Wireless Network Management

The convenience of wireless networking and lightweight handheld devices have led to a large-scale adoption of wireless technologies. Corporations, universities, hospitals, homes, and public places are deploying these networks at a remarkable rate. Many cities, such as Buffalo (MN), Ripon (CA), Philadelphia (PA), and Portland (OR), have deployed or are planning to deploy city-wide wireless networks.

On the other hand, wireless networks pose significant management challenges in the following ways. First, a wireless network is a complex system with many inter-dependent factors that affect its behavior. The factors include traffic flows, network topologies, network protocols, hardware, software, and most importantly, the interactions among them. The interactions among these factors are not well understood. Second, wireless interference has a profound impact on network performance. Due to its high variability and dependency on environmental conditions, how to effectively obtain and incorporate wireless interference into network management remains an open problem. Third, unlike wireline networks, which may use over-provisioning to reduce the impact of performance problems and network failures to a certain extent, over-provisioning in wireless networks is often not a solution. This is due to the limited wireless spectrum and the effects of wireless interference. As a result, wireless users experience various problems, such as lack of coverage, intermittent connectivity, poor performance and reliability. Network administrators lack tools to effectively configure, provision, diagnose, and optimize networks, so they often have to resort to manual trial-and-error.

In this research project, we aim to provide solutions for managing wireless networks, in particular, wireless LANs and wireless mesh networks. We are developing a systematic management framework that consists of measurement, modeling, and control. Our current focuses involve (i) developing flexible network models to estimate normal network behavior and perform what-if analysis, and (ii) designing effective control strategies (e.g., channel assignment, routing, and power control). We use Qualnet simulation and testbed experiments to demonstrate the effectiveness of our approaches. Qualnet simulator, developed by Scalable Networks is a very efficient and effective way of accessing the protocol design, while testbed experiments further help us to understand the performance in real networks.