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   https://doi.org/10.1145/3268935.3268937  

   Liu, Renju; Srivastava, Mani (January 2018, Proceedings of the 3rd Workshop on System Software for Trusted Execution (SysTEX '18))

   Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment. 

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2. SENSELET++: A Low-cost Internet of Things Sensing Platform for Academic Cleanrooms

   https://doi.org/10.1109/MASS52906.2021.00020  

   Tian, Beitong; Yang, Zhe; Moeini, Hessam; Gupta, Ragini; Su, Patrick; Kaufman, Robert; McCollum, Mark; Dallesasse, John; Nahrstedt, Klara (October 2021, 021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2021,)

   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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3. RF Sensing in the Internet of Things: A General Deep Learning Framework

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4. Dataset: User side acquisition of People-Centric Sensing in the Internet-of-Things

   https://doi.org/10.1145/3359427.3361914  

   Liu, Chenguang; Hua, Jie; Kalbarczyk, Tomasz; Lee, Sangsu; Julien, Christine (January 2019, Proceedings of the 2nd Workshop on Data Acquisition To Analysis)
5. PRSS: A Prejudiced Random Sensing Strategy for Energy-Efficient Information Collection in the Internet of Things

   https://doi.org/10.1109/JIOT.2018.2873756  

   Sun, Peng; Tian, Zhi; Wang, Zhibo; Wang, Zhi (October 2018, IEEE Internet of Things Journal)