Learning Network Topology from Simple Sensor Data

Dimitri Marinakis, Philippe Giguère, Gregory Dudek

Abstract: In this paper, we present an approach for recovering a topological map of the environment using only detection events from a deployed sensor network. Unlike other solutions to this problem, our technique operates on timestamp free observational data; i.e. no timing information is exploited by our algorithm except the ordering. We first give a theoretical analysis of this version of the problem, and then we show that by considering a sliding window over the observations, the problem can be re-formulated as a version of set-covering. We present two heuristics based on this set-covering formulation and evaluate them with numerical simulations. The experiments demonstrate that promising results can be obtained using a greedy algorithm.

External IDs:dblp:conf/ai/MarinakisGD07