TY - GEN
T1 - On quality of monitoring for multi-channel wireless infrastructure networks
AU - Chhetri, Arun
AU - Nguyen, Huy
AU - Scalosub, Gabriel
AU - Zheng, Rong
PY - 2010/1/1
Y1 - 2010/1/1
N2 - 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 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.
AB - 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 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.
KW - Approximation algorithms
KW - Binary independent component analysis
KW - Quality of monitoring
KW - Wireless networks
UR - http://www.scopus.com/inward/record.url?scp=78649278542&partnerID=8YFLogxK
U2 - 10.1145/1860093.1860109
DO - 10.1145/1860093.1860109
M3 - Conference contribution
AN - SCOPUS:78649278542
SN - 9781450301831
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 111
EP - 120
BT - MobiCom'10 and MobiHoc'10 - Proceedings of the 16th Annual International Conference on Mobile Computing and Networking and 11th ACM International Symposium on Mobile Ad Hoc Networking and Computing
PB - Association for Computing Machinery
T2 - 11th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2010
Y2 - 20 September 2010 through 24 September 2010
ER -