Development of Outlier Detection Algorithms for Sensors with Time-Varying Characteristics

Lee, Chang-Ho; Lee, Jungmin; Lee, B. S.; Park, Jaeseok; Park, Jiyeon; Kim, Young-Hoon; Park, Jae Yong

doi:10.1109/asmc57536.2023.10121105

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…In [25], the focus is on finding faults in etching equipment used in Tool-to-Tool Matching (TTTM) in semiconductor manufacturing. Their approach focuses on rapid gradient shifting and deviations from the normal trend range.…”

Section: Recent Advances In Anomaly Detection (Ad) On Iot and Ad Usin...mentioning

confidence: 99%

Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records

DeMedeiros,

Koh,

Hendawi

2024

Future Internet

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The Chicago Array of Things (AoT) is a robust dataset taken from over 100 nodes over four years. Each node contains over a dozen sensors. The array contains a series of Internet of Things (IoT) devices with multiple heterogeneous sensors connected to a processing and storage backbone to collect data from across Chicago, IL, USA. The data collected include meteorological data such as temperature, humidity, and heat, as well as chemical data like CO2 concentration, PM2.5, and light intensity. The AoT sensor network is one of the largest open IoT systems available for researchers to utilize its data. Anomaly detection (AD) in IoT and sensor networks is an important tool to ensure that the ever-growing IoT ecosystem is protected from faulty data and sensors, as well as from attacking threats. Interestingly, an in-depth analysis of the Chicago AoT for anomaly detection is rare. Here, we study the viability of the Chicago AoT dataset to be used in anomaly detection by utilizing clustering techniques. We utilized K-Means, DBSCAN, and Hierarchical DBSCAN (H-DBSCAN) to determine the viability of labeling an unlabeled dataset at the sensor level. The results show that the clustering algorithm best suited for this task varies based on the density of the anomalous readings and the variability of the data points being clustered; however, at the sensor level, the K-Means algorithm, though simple, is better suited for the task of determining specific, at-a-glance anomalies than the more complex DBSCAN and HDBSCAN algorithms, though it comes with drawbacks.

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Section: Recent Advances In Anomaly Detection (Ad) On Iot and Ad Usin...mentioning

confidence: 99%

Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records

DeMedeiros,

Koh,

Hendawi

2024

Future Internet

View full text Add to dashboard Cite

show abstract

Development of Outlier Detection Algorithms for Sensors with Time-Varying Characteristics

Cited by 1 publication

References 6 publications

Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records

Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records

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