An effective technique to monitor activities in wireless infrastructure networks

The problem of maximizing QoM in multi-channel infrastructure wireless networks with different a priori knowledge. Two different models are considered, which differ by the amount (and type) of information available to the sniffers. Wireless usage spans a diverse set of QoS requirements from best-effort data services, to VOIP and streaming applications. The task of managing the wireless infrastructure is made more difficult due to the additional constraints posed by QoS sensitive services. Monitoring the detailed characteristics of an operational wireless network is critical to many system administrative tasks including, fault diagnosis, resource management, and critical path analysis for infrastructure upgrades. wire side monitoring using SNMP and base station logs since it reveals detailed PHY (e.g., signal strength, spectrum density) and MAC behaviors (e.g, collision, retransmissions), timing information (e.g.,backoff time), which are often essential for wireless diagnosis. The architecture of a canonical monitoring system consists of three components: 1) sniffer hardware, 2) sniffer coordination and data collection, and 3) data processing and mining. The implied optimization problem is NP-hard, but a constant approximation ratio can be attained via polynomial complexity algorithms. The sniffer-centric model, devise stochastic inference schemes to transform the problem into the user-centric domain, they apply our polynomial approximation algorithms. The effectiveness of our proposed schemes and algorithms is further evaluated using both synthetic data real world traces from an operational WLAN.