Predictive or Oblivious: A Comparative Study of Routing Strategies for Wireless Mesh Networks under Uncertain Demand

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.