anomaly detection

iot devices

anomalies

inertia

pcct

effector_119160

soils_45167

unsupervised

iot_60729

anomaly detection_60762

liquid ammonia

exports_44124

multivariate time series

anomaly detection_39539

iot_133584

geography_81457

iot_39569

lcas

korean children

identity_77542

IEEE Internet of Things Journal

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.

Anomaly Detection for IoT Time-Series Data: A Survey