Cart (Loading....) | Create Account
Close category search window

Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy

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)
Vaisenberg, R. ; Univ. of California, Irvine, Irvine, CA, USA ; Motta, A.D. ; Mehrotra, S. ; Ramanan, D.

We present a framework for sensor actuation and control in sentient spaces, in which sensors are used to observe a physical phenomena. We focus on sentient spaces that enable pervasive computing applications, such as smart video surveillance and situational awareness in instrumented office environments. Our framework utilizes the spatio-temporal statistical properties of an observed phenomena, with the goal of maximizing an application-specified reward. Specifically, we define an observation of a phenomena by assigning it a discrete value (state) and we model its semantics as the transition between these values (states). This semantic model is used to predict the future states in which the phenomena is likely to be at, based on partially-observed past states. To accomplish real-time agility, we designed an approximate, adaptive-grid solution for Partially Observable Markov Decision Processes (POMDPs) that yields practically good results, and in some cases, guarantees on the quality of the approximation. We use our framework to control and actuate a large-scale camera network so as to maximize the number and type of captured events. To enable real-time control, we implement an action schedule using a table lookup and make use of a factored probability model to capture state semantics. To the best of our knowledge, we are the first to address the problem of actuating a large-scale sensor network based on a real-time POMDP formulation.

Published in:

Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on

Date of Conference:

18-22 March 2013

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.