Low-power Artificial Intelligence of Things(AIoT) Systems

Internet-of-Things (IoT) is another excellent innovation after the Internet and mobile networks in the information era, aiming at connecting billions of end-devices across scales. A multitude of IoT applications often operate under conditions of constrained energy resources, which has rendered low-power IoT systems a subject of considerable research interest. The increasing need for AI in complex scenario-based composite tasks has led to the rise of Artificial Intelligence of Things(AIoT), which encompasses research in two major directions: AI for IoT that solves problems in IoT systems with AI techniques and IoT for AI that adopts IoT infrastructure/data to advance the development of AI models. While AIoT systems in low-power scenarios offer significant benefits, they also face specific challenges that are inherent to their design and operational requirements. This dissertation delves into low-power AIoT from both angles. 1) We endeavor to harness the capabilities of AI to predict and analyze the communication channels of dynamic long links in LoRaWAN, which is one of the Low-power Wide-area Networks(LPWANs). DeepLoRa adopts Deep Neural Networks based on Bi-directional LSTM(Long-Short-Time-Memory) to capture the sequential information of environmental influence on LoRa link performances for accurate LoRa link path-loss estimation. It reduces the path-loss estimation error to less than 4 dB, which is 2x smaller than state-of-the-art models. LoSee extends the contributions of DeepLoRa. It measures the real-world fine-grained performance, including detailed coverage study and feasibility analysis of fingerprint-based localization, of a self-deployed LoRaWAN system with temporal dynamics and spatial dynamics. 2) We design energy-efficient IoT systems that facilitate the deployment of AI models for practical applications. FaceTouch enables accurate face touch detection with a multimodal wearable system consisting of an inertial sensor on the wrist and a novel vibration sensor on the finger. We leverage a cascading classification model, including simple filters and a DNN, to significantly extend the battery life while keeping a high recall. FaceTouch achieves a 93.5% F-1 score and can continuously detect face-touch events for 79 – 273 days using a small 400 mWh battery, depending on usage. In general, this dissertation studies both theoretical and practical aspects in the field of low-power AIoT systems, including LoRaWAN link behavior analysis and building practical wearable systems. These advancements not only underscore the feasibility of deploying low-power AIoT in real-world settings but also pave the way for future research and development in this domain, aiming to bridge the gap between IoT and AI for the creation of smarter, sustainable, and more efficient technologies.