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IMPACT ANALYSIS OF VARIOUS FACTORS ON NETWORK CAPACITY CONSTRAINTS IN WIRELESS MESH NETWORKS

Wireless mesh networks (WMNs) have gained considerable interest in expanding IEEE 802.11 networks to large-scale enterprise and community scenarios, primarily because its capability of providing wide-band ubiquitous network access to a significant number of users. However, the mesh nodes are typically limited in resources such as bandwidth, computation power and memory. Moreover, the shared wireless medium introduces the interference. In the context of the network capacity enhancement, we focus on using the techniques of multi-channel multi-radio (MCMR) and partially overlapped channels (POC) respectively; In the context of network Capacity, we focus on the measurementbased analysis and prediction of Capacity in an experimental way. Capacity limitation is one of the fundamental issues in wireless mesh networks. We start our investigation by addressing capacity improvement enabled by multiple radios and channels with a centralized scheduling approach. In a MCMR network, our objective is to find both dynamic and static channel assignments and corresponding link schedules that maximize the network capacity. Our results are based on measurements performed on our real-world mesh network test bed. In identifying the sources of packet loss, we developed a tool, Flow PaC, to collect flow-based statistics at different points in the system to understand the effects of the MAC layer parameters and the traffic attributes. We also explore the potential remedies for the system configuration and thereby improve the Capacity of wireless mesh networks. Prediction of network Capacity of WMNs at the level of individual flows in a small time granularity becomes very important, but is missing in the literature.. We measure Capacity using metrics of both the goodput and packet loss in short intervals of time for individual flows. We propose a model to predict the short-term Capacity for a user’s flow in WMNs. Based on the measurement results obtained from our tools Rater and Cal Medium, our model can achieve the prediction accuracy and avoid the complexity of the inter-dependency among the traffic, different system queues, interference, and MACinduced factors. The predicted results have a good match with those obtained from our real-world mesh network test bed for both single-flow and multiple-flow scenarios. We also demonstrate two application examples of utilizing our work: finding the bottleneck rate of a flow and admission control.