A Practical Algorithm for Network Topology Inference

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A Practical Algorithm for Network Topology Inference
— When a network of robots or static sensors is emplaced in an environment, the spatial relationships between the sensing units must be inferred or computed for most key applications. In this paper we present a Monte Carlo Expectation Maximization algorithm for recovering the connectivity information (i.e. topological map) of a network using only detection events from deployed sensors. The technique is based on stochastically reconstructing samples of plausible agent trajectories allowing for the possibility of transitions to and from sources and sinks in the environment. We demonstrate robustness to sensor error and non-trivial patterns of agent motion. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We conclude with results from numerical simulations and an experiment conducted with a heterogeneous sensor network.
Dimitri Marinakis, Gregory Dudek
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICRA
Authors Dimitri Marinakis, Gregory Dudek
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