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

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.

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

“…In addition, although we could know the traffic demand (with the corresponding energy impact), it is difficult to predict it due to its highly dynamic nature, which should be considered when applying routing solutions. For example, tools for network routing under dynamic data demand are shown in [11], where the traffic is predicted by studying the traces collected at APs by means of time series analysis; and a network-based routing algorithm [12] and a meta-director framework [13] were developed to obtain optimal paths in cloud computing massive infrastructures in order to minimise the energy consumed by the users' requests.…”

Harnessing Traffic Uncertainties in Wireless Mesh Networks—A Stochastic Optimization Approach

“…Oblivious routing schemes fall into this category. The second category, which is in analogy of the general stochastic optimization techniques, alternatively pursues the expected utility maximization [10][11][12][13][14]. Our work falls into this category.…”

“…Nevertheless, in our work, the traffic demand is dynamic and may even appear zero sometime. Another related work is [10] where the throughput maximization routing is considered. The traffic dynamic in wireless mesh networks is well addressed.…”

Integrating traffic estimation and dynamic channel reconfiguration in Wireless Mesh Networks

“…Various centralized heuristic algorithms for CA are proposed and evaluated in [2], [15], [16], and [17]. The work in [18] proposes to integrate trafſc estimation with routing decision. They have proposed an Auto Regressive Moving Average (ARMA)-based prediction mechanism to estimate the future trafſc and use it to optimally route data in an MC-MR WMN.…”