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A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective
MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China; National Trusted Embedded Software Engineering Technology Research Center (No. 2012FU125X15), Shanghai, China.
Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Richard Petersens Plads, Kongens Lyngby, Denmark. (AASS)ORCID iD: 0000-0001-9575-2990
MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China; National Trusted Embedded Software Engineering Technology Research Center (No. 2012FU125X15), Shanghai, China.
2015 (English)In: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, article id 678029Article, review/survey (Refereed) Published
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Abstract [en]

Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2015. article id 678029
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-47173DOI: 10.1155/2015/678029ISI: 000365545900001Scopus ID: 2-s2.0-84948807614OAI: oai:DiVA.org:oru-47173DiVA, id: diva2:885808
Note

Funding Agencies:

China Scholarship Council

National Natural Science Foundation of China 61361136002

National Science and Technology Major Project 2014ZX01038-101-001

Shanghai Committee of Science and Technology, China 14511100400

National Trusted Embedded Software Engineering Technology Research Center, China 2012FU125X15

Available from: 2015-12-21 Created: 2015-12-21 Last updated: 2018-01-10Bibliographically approved

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Dragoni, Nicola

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