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Research Article Open Access
In this paper a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic has been proposed. This tool proposes the use of multiple sniffers being deployed across the network to capture wireless traffic trace thus does not requires any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Using machine learning approach these traces help to infer the carrier-sense relationship between network nodes. We are also able to detect selfish carrier-sense behavior. 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 use ns2 simulation to also validate the approach in a real Wireless LAN environment.