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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.
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Senselet: Distributed Sensing Infrastructure for Improving Process Control and Safety in Academic Cleanroom Environments
Semiconductor cleanrooms are used to fabricate devices with feature sizes that can be much smaller than a dust particle. Hence, any environmental deviations in temperature, or humidity around fabrication instruments may become the root cause of hundreds of transistors failing during the manufacturing. Furthermore, researchers work with dangerous chemicals in cleanrooms and violation of safety may lead to disastrous consequences. Therefore, we have developed an affordable, locally-controlled distributed sensing infrastructure, called SENSELET, for academic cleanrooms. It provides highly effective services for environment sensing around scientific instruments, sensory data collection and visualization, indoor localization, and instrument proximity detection for safety of researchers.
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- Award ID(s):
- 1835543
- PAR ID:
- 10292021
- Date Published:
- Journal Name:
- GetMobile: Mobile Computing and Communications
- Volume:
- 24
- Issue:
- 2
- ISSN:
- 2375-0529
- Page Range / eLocation ID:
- 12 to 16
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3427384.3427388
