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AuthorMajib, Yasar
AuthorBarhamgi, Mahmoud
AuthorHeravi, Behzad Momahed
AuthorKariyawasam, Sharadha
AuthorPerera, Charith
Available date2022-10-24T07:23:45Z
Publication Date2022-09-24
Publication NameJournal of Ambient Intelligence and Humanized Computing
Identifierhttp://dx.doi.org/10.1007/s12652-022-04376-w
CitationMajib, Y., Barhamgi, M., Heravi, B. M., Kariyawasam, S., & Perera, C. (2022). Detecting anomalies within smart buildings using do-it-yourself internet of things. Journal of Ambient Intelligence and Humanized Computing, 1-17.
ISSN1868-5137
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138680732&origin=inward
URIhttp://hdl.handle.net/10576/35340
AbstractDetecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.
SponsorEPSRC PETRAS (EP/S035362/1) and GCHQ National Resilience Fellowship
Languageen
PublisherSpringer
SubjectAnomaly detection
Internet of things
Machine learning
Smart buildings
TitleDetecting anomalies within smart buildings using do-it-yourself internet of things
TypeArticle
Pagination1-17
ESSN1868-5145
dc.accessType Open Access


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