@inproceedings{b0770c348f9d4877bc939e4c45b09c7c,
title = "SENSELET++: A Low-cost Internet of Things Sensing Platform for Academic Cleanrooms",
abstract = "Sensory IoT (Internet of Things) networks are widely applied and studied in recent years and have demonstrated their unique benefits in various areas. In this paper, we bring the sensor network to an application scenario that has rarely been studied - the academic cleanrooms. We design SENSELET++, a low-cost IoT sensing platform that can collect, manage and analyze a large amount of sensory data from heterogeneous sensors. Furthermore, we design a novel hybrid anomaly detection framework which can detect both time-critical and complex non-critical anomalies. We validate SENSELET++ through the deployment of the sensing platform in a lithography cleanroom. Our results show the scalability, flexibility, and reliability properties of the system design. Also, using real-world sensory data collected by SENSELET++, our system can analyze data streams in real-time and detect shape and trend anomalies with a 91% true positive rate.",
keywords = "Anomaly Detection, Internet of Things, Sensor Network",
author = "Beitong Tian and Zhe Yang and Hessam Moeini and Ragini Gupta and Patrick Su and Robert Kaufman and Mark McCollum and John Dallesasse and Klara Nahrstedt",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021 ; Conference date: 04-10-2021 Through 07-10-2021",
year = "2021",
doi = "10.1109/MASS52906.2021.00020",
language = "English (US)",
series = "Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "90--98",
booktitle = "Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021",
address = "United States",
}