Chapter

Wireless Mesh Network Routing Under Uncertain Demands

DOI: 10.1007/978-1-84800-909-7_7

ABSTRACT Traffic routing plays a critical role in determining the performance of a wireless mesh network. Recent research results usually
fall into two ends of the spectrum. On one end are the heuristic routing algorithms, which are highly adaptive to the dynamic
environments of wireless networks yet lack the analytical properties of how well the network performs globally. On the other
end are the optimal routing algorithms that are derived from the optimization problem formulation of mesh network routing.
They can usually claim analytical properties such as resource use optimality and throughput fairness. However, traffic demand
is usually implicitly assumed as static and known a priori in these problem formulations. In contrast, 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, 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 optimal mesh network routing
(1) predictive routing that 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 that considers all the possible traffic demands and
selects the routing strategy where the worst-case network performance could be optimized. This chapter provides an overview
of the optimal routing strategies for wireless mesh networks with a focus on the above two strategies that explicitly consider
the traffic uncertainty. It also identifies the key factors 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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    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.
    Sensor, Mesh and Ad Hoc Communications and Networks, 2008. SECON '08. 5th Annual IEEE Communications Society Conference on; 07/2008
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    ABSTRACT: Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. Network routing plays a critical role in determining the performance of a wireless mesh network. To study the best mesh network routing strategy which can maximize the network throughput while satisfying the fairness constraints, existing research proposes to formulate the mesh network routing problem as an optimization problem. These works usually make ideal assumptions such as known static traffic input. Whether they could be applied for practical use under the highly dynamic and uncertain traffic in wireless mesh network is still an open issue. The main objective of this paper is to understand the effects of traffic uncertainty in wireless mesh networks and consider these effects in throughput maximization routing. It identifies the appropriate optimization framework that could integrate the statistical user traffic demand model into a tractable throughput maximization problem. The trace-driven simulation study demonstrates that our algorithm can effectively incorporate the traffic demand uncertainty in routing optimization, and outperform the throughput maximization routing which only considers static traffic demand.
    Mobile Adhoc and Sensor Systems, 2007. MASS 2007. IEEE Internatonal Conference on; 11/2007
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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 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.
    INFOCOM 2008. The 27th Conference on Computer Communications. IEEE; 05/2008

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