Learning Network Topology from Simple Sensor Data

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. Key words: sensor networks, topology inference, set-covering, mapping

• Type: Conference
• Year: 2007
• Where: AI
• Authors: Dimitri Marinakis, Philippe Giguère, Gregory Dudek
• Keywords: AI 2007, Artificial Intelligence, Deployed Sensor Network, Free Observational Data, Sensor Networks