Learning Sensor Network Topology through Monte Carlo Expectation Maximization.

ó We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an envi- ronment given a set of cameras with non-overlapping elds of view. In this way, without prior knowledge of the environment nor the exact position of sensors within the environment, one can infer the topology of the environment, and common trafc patterns within it. In particular, we consider sensors stationed at the junctions of the hallways of a large building. We infer the sensor connectivity graph and the travel times between sensors (and hence the hallway topology) from the sequence of events caused by unlabeled agents (i.e. people) passing within view of the different sensors. We do this based on a rst- order semi-Markov model of the agent's behavior. The paper describes a problem formulation and proposes a stochastic algorithm for its solution. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying trafc patterns. We conclude with results from numerical simulations