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Detecting anomalies within smart buildings using do-it-yourself internet of things

Majib, Yasar, Barhamgi, Mahmoud, Heravi, Behzad Momahed, Kariyawasam, Sharadha and Perera, Charith ORCID: 2023. Detecting anomalies within smart buildings using do-it-yourself internet of things. Journal of Ambient Intelligence and Humanized Computing 14 , pp. 4727-4743. 10.1007/s12652-022-04376-w

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Detecting 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Publisher: Springer
ISSN: 1868-5137
Funders: EPSRC
Date of First Compliant Deposit: 1 February 2023
Date of Acceptance: 30 July 2022
Last Modified: 12 Jun 2023 16:55

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