The Community for Technology Leaders
Green Image
Issue No. 03 - March (2013 vol. 12)
ISSN: 1536-1233
pp: 434-446
U. Paul , Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY, USA
A. Kashyap , Symantec Res. Labs., San Jose, CA, USA
R. Maheshwari , Akamai Technol., Somerville, MA, USA
S. R. Das , Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY, USA
We present a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This tool does not require any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Our approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed using a machine learning approach to infer the carrier-sense relationship between network nodes. This coupled with an estimation of collision probabilities helps us to deduce the interference relationships. We also demonstrate an important application of this tool-detection of selfish carrier-sense behavior. This is based on identifying any asymmetry in carrier-sense behavior between node pairs and finding multiple witnesses to raise confidence. We evaluate the effectiveness of the tool for both the applications using extensive experiments and simulation. Experimental and simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. We also validate the approach in a real Wireless LAN environment. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability of the selfish behavior. On the other hand, the metric of selfishness used to estimate selfish behavior matches closely with actual degree of selfishness observed.
wireless LAN, interference suppression, learning (artificial intelligence), probability, telecommunication computing, telecommunication traffic, wireless LAN, passive measurement, interference measurement, WiFi network, misbehavior detection, passive monitoring, wireless traffic, multiple sniffers, machine learning, carrier-sense relationship, collision probabilities, selfish carrier-sense behavior, Interference, Hidden Markov models, Markov processes, IEEE 802.11 Standards, Wireless communication, Sensors, Monitoring, interference, 802.11 protocol, hidden Markov model, MAC layer misbehavior

S. R. Das, R. Maheshwari, A. Kashyap and U. Paul, "Passive Measurement of Interference in WiFi Networks with Application in Misbehavior Detection," in IEEE Transactions on Mobile Computing, vol. 12, no. , pp. 434-446, 2013.
188 ms
(Ver 3.3 (11022016))