Search Results for author: M. Di Mauro

Found 1 papers, 0 papers with code

Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review

no code implementations27 Oct 2020 L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, A. Liotta

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.

Anomaly Detection Time Series +1

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