By Topic

Latent variables based data estimation for sensing applications

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
$33 $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

3 Author(s)
Nakul Verma ; Computer Science and Engineering, University of California San Diego 9500 Gilman Dr., CA 92093, USA ; Piero Zappi ; Tajana Rosing

Recovering missing sensor data is a critical problem for sensor networks, especially when nodes duty cycle their activity or may experience periodic downtimes due to limited energy. Fortunately, sensor readings are often correlated across different nodes and sensor types. Among state-of-the-art statistical data estimation techniques, latent variable based factor models have emerged as a powerful framework for recovering missing data. In this paper we propose the use of latent variable models to estimate missing data in heterogeneous sensor networks. Our model not only correlates data across different sensor locations and types, but also takes advantage of the temporal structure that is often present in sensor readings. We analyze how this model can effectively reconstruct missing sensor data when the individual sensor nodes have to duty-cycle their activity in order to extend network lifetime. We evaluate our model on a real life sensor network consisting of 122 environmental monitoring stations that periodically collect data from 13 different sensors. Results show that our proposed model can effectively reconstruct over 50% of missing data with less than 10% error.

Published in:

Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2011 Seventh International Conference on

Date of Conference:

6-9 Dec. 2011