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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. 

Indoor localization based on Wi-Fi fingerprints has been an active research topic for years. However, existing approaches do not consider the instability of access points (APs) which may be unreliable in practice, particularly the ones deployed by individual users. This instability impacts the localization accuracy severely, due to the unreliable or even wrong Wi-Fi fingerprints. Ideally, the localization should be done using only the well-deployed APs (e.g., deployed by facility teams). However, in many places the number of these APs is too few to achieve a good localization accuracy. To solve this problem, we leverage emerging smart APs equipped with multi-mode antennas, and build a new indoor localization system called MMLOC to reduce the number of necessary APs. The key idea is controlling the modes of AP antennas to generate more fingerprints with fewer APs. A clustering based localization strategy is designed to enable a mobile terminal to figure out the RSSI (Received Signal Strength Indicator) for different antenna modes without requiring any synchronization. We have implemented a prototype system using smart APs and commercial smartphones. Experimental results demonstrate that MMLOC can reduce the number of necessary APs by 50%, and achieve the same or even better localization accuracy. 

Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.