Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

By Jing Zhang, Dacheng Tao. 2020

624 citations recorded by the source corpus.

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the Internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas, including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of AI of things (AIoT). This article presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: 1) perceiving; 2) learning; 3) reasoning; and 4) behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

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Citations in the corpus

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (2021, background): “Currently, deep convolutional neural networks [23, 11, 35] have made signiﬁcant breakthroughs in a large number of recognition tasks.”
ISNet: Shape Matters for Infrared Small Target Detection (2022, background): “Infrared small target detection (IRSTD) has a wide range of important applications such as traffic management and marine rescue [8, 33, 37].”
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Progressive One-shot Human Parsing (2020, positive): “Recent success in deep CNN has made great progress in multiple areas (Ronneberger, Fischer, and Brox 2015; Chen et al. 2017; Zhan et al. 2020; Zhang and Tao 2020; Zhan et al. 2020; Ma et al. 2020), including human parsing (Li et al. 2017; Zhao et al. 2017; Luo et al. 2018).”
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition (2021, neutral): “Scene Text Recognition (STR) remains a fundamental and active research topic in computer vision for its wide applications (Zhang and Tao 2020).”
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