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.

中文翻译：

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传感器系统中的智能异常检测：多视角回顾

异常检测涉及识别明显偏离预期行为的数据模式。这是一个重要的研究问题，因为它具有广泛的应用领域，从数据分析到电子健康、网络安全、预测性维护、故障预防和工业自动化。在此，我们回顾了可用于检测传感器系统特定区域异常的最先进方法，这些方法在信息融合、数据量、数据速度和网络/能源效率方面提出了艰巨的挑战，以提到但最紧迫的。在这种情况下，异常检测是一个特别困难的问题，因为需要在受限环境中找到计算能量准确性的权衡。我们对各种方法进行分类，从传统技术（统计方法、时间序列分析、信号处理等）到数据驱动技术（监督学习、强化学习、深度学习等）。我们还研究了不同架构环境（云、雾、边缘）对传感器生态系统的影响。该评论指出了最有前途的智能传感方法，并指出了一系列有趣的开放性问题和挑战。