By Topic

Adaptive tracking in distributed 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

4 Author(s)
Lizhi Yang ; Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ ; Chuan Feng ; Rozenblit, Jerzy W. ; Haiyan Qiao

We study the problem of tracking moving objects using distributed wireless sensor networks (WSNs) in which sensors are deployed randomly. Due to the uncertainty and unpredictability of real-world objects' motion, the tracking algorithm is needed to adapt to real-time changes of velocities and directions of a moving target. Moreover, the energy consumption of the tracking algorithm has to be considered because of the inherent limitations of wireless sensors. In this paper, we proposed an energy efficient tracking algorithm, called Predict-and-Mesh (PaM) that is well suited for pervasively monitoring various kinds of objects with random movement patterns. PaM is a distributed algorithm consisting of two prediction models: n-step prediction and collaborative prediction, and a predication failure recovery process called mesh. The simulation results show that the PaM algorithm is robust against diverse motion changes and has the excellent performance

Published in:

Engineering of Computer Based Systems, 2006. ECBS 2006. 13th Annual IEEE International Symposium and Workshop on

Date of Conference:

27-30 March 2006