Conference Paper

Integrating Traffic Estimation and Routing Optimization for Multi-Radio Multi-Channel Wireless Mesh Networks

Vanderbilt Univ., Nashville
DOI: 10.1109/INFOCOM.2008.23 Conference: INFOCOM 2008. The 27th Conference on Computer Communications. IEEE
Source: IEEE Xplore

ABSTRACT Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best 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. This paper presents an integrated framework for network routing in multi-radio multi-channel wireless mesh networks under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By analyzing the traces collected at wireless access points, the traffic estimation component predicts future traffic demand based on its historical value using time-series analysis, and represents the prediction result in two forms - mean value and statistical distribution. The optimal mesh network routing strategies then take these two forms of traffic demand estimations as inputs. In particular, two routing algorithms are proposed based on linear programming which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate traffic dynamics in mesh network routing, where both algorithms outperform the shortest path algorithm in about 80% of the test cases.

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    • "crnt is the current load, L a est is the estimated load and w 1 , w 2 are the weights that are used in the calculation (we will give more details about the selection of w 1 , w 2 in the evaluation of our protocol). As far as the calculation of the estimated load L a est is concerned, we adopt the estimation method (based on historical data), proposed in [25]. In this approach the authors design a trace-based traffic model in order to predict the aggregated traffic demands of an AP in near future. "
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    • "To cope with traffic variability in WMNs, predictive routing schemes have been recently proposed, which model in real-time the traffic flows based on past behaviours, and optimize the routing strategy for the predicted traffic demands [15]. However, predictive routing can perform very poorly when the traffic demands are highly variable and hard to estimate with accuracy. "
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    [Show abstract] [Hide abstract]
    ABSTRACT: Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best 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 in practice, one must take into account the dynamic and uncertain nature of wireless traffic demand. There are two basic approaches to address the traffic uncertainty in network routing: (1) predictive routing which infers the traffic demand with maximum possibility based in its history and optimizes the routing strategy based on the predicted traffic demand and (2) oblivious routing which considers all the possible traffic demands and selects the routing strategy where the worst-case network performance could be optimized. This paper conducts a systematic comparison study of these two approaches based on the extensive simulation study over a variety of network and traffic scenarios. It identifies the key factors of the network topology and traffic profile that affect the performance of each routing strategy and provides guidelines towards the strategy selection in mesh network routing under uncertain traffic demands.
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