Title :

Estimation of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks

Author(s) :

Nevat, Ido [Auteur]
Institute for Infocomm Research - I²R [Singapore]
Peters, Gareth W. [Auteur]
University College of London [London] [UCL]
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Institut TELECOM/TELECOM Lille1
Matsui, Tomoko [Auteur]

Journal title :

IEEE Transactions on Signal Processing

Pages :

2597--2609

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2015-05

ISSN :

1053-587X

English keyword(s) :

Wireless sensor networks
detection
Gaussian processes
Kernel methods
imperfect communication channels

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

We develop new algorithms for spatial field re- construction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) Wireless Sensor Networks (WSNs). We consider spatial physical ...
Show more >We develop new algorithms for spatial field re- construction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) Wireless Sensor Networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it consists partially of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Méthodes bayésiennes non paramétriques pour le traitement du signal et de l'image

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL