Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Distributed prediction of time series data with kernels and adaptive filtering techniques in sensor networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Honeine, P. ; Lab. LM2S, Univ. de Technol. de Troyes, Troyes ; Richard, C. ; Bermudez, J.C.M. ; Snoussi, H.

Wireless sensor networks are becoming versatile tools for learning a physical phenomenon, monitoring its variations and predicting its evolution. They rely on low-cost tiny devices which are deployed in the region under scrutiny and collaborate with each other. Limited computation and communication resources require special care in designing distributed prediction algorithms for sensor networks. In this communication, we propose a nonlinear prediction technique that takes advantage of recent developments in kernel machines and adaptive filtering for online nonlinear functional learning. Conventional methods, however, are inappropriate for large-scale sensor networks, as the resulting model corresponds to the number of deployed sensors. To circumvent these drawbacks, we consider a distributed control of the model order. The model parameters are transmitted from sensor to sensor and updated by each sensor based the measurement information. The model order is incremented whenever this increment is relevant compared to a fixed-order model. The proposed approach is naturally adapted for predicting a time-varying phenomenon, as model order increases are governed by the novelty of the new observation at each sensor node. We illustrate the applicability of the proposed technique by some simulations on establishing the temperature map in an region heated by sources.

Published in:

Signals, Systems and Computers, 2008 42nd Asilomar Conference on

Date of Conference:

26-29 Oct. 2008