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. Trade-offs exist in the completeness of information captured versus the deployment and operational cost of a passive monitor system. 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 knowledge of user activities in a multi-channel wireless network. Two capture models are considered. The first one, called the user-centric model assumes frame-level capturing capability of sniffers such that the activities of different users can be distinguished. The second one, called the sniffer-centric model only utilizes 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 a stochastic inference scheme that transforms the problem into the user-centric domain, where we are able to apply our polynomial approximation algorithms. The effectiveness of our proposed scheme and algorithms is further evaluated using both synthetic data as well as real-world traces from an operational WLAN.
In REF , we introduced a quality of monitoring (QoM) metric defined by the expected number of active users monitored, and investigated the problem of maximizing QoM by judiciously assigning sniffers to channels based on knowledge of user activities in a multi-channel wireless network.
On Quality of Monitoring for Multi-channel Wireless Infrastructure Networks
IEEE Trans. Mob. Comput.
IEEE Trans. Mob. Comput.
Computer Science