By Topic

Reliability of Data Fusion Algorithm 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

1 Author(s)
Pandya, A. ; Booz Allen Hamilton, Los Angeles

Dense deployment of sensor and the nature of the physical phenomenon being observed (sensed) result in spatio-temporal correlation in the observed data. Data fusion plays an important role in sensor networks for exploiting this correlation. However, fusion performance depends on parameters such as the reliability model of the sensors, sensor observations, and a priori information. Individual sensors transmit likelihood functions to the fusion center to produce a single posterior distribution or estimate. Here we proposes a new fusion method, reliable likelihood opinion pool (RelOP), for aggregating likelihoods to produce a reliable estimate. It is based on a Bayesian framework. The performance of RelOP is compared with the commonly used opinion pools through simulations. We further propose a multi-sensor fusion architecture that follows from application of the RelOP rule.

Published in:

Information Theory, 2007. ISIT 2007. IEEE International Symposium on

Date of Conference:

24-29 June 2007