@inproceedings{modi:mobiquitous09,
 title = {Model-Based Fault Diagnosis for {IEEE} 802.11 Wireless {LANs}},
 author = {Bo Yan and Guanling Chen},
 booktitle = {Proceedings of the Sixth Annual International
Conference on Mobile and Ubiquitous Systems: Computing, Networking and
Services (MobiQuitous)},
 year = 2009,
 month = jul,
 address = {Toronto, Canada},
 url = {http://www.cs.uml.edu/~glchen/papers/modi-mobiquitous09.pdf},
 abstract = {The increasingly deployed IEEE 802.11 wireless LANs
(WLANs) challenge
traditional network management systems because of the shared open medium and
the varying channel conditions. There needs to be an automated tool that can
help diagnosing both malicious security faults and benign performance faults.
It is often difficult, however, to identify the root causes since the
manifesting anomalies from network measurements are highly interrelated. In
this paper we present a novel approach, called {\em MOdel-based
self-DIagnosis} (MODI), for fault detection and localization.

Our solution consists of Structural and Behavioral Model (SBM) that is
constructed using both \emph{structural} causality from wireless protocol
specifications and \emph{behavioral} statistics from network measurements. We
use logic-based backward reasoning to automate fault detection and
localization based on SBM, by comparing observed network measurements with
expected network behaviors and by tracing back causality structures. The
reasoning algorithm and the model description are decoupled so a SBM model can
be easily updated for varying WLAN configurations and changing network
conditions. Compared to previous work, the contribution of this paper is the
architecture and the algorithm of the diagnosis core, rather than the WLAN
measurement techniques.

We built and deployed MODI-embedded wireless APs that can detect both security
attacks and troubleshoot performance problems. These MODI-enabled APs can also
cooperate to diagnose cross-AP problems, such as those caused by device
mobility. The evaluation results demonstrate that the proposed model-based
diagnosis is fast and effective with little overhead.},
 note = {To appear.},
}