Approximation algorithms for NP-hard problems
A framework for wireless LAN monitoring and its applications
Proceedings of the 3rd ACM workshop on Wireless security
Understanding link-layer behavior in highly congested IEEE 802.11b wireless networks
Proceedings of the 2005 ACM SIGCOMM workshop on Experimental approaches to wireless network design and analysis
MOJO: a distributed physical layer anomaly detection system for 802.11 WLANs
Proceedings of the 4th international conference on Mobile systems, applications and services
Jigsaw: solving the puzzle of enterprise 802.11 analysis
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Analyzing the MAC-level behavior of wireless networks in the wild
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Passive online rogue access point detection using sequential hypothesis testing with TCP ACK-pairs
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Robust Detection of Unauthorized Wireless Access Points
Mobile Networks and Applications
A location-based management system for enterprise wireless LANs
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Sniffer channel selection for monitoring wireless LANs
Computer Communications
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Wireless sniffers are often used to monitor APs in wireless LANs (WLANs) for network management, fault detection, traffic characterization, and optimizing deployment. It is cost effective to deploy single-radio sniffers that can monitor multiple nearby APs. However, since nearby APs often operate on orthogonal channels, a sniffer needs to switch among multiple channels to monitor its nearby APs. In this paper, we formulate and solve two optimization problems on sniffer channel selection. Both problems require that each AP be monitored by at least one sniffer. In addition, one optimization problem requires minimizing the maximum number of channels that a sniffer listens to, and the other requires minimizing the total number of channels that the sniffers listen to. We propose a novel LP-relaxation based algorithm, and two simple greedy heuristics for the above two optimization problems. Through simulation, we demonstrate that all the algorithms are effective in achieving their optimization goals, and the LP-based algorithm outperforms the greedy heuristics.