By Topic

Distributed model consensus for models of locally biased measurements 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)
Thompson, J. ; Dept. of Comput. Sci. & Electr. Eng., Univ. of Maryland Baltimore County, Baltimore, MD, USA ; Kalpakis, K.

In a wireless sensor network, the sensors collect measurements from their local environments and build a model from those measurements in order to draw conclusions. Distributed model consensus allows sensors to make inferences about the global state of the deployment environment, by sharing models among the sensors, rather than raw data. In this paper, we analyze a regression model consensus framework based on graphical models. We compare its performance to a baseline alternative based on gossip averaging. Convergence and accuracy issues arise in the belief propagation used in the graphical model method, when the underlying communication topology contains cycles. Through simulation, we evaluate the performance on random geometric graph network topologies containing cycles.

Published in:

Computing, Networking and Communications (ICNC), 2013 International Conference on

Date of Conference:

28-31 Jan. 2013