By Topic

Decentralized fuzzy controlling for target classification using 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)
Tashtoush, Y.M. ; Dept. of Comput. Sci., Jordan Univ. of Sci. & Technol., Irbid, Jordan ; Al-Enizy, A.-A.

Target classification is one of the applications of wireless sensor networks that aims to recognize the type of mobile targets that navigate within a sensing field. This paper presents a fuzzy-based controller module using MaxMin and MinMax Distributed K-Nearest Neighbors (DKNN) algorithms for ground vehicle classification in order to achieve efficient energy usage and better classification accuracy. This fuzzy module has embedded in an existing target classification system. The fuzzy-based controller module handles the wireless sensor nodes sensing rate (refresh rate) dynamically. A simulation-based study has carried out to test our approach and the simulation results have compared to well-known MaxMin and MinMax DKNN algorithms from literature. Simulation results show that our proposed approach prolongs the network lifetime and achieves better target classification accuracy.

Published in:

Intelligent and Advanced Systems (ICIAS), 2010 International Conference on

Date of Conference:

15-17 June 2010