Anomaly Detection for IoT Time-Series Data: A Survey

By Andrew Cook, Goksel Misirli, Zhong Fan. IEEE Internet of Things Journal, 2020

650 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/30d65fbc8d71ac202ee8d7d5f0e58c63a6c6a957

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ming Jin
2. Liang Sun
3. Tian Zhou
4. Nada Sahlab
5. Xiuzhen Cheng
6. Benjamin Maschler
7. Geoffrey I. Webb
8. Cesare Alippi

In Signal Processing

1. Tian Zhou
2. Liang Sun
3. Geoffrey I. Webb
4. Cesare Alippi
5. Jihun Yi
6. Phillip Wenig
7. Yiyuan Yang
8. Kukjin Choi

Citations in the corpus

• A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, background): “The survey conducted in [7] classifies existing detection approaches from the viewpoint of time series characteristics, namely approaches for uni-and multivariate time series data.”
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “Time series analytics have become increasingly crucial in various fields, including but not limited to cloud computing, transportation, energy, finance, social networks, and the Internet-of-Things [8], [9], [10].”
• Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, background): “Autonomous collection devices, in turn, reduce the requirement for manual meter readings [11].”
• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, background): “Time series anomaly detection is widely used in real-world applications, including but not limited to industrial equipment status monitoring, financial fraud detection, fault diagnosis, and daily monitoring and maintenance of automobiles [3, 20, 27, 57, 79].”
• Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, background): “The different anomaly types, pattern models, and time series properties led to the development of amultitude of different anomaly detection algorithms, about which various surveys exist [12, 20, 27, 28, 29, 37, 38, 53, 63].”
• Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT (2021, background): “In real-world scenarios, the time series data acquisition sources could be multiple [34].”