In traditional IoT scenarios, Specific-Purpose Sensor (SPS) or Distributed Multi-Sensor (DMS) are usually deployed directly on different locations or objects to sense diverse purposes, However, the number of sensing purposes always conflict with the complexity of networking and deployment. Integrated Multi-Sensor Tag (IMST) alleviates this problem, such as Texas Instruments SimpleLink SensorTag, and Laput's Synthetic Sensors, with multiple sensors integrated on a small board to indirectly monitor a large context, without direct instrumentation of objects. But due to the limited computing power of IMST, a large amount of raw data still needs to be sent to the remote server for processing through wireless technologies such as Bluetooth or WiFi, which may lead to processing delays, data leakage or intrusion.


With the rapid increase in computing power of end devices, many machine learning models are popular for inference on end devices. With the help of real-time data from sensors, the activities that are happening in the environment can be analyzed and recognized in real-time, which has a lot of demand and promise in the field of autonomous driving, sports, healthcare, etc.



Edge Computing Sensor Kit, Information Fusion, Machine Learning, Real-time Activity Recognition, Digital Twin, Data Visualization.



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