Dimitri Marinakis, Gregory Dudek
In this paper we address the problem of inferring the topology, or inter-node navigability, of a sensor network given non-discriminating observations of activity in the environment. By exploiting motion present in the environment, our approach is able to recover a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We employ a reasoning system made up of a stochastic Expectation Maximization algorithm and a higher level search strategy employing the principle of Occam's Razor to look for the simplest solution explaining the data. The technique is assessed through numerical simulations and experiments conducted on a real sensor network.
Subjects: 17. Robotics; 3.4 Probabilistic Reasoning
Submitted: Oct 13, 2006