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

arXiv: https://arxiv.org/abs/2010.14946 · HTML: https://mlsanity.com/paper/2010.14946/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks](https://mlsanity.com/paper/2407.05639/) (2024)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection](https://mlsanity.com/paper/2310.11169/) (2023)
- [Time Series Anomaly Detection for Smart Grids: A Survey](https://mlsanity.com/paper/2107.08835/) (2021)
- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2312.00279/) (2023)
- [Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision](https://mlsanity.com/paper/2305.15652/) (2023)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Agentic AI for autonomous anomaly management in complex systems](https://mlsanity.com/paper/2507.15676/) (2025)
- [Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management](https://mlsanity.com/paper/2110.04256/) (2021)
- [Coupled Attention Networks for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2306.07114/) (2023)
- [Anomaly Detection Techniques in Smart Grid Systems: A Review](https://mlsanity.com/paper/2306.02473/) (2023)
- [Research on Dynamic Data Flow Anomaly Detection based on Machine Learning](https://mlsanity.com/paper/2409.14796/) (2024)
- [Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis](https://mlsanity.com/paper/2412.16098/) (2024)
- [Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning](https://mlsanity.com/paper/2101.11924/) (2021)
- [A Marketplace Price Anomaly Detection System at Scale](https://mlsanity.com/paper/2310.04367/) (2023)
- [A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data](https://mlsanity.com/paper/2301.00462/) (2023)
- [Architectures for Robust Self-Organizing Energy Systems under Information and Control Constraints](https://mlsanity.com/paper/2604.21529/) (2026)
- [Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder](https://mlsanity.com/paper/2606.05274/) (2026)

## Abstract (arXiv)

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.