Learning Sensor Network Topology through Monte Carlo Expectation Maximization
Abstract
We consider the problem of inferring sensor positions and a topological
(i.e. qualitative) map of an environment given a set of cameras with
non-overlapping fields 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 traffic 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 first-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 traffic patterns. We conclude with
results from numerical simulations.