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dc.contributor.authorErhan, L.
dc.contributor.authorNdubuaku, M.
dc.contributor.authorDi Mauro, M.
dc.contributor.authorSong, W.
dc.contributor.authorChen, M.
dc.contributor.authorFortino, G.
dc.contributor.authorBagdasar, O.
dc.contributor.authorLiotta, A.
dc.date.accessioned2020-11-20T16:54:56Z
dc.date.available2020-11-20T16:54:56Z
dc.date.issued2020-10-15
dc.identifier.citationErhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O. and Liotta, A., (2020). 'Smart anomaly detection in sensor systems: A multi-perspective review'. Information Fusion, 67, pp. 1-21.en_US
dc.identifier.doi10.1016/j.inffus.2020.10.001
dc.identifier.urihttp://hdl.handle.net/10545/625395
dc.description.abstractAnomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy-accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S1566253520303717?via%3Dihuben_US
dc.relation.urlhttps://arxiv.org/abs/2010.14946en_US
dc.subjectAnomaly detectionen_US
dc.subjectIntelligent sensingen_US
dc.subjectInternet of Thingsen_US
dc.subjectMachine learningen_US
dc.subjectSensor systemsen_US
dc.titleSmart anomaly detection in sensor systems: A multi-perspective reviewen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Salerno, Italyen_US
dc.contributor.departmentShanghai Ocean University, Chinaen_US
dc.contributor.departmentHuazhong University of Science & Technology, Chinaen_US
dc.contributor.departmentUniversity of Calabria, Italyen_US
dc.contributor.departmentFree University of Bozen-Bolzano, Italyen_US
dc.identifier.journalInformation Fusionen_US
dc.identifier.eid2-s2.0-85093684355
dc.identifier.scopusidSCOPUS_ID:85093684355
dc.identifier.piiS1566253520303717
dc.source.journaltitleInformation Fusion
dc.source.volume67
dc.source.beginpage64
dc.source.endpage79
dcterms.dateAccepted2020-10-04
dc.author.detailstf4004en_US


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