2025
Hammad, Sahibzada Saadoon; Trilles-Oliver, Sergio
Anomaly Detection for Trust Management in Internet of Things Systems Proceedings Article
In: Distributed Computing and Artificial Intelligence, Special Sessions II, 21st International Conference, Springer, Cham, 2025, ISBN: 978-3-031-80946-0, (2025-02).
Abstract | Links | BibTeX | Tags: Anomaly detection, Internet of things, machine learning
@inproceedings{Saadoon2025a,
title = {Anomaly Detection for Trust Management in Internet of Things Systems},
author = {Sahibzada Saadoon Hammad and Sergio Trilles-Oliver},
doi = {10.1007/978-3-031-80946-0_29},
isbn = {978-3-031-80946-0},
year = {2025},
date = {2025-03-15},
urldate = {2025-03-15},
booktitle = {Distributed Computing and Artificial Intelligence, Special Sessions II, 21st International Conference},
volume = {1151},
publisher = {Springer, Cham},
series = {Lecture Notes in Networks and Systems},
abstract = {The fast growth of Internet of Things systems has transformed the way policy decisions are made based on the data produced by these devices. These data are prone to errors and anomalies and can present privacy and security issues which can affect policy decisions. Therefore, it is necessary that, at each layer of Internet of Things architecture, the data undergo a process to ensure its quality. This paper presents a framework for building a trust management system for Internet of Things devices using anomaly detection in different layers of IoT architecture and formulation of a reputation index.},
note = {2025-02},
keywords = {Anomaly detection, Internet of things, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
The fast growth of Internet of Things systems has transformed the way policy decisions are made based on the data produced by these devices. These data are prone to errors and anomalies and can present privacy and security issues which can affect policy decisions. Therefore, it is necessary that, at each layer of Internet of Things architecture, the data undergo a process to ensure its quality. This paper presents a framework for building a trust management system for Internet of Things devices using anomaly detection in different layers of IoT architecture and formulation of a reputation index.
2024
Trilles-Oliver, Sergio; Hammad, Sahibzada Saadoon; Iskandaryan, Ditsuhi
Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping Journal Article
In: Internet of Things, vol. 25, pp. 101063, 2024, ISSN: 2542-6605, (2024-02).
Abstract | Links | BibTeX | Tags: Anomaly detection, Edge computing, Internet of things, TidyML
@article{Trilles2024a,
title = {Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping},
author = {Sergio Trilles-Oliver and Sahibzada Saadoon Hammad and Ditsuhi Iskandaryan},
doi = {https://doi.org/10.1016/j.iot.2024.101063},
issn = {2542-6605},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Internet of Things},
volume = {25},
pages = {101063},
publisher = {Elsevier},
abstract = {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.},
note = {2024-02},
keywords = {Anomaly detection, Edge computing, Internet of things, TidyML},
pubstate = {published},
tppubtype = {article}
}
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.