  This work proposes a tool to estimate the interference between nodes and
links in a live wireless network by passive monitoring of wireless traffic.
This 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. It also demonstrates 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. Simulation results demonstrate that the proposed approach of
estimating interference relations is significantly more accurate than simpler
heuristics and quite competitive with active measurements. Minimizing router
overhead taken as a main goal.
