Smart anomaly detection in sensor systems: A multi-perspective review. Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., & Liotta, A. Information Fusion, October, 2020. ![Smart anomaly detection in sensor systems: A multi-perspective review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. 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.
@article{erhan_smart_2020,
title = {Smart anomaly detection in sensor systems: {A} multi-perspective review},
issn = {1566-2535},
shorttitle = {Smart anomaly detection in sensor systems},
url = {http://www.sciencedirect.com/science/article/pii/S1566253520303717},
doi = {10.1016/j.inffus.2020.10.001},
abstract = {Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. 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.},
language = {en},
urldate = {2020-10-19},
journal = {Information Fusion},
author = {Erhan, L. and Ndubuaku, M. and Di Mauro, M. and Song, W. and Chen, M. and Fortino, G. and Bagdasar, O. and Liotta, A.},
month = oct,
year = {2020},
keywords = {Anomaly detection, Intelligent sensing, Internet of things, Machine learning, Sensor systems},
}
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