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

Improving Monte Carlo localization algorithm using time series forecasting method and dynamic sampling in mobile WSNs

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

3 Author(s)
Soltaninasab, B. ; Dept. of Comput. & IT Eng., Azad Univ., Qazvin, Iran ; Sabaei, M. ; Amiri, J.

Localization of sensor nodes is one of the important operations in wireless sensor networks. Because the data produced by sensor nodes should also provide geographical location of these nodes. So having a reliable localization algorithm is always necessary. Most of presented algorithms for localization of sensor networks considered situations that the sensor nodes are static. In some of sensor networks, the nodes are mobile. So, using static localization algorithms in these networks is not suitable. Thus to support the mobility of nodes in these networks a localization algorithm will be needed that must be consistent with the mobility of nodes. Two important localization algorithms that presented in this area are Monte Carlo localization algorithm (MCL) and its improvement Monte Carlo localization boxed (MCB). Despite having a good localization accurately, sampling in these algorithms is static and they have high energy consumption. Also these algorithms are not able to localize sensor nodes in some circumstances. The main reason is that in some time slots the node can not hear any seed node. In this paper a new method has been suggested that uses forecasting and dynamic sampling for localization. This method has the ability of nodes localization in these conditions and that is an energy efficient method. Simulation results showed that the proposed method has a better performance in sparse networks in comparison with previous similar methods.

Published in:

Communication Systems, Networks and Applications (ICCSNA), 2010 Second International Conference on  (Volume:1 )

Date of Conference:

June 29 2010-July 1 2010

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.