Localization in sensor networks - A matrix regression approach
Résumé
In this paper, we propose a new approach to sensor localization problems, based on recent developments in machine leaning. The main idea behind it is to consider a matrix regression method between the ranging matrix and the matrix of inner products between positions of sensors, in order to complete the latter. Once we have learnt this regression from information between sensors of known positions (beacons), we apply it to sensors of unknown positions. Retrieving the estimated positions of the latter can be done by solving a linear system. We propose a distributed algorithm, where each sensor positions itself with information available from its nearby beacons. The proposed method is validated by experimentations.
Mots clés
machine learning
matrix algebra
regression analysis
sensor networks
matrix regression approach
sensor localization problems
machine leaning
ranging matrix
linear system
distributed algorithm
Distance measurement
Kernel
Optimization
Distributed algorithms
Wireless sensor networks
Network topology
Topology
| Origine | Fichiers produits par l'(les) auteur(s) |
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