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

TL;DR

This paper reviews advanced anomaly detection methods for sensor systems, highlighting challenges, architectures, and promising techniques, to improve data reliability and system efficiency across various application domains.

Contribution

It provides a comprehensive taxonomy and analysis of both conventional and data-driven anomaly detection methods tailored for sensor systems, considering architectural environments.

Findings

Identifies key challenges in sensor anomaly detection, including data volume and energy constraints.

Highlights promising intelligent sensing methods for improved anomaly detection.

Pinpoints open issues and future research directions in the field.

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.