Recovering network topology with binary sensors

We present a method to extract topology information from detection events of mobile entities moving through a network of binary sensors. We extract the topological structure of possible paths in the network by analyzing the time correlation of events at different sensors. The histograms of time delays between any two sensors contain the necessary information to reconstruct the network topology. This data is heavily corrupted by noise due to multiple agents in the network. We therefore use a mixture model of multiple Gaussian and a uniform distribution to explicitly isolate the noise. Our algorithm yields a graph representing the topology of our sensor network along with average travel time between nodes.