Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9679
Title: Fault tolerant localization and tracking of multiple sources in WSNs using binary data
Authors: Michaelides, Michalis P. 
Laoudias, Christos 
Panayiotou, Christos 
metadata.dc.contributor.other: Μιχαηλίδης, Μιχάλης Π.
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Binary data;Fault tolerance;Localization and tracking;Multiple sources;Wireless Sensor Networks
Issue Date: 1-Jan-2014
Source: IEEE Transactions on Mobile Computing, 2014, vol. 13, no. 6, pp. 1213-1227
Volume: 13
Issue: 6
Start page: 1213
End page: 1227
Project: Fault Tolerant Detection, Localization and Tracking using WSNs 
Journal: IEEE Transactions on Mobile Computing 
Abstract: This paper investigates the use of a Wireless Sensor Network for localizing and tracking multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development and analysis of a low-complexity, distributed, real-time algorithm that uses the binary observations of the sensors for identifying, localizing, and tracking multiple targets in a fault tolerant way. Specifically, our results indicate that the proposed algorithm retains its performance in tracking accuracy in the presence of noise and faults, even when a large percentage of sensor nodes (25 percent) report erroneous observations.
URI: https://hdl.handle.net/20.500.14279/9679
ISSN: 15361233
DOI: 10.1109/TMC.2013.2297319
Rights: © IEEE
Type: Article
Affiliation : Cyprus University of Technology 
University of Cyprus 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

CORE Recommender
Show full item record

SCOPUSTM   
Citations

26
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations 5

19
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s) 20

475
Last Week
0
Last month
8
checked on Dec 22, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.