Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping

Advanced Machine Learning (ML) algorithms can be applied using Edge Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solution for processing and analysing information on IoT devices. This field aims to allow the implementation of...

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Autores: Trilles, Sergio, Hammad, Sahibzada Saadoon, Iskandaryan, Ditsuhi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/209549
Acceso en línea:https://hdl.handle.net/2445/209549
Access Level:acceso abierto
Palabra clave:Ressenyes sistemàtiques (Investigació mèdica)
Intel·ligència artificial
Systematic reviews (Medical research)
Artificial intelligence
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spelling Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature MappingTrilles, SergioHammad, Sahibzada SaadoonIskandaryan, DitsuhiRessenyes sistemàtiques (Investigació mèdica)Intel·ligència artificialSystematic reviews (Medical research)Artificial intelligenceAdvanced Machine Learning (ML) algorithms can be applied using Edge Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solution for processing and analysing information on IoT devices. This field aims to allow the implementation of Machine/Deep Learning (DL) models on MicroController Units (MCUs). Integrating anomaly detection analysis on Internet of Things (IoT) devices produces clear benefits as it ensures the use of accurate data from the initial stage. However, this process poses a challenge due to the unique characteristics of IoT. This article presents a Systematic Literature Mapping of scientific research on the application of anomaly detection techniques in EC using MCUs. A total of 18 papers published over the period 2021-2023 were selected from a total of 162 in four databases of scientific papers. The results of this paper provide a comprehensive overview of anomaly detection using TinyML and MCUs. The main contributions of this survey are the fact that it aims to: (a) study techniques for anomaly detection in ML/DL and validation metrics used in the AIoT; (b) analyse data used in the estimation of models; (c) show how ML is applied in EC using hardware or software; (d) investigate the main microcontrollers, types of power supply, and communication technology; and (e) develop a taxonomy of ML/DL algorithms used to detect anomalies in TinyML. Finally, the benefits and challenges of this kind of TinyML analysis are described.Elsevier BV2024202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion20 p.application/pdfhttps://hdl.handle.net/2445/209549Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.iot.2024.101063Internet of Things, 2024, vol. 25, p. 101063https://doi.org/10.1016/j.iot.2024.101063cc by (c) Trilles, Sergio et al, 2024http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2095492026-05-29T05:05:01Z
dc.title.none.fl_str_mv Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
title Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
spellingShingle Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
Trilles, Sergio
Ressenyes sistemàtiques (Investigació mèdica)
Intel·ligència artificial
Systematic reviews (Medical research)
Artificial intelligence
title_short Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
title_full Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
title_fullStr Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
title_full_unstemmed Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
title_sort Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping
dc.creator.none.fl_str_mv Trilles, Sergio
Hammad, Sahibzada Saadoon
Iskandaryan, Ditsuhi
author Trilles, Sergio
author_facet Trilles, Sergio
Hammad, Sahibzada Saadoon
Iskandaryan, Ditsuhi
author_role author
author2 Hammad, Sahibzada Saadoon
Iskandaryan, Ditsuhi
author2_role author
author
dc.subject.none.fl_str_mv Ressenyes sistemàtiques (Investigació mèdica)
Intel·ligència artificial
Systematic reviews (Medical research)
Artificial intelligence
topic Ressenyes sistemàtiques (Investigació mèdica)
Intel·ligència artificial
Systematic reviews (Medical research)
Artificial intelligence
description Advanced Machine Learning (ML) algorithms can be applied using Edge Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solution for processing and analysing information on IoT devices. This field aims to allow the implementation of Machine/Deep Learning (DL) models on MicroController Units (MCUs). Integrating anomaly detection analysis on Internet of Things (IoT) devices produces clear benefits as it ensures the use of accurate data from the initial stage. However, this process poses a challenge due to the unique characteristics of IoT. This article presents a Systematic Literature Mapping of scientific research on the application of anomaly detection techniques in EC using MCUs. A total of 18 papers published over the period 2021-2023 were selected from a total of 162 in four databases of scientific papers. The results of this paper provide a comprehensive overview of anomaly detection using TinyML and MCUs. The main contributions of this survey are the fact that it aims to: (a) study techniques for anomaly detection in ML/DL and validation metrics used in the AIoT; (b) analyse data used in the estimation of models; (c) show how ML is applied in EC using hardware or software; (d) investigate the main microcontrollers, types of power supply, and communication technology; and (e) develop a taxonomy of ML/DL algorithms used to detect anomalies in TinyML. Finally, the benefits and challenges of this kind of TinyML analysis are described.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/209549
url https://hdl.handle.net/2445/209549
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.iot.2024.101063
Internet of Things, 2024, vol. 25, p. 101063
https://doi.org/10.1016/j.iot.2024.101063
dc.rights.none.fl_str_mv cc by (c) Trilles, Sergio et al, 2024
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Trilles, Sergio et al, 2024
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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