Sequential learning in wireless monitoring

Past

Abstract: Passive monitoring is a technique where a dedicated set of hardware devices called sniffers, are used to monitor activities in wireless networks. These devices capture transmissions of wireless devices or activities of interference sources in their vicinity, and store packet level or PHY layer information in trace files, which can be analyzed distributively or at a central location. Since most, if not all, infrastructure networks utilize multiple contiguous or non-contiguous channels or bands, an important issue is to determine which set of frequency bands each sniffer operates on to maximize the total amount of information gathered. In this talk, we consider the problem of optimally assigning p sniffers to K channels to monitor the transmission activities in a multi-channel wireless network. The activity of users is initially unknown to the sniffers and is to be learned along with channel assignment decisions. We devise efficient sequential learning approaches and address practical constraints including channel switching time, computation costs and non-stationary network conditions. Bio: Rong Zheng received her Ph.D. degree from Dept. of Computer Science, University of Illinois at Urbana-Champaign and earned her M.E. and B.E. in Electrical Engineering from Tsinghua University, P.R. China. She is an associate professor in Dept. of Computer Science, University of Houston. Rong Zheng's research interests include network monitoring and diagnosis, cyber physical systems, and sequential learning and decision theory. She is the recipient of the National Science Foundation CAREER Award in 2006, and University of Houston research excellence award in 2010. She serves on the technical program committees of several leading networking conferences, and is the program co-chair of WASA'12 and CPSCom'12.