By Topic

Cluster-based Jacobi iteration for distributed regression in wireless 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

2 Author(s)
Chaojun Hou ; Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China ; Guoli Wang

This paper presents a Jacobi iterative based computational paradigm for solving the data regression in wireless sensor networks (WSNs). The in-network computational scheme is proposed to construct a mixture regression model through the cluster-based Jacobi distributed iteration, where the intersections among mixture structure of regression model are decoupled through a new cluster-based message passing protocol in an energy-efficient fashion. The cluster-based computational scheme proposed here contributes not only to easing network topology management, but also to speeding the convergent rate of distributed computation. Experimental results are reported to illustrate the validation of the proposed approach.

Published in:

Asian Control Conference, 2009. ASCC 2009. 7th

Date of Conference:

27-29 Aug. 2009