Tracking Many Objects with Many Sensors
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Towards a general theory of topological maps
Artificial Intelligence
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
Learning Network Topology from Simple Sensor Data
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Pheromone Approach to the Adaptive Discovery of Sensor-Network Topology
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Estimation of sensor network topology using ant colony optimization
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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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.