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FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

  • Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeableWireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.show moreshow less

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    Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Document Type:Article
Author:Prof Shahaboddin ShamshirbandORCiD, Javad Hassannataj Joloudari, Mohammad GhasemiGol, Hamid SaadatfarORCiD, Amir MosaviORCiD, Narjes NabipourORCiD
DOI (Cite-Link):https://doi.org/https://doi.org/10.3390/math8010028Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200107-40541Cite-Link
URL:https://www.mdpi.com/2227-7390/8/1/28
Parent Title (English):Mathematics
Publisher:MDPI
Language:English
Date of Publication (online):2019/12/23
Date of first Publication:2019/12/23
Release Date:2020/01/07
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 8, Issue 1, 28
Pagenumber:24
Tag:Internet of Things; machine learning; wireless sensor networks
GND Keyword:Vernetzung; Funktechnik; Sensor; Maschinelles Lernen
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme
500 Naturwissenschaften und Mathematik / 510 Mathematik
BKL-Classification:54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2019
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)