utilization optimality and throughput fairness. In these optimization frameworks, 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. Such observations have significantly challenged the practicability of the existing optimizationbased routing solutions in wireless mesh networks. To address this challenge, we investigates the optimal mesh network routing framework which takes into account the dynamic nature of wireless traffic demand. To incorporate the traffic dynamics, the following two components are integrated into the framework. Traffic demand estimation which derives the traffic model of a wireless mesh networks. The model should be dependable at predicting the mean demand at long term, yet agile at containing often uncertain dynamics at short term. Routing optimization which distributes the traffic along different routes so that minimum congestion will be incurred even under dynamic traffic. The routing strategy should be effectively take into account the traffic demand estimation results. We present a traffic prediction method based on time-series analysis. This method derives future traffic demand baed on its historical data. The mean value of the predicted demand, together with its prediction error distribution, are used in establishing a statistical model for the traffic demand at a local access point.

FRAMEWORK OVERVIEW

Network Model A multi-hop wireless mesh network is considered, where local access points aggregate and forward the traffic from the mobile clients that are associated with them. They communicate with each other, also with the stationary wireless routers to form a multi-hop wireless backbone network. This network forwards the user traffic to the gateway access points which are connected to the Internet. Here, local access point, gateway access point and mesh router are collectively called mesh nodes. Figure 1: Illustration of wireless mesh network Traffic Model The gateway access points are the sources of all incoming traffic and the destinations of all outgoing traffic of a mesh network. A virtual gateway access point introduced into the network coonects to each gateway access point with a virtual edge. The virtual edge could be regarded as a wireline link with unlimited capacity which does not interfere with any of the wireless transmission. Schedulability The aggregated flow rate is said to be schedulable, if there exists a stable schedule that ensures every packet transmission with a bounded delay. We assume that the channel capacity, which is maximum data that can be carried in unit time, is the same for all links.

TRAFFIC ESTIMATION

Our goal is to (1) develop a reliable estimation method that is able to predict the aggregated traffic demand of an access point based on its historical data, and (2) develop a statistical model to characterize the prediction results.

PUBLICATIONS AND PRESENTATIONS

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand Mobile Networks and Applications (MONET), 2008. Liang Dai, Yuan Xue, Bin Chang, Yanchuan Cao, and Yi Cui [pdf] Integrating Traffic Estimation and Routing Optimization for Multi-Radio Multi-Channel Wireless Mesh Networks IEEE INFOCOM, 2008. Liang Dai, Yuan Xue, Bin Chang, Yanchuan Cao, and Yi Cui [pdf] Throughput Optimization Routing under Uncertain Demand for Wireless Mesh Networks The Fourth IEEE International Conference on Mobile Ad-hoc and Sensor Systems(MASS), 2007. Liang Dai, Yuan Xue, Bin Chang, and Yi Cui [pdf]

Contact:
Prof. Yuan Xue
Vanderbilt University
Phone: (615) 322-2926

Last updated July 04, 2009