Anomaly Detection for IoT Time-Series Data: A Survey

TLDR

Anomaly detection identifies novel or unexpected observations, yet most existing methods are highly specific and require expert knowledge, and applying them to the rapidly expanding IoT domain presents additional challenges due to its unique characteristics. This review aims to outline the challenges of applying anomaly detection to IoT data and illustrate them with literature examples. The authors discuss a range of approaches developed across various domains, not limited to IoT, reflecting the novelty of this application. The survey concludes by summarizing current challenges in anomaly detection and highlighting future research opportunities.

Abstract

Anomaly detection is a problem with applications for a wide variety of domains; it involves the identification of novel or unexpected observations or sequences within the data being captured. The majority of current anomaly detection methods are highly specific to the individual use case, requiring expert knowledge of the method as well as the situation to which it is being applied. The Internet of Things (IoT) as a rapidly expanding field offers many opportunities for this type of data analysis to be implemented, however, due to the nature of the IoT, this may be difficult. This review provides a background on the challenges which may be encountered when applying anomaly detection techniques to IoT data, with examples of applications for the IoT anomaly detection taken from the literature. We discuss a range of approaches that have been developed across a variety of domains, not limited to IoT due to the relative novelty of this application. Finally, we summarize the current challenges being faced in the anomaly detection domain with a view to identifying potential research opportunities for the future.