  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.
