Distributed Non-Asymptotic Confidence Region Computation over Sensor Networks

Vincenzo Zambianchi 1 Francesca Bassi 2, 3 Alex Calisti 1 Davide Dardari 1 Michel Kieffer 3 Gianni Pasolini 1
1 CNIT - Consorzio Nazionale Interuniversitario per le telecomunicazioni
2 ESME Sudria [Paris]
3 L2S - Laboratoire des signaux et systèmes
Abstract : This paper addresses the distributed computation of exact, non-asymptotic confidence regions for the parameter estimation of a linear model from observations at different nodes of a network of sensors. If a central unit gathers all the data, the sign perturbed sums (SPS) method proposed by Csáji et al. can be used to define guaranteed confidence regions with prescribed confidence levels from a finite number of measurements. SPS requires only mild assumptions on the measurement noise. This work proposes distributed solutions, based on SPS and suited to a wide variety of sensor networks, for distributed in-node evaluation of non-asymptotic confidence regions as defined by SPS. More specifically, a Tagged and Aggregated Sum information diffusion algorithm is introduced, which exploits the specificities of SPS to avoid flooding the network with all measurements provided by the sensors. The performance of the proposed solutions is evaluated in terms of required traffic load, both analytically and experimentally on different network topologies. The best information diffusion strategy among nodes depends on how structured the network is.
Keywords : sign-perturbed sums guaranteed precision Confidence region distributed algorithms
Type de document :
Article dans une revue
IEEE transactions on Signal and Information Processing over Networks, IEEE, 2018, 4 (2), pp.308 - 324. 〈10.1109/TSIPN.2017.2695403〉
Liste complète des métadonnées

Littérature citée [33 références]  Voir  Masquer  Télécharger
https://hal-centralesupelec.archives-ouvertes.fr/hal-01576604
Contributeur : Michel Kieffer <>
Soumis le : mercredi 23 août 2017 - 15:07:25
Dernière modification le : mercredi 28 novembre 2018 - 01:10:26

Fichier

Single_v11.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

CENTRALESUPELEC | L2S | SUP_LSS | UNIV-PARIS-SACLAY | PARISTECH | SUP_TELECOMS

Citation

Vincenzo Zambianchi, Francesca Bassi, Alex Calisti, Davide Dardari, Michel Kieffer, et al.. Distributed Non-Asymptotic Confidence Region Computation over Sensor Networks. IEEE transactions on Signal and Information Processing over Networks, IEEE, 2018, 4 (2), pp.308 - 324. 〈10.1109/TSIPN.2017.2695403〉. 〈hal-01576604〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

312

Téléchargements de fichiers

127

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD