Anomaly Detection for IoT Time-Series Data: A Survey

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 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 IoT anomaly detection taken from the literature. We discuss a range of approaches which have been developed across a variety of domains, not limited to Internet of Things due to the relative novelty of this application. Finally we summarise the current challenges being faced in the anomaly detection domain with a view to identifying potential research opportunities for the future.

Application of Unsupervised Anomaly Detection Techniques to Moisture Content Data from Wood Constructions

“…One of the main features of these is the presence of a high number of connected sensors, providing big amounts of time series data. Considerable research has already been done on how to effectively exploit the potential that this kind of data offers to automatically detect anomalies in complex systems [17].…”

IoT reliability: a review leading to 5 key research directions

“…Within the scope of this work, it is not possible to review all available anomaly detection methods, and as such, only the pertinent IoT examples are reviewed here in detail. A more detailed review of anomaly detection methods can be found within the literature (Zarpelão et al 2017;Moustafa et al 2019;Cook et al 2020;da Costa et al 2019).…”

Overview of Modern Time Series Management Systems

“…В работах [30] и [70] предложены алгоритмы поиска диссонансов в ряде, целиком размещенном в оперативной памяти, и для случая временного ряда, хранящегося на диске, соответственно. В настоящее время поиск аномалий во временных рядах является сферой интенсивных научных исследований (см., например, обзоры [8,9]).…”