  Passive monitoring utilizing distributed wireless sniffers is an effective
technique to monitor activities in wireless infrastructure networks for fault
diagnosis, resource management and critical path analysis. In this paper, we
introduce a quality of monitoring (QoM) metric defined by the expected number
of active users monitored, and investigate the problem of maximizing QoM by
judiciously assigning sniffers to channels based on the knowledge of user
activities in a multi-channel wireless network. Two types of capture models are
considered. The user-centric model assumes frame-level capturing capability of
sniffers such that the activities of different users can be distinguished while
the sniffer-centric model only utilizes the binary channel information (active
or not) at a sniffer. For the user-centric model, we show that the implied
optimization problem is NP-hard, but a constant approximation ratio can be
attained via polynomial complexity algorithms. For the sniffer-centric model,
we devise stochastic inference schemes to transform the problem into the
user-centric domain, where we are able to apply our polynomial approximation
algorithms. The effectiveness of our proposed schemes and algorithms is further
evaluated using both synthetic data as well as real-world traces from an
operational WLAN.
