Hou Kun Mean, Diao Xunxing, Shi Hongling, Ding Hao, Zhou Haiying, de Vaulx Christophe
Université Clermont-Auvergne, CNRS, Mines de Saint-Étienne, Clermont-Auvergne-INP, LIMOS, F-63000 Clermont-Ferrand, France.
uSuTech Company, 63173 Aubière, France.
Sensors (Basel). 2023 May 25;23(11):5074. doi: 10.3390/s23115074.
For the next coming years, metaverse, digital twin and autonomous vehicle applications are the leading technologies for many complex applications hitherto inaccessible such as health and life sciences, smart home, smart agriculture, smart city, smart car and logistics, Industry 4.0, entertainment (video game) and social media applications, due to recent tremendous developments in process modeling, supercomputing, cloud data analytics (deep learning, etc.), communication network and AIoT/IIoT/IoT technologies. AIoT/IIoT/IoT is a crucial research field because it provides the essential data to fuel metaverse, digital twin, real-time Industry 4.0 and autonomous vehicle applications. However, the science of AIoT is inherently multidisciplinary, and therefore, it is difficult for readers to understand its evolution and impacts. Our main contribution in this article is to analyze and highlight the trends and challenges of the AIoT technology ecosystem including core hardware (MCU, MEMS/NEMS sensors and wireless access medium), core software (operating system and protocol communication stack) and middleware (deep learning on a microcontroller: TinyML). Two low-powered AI technologies emerge: TinyML and neuromorphic computing, but only one AIoT/IIoT/IoT device implementation using TinyML dedicated to strawberry disease detection as a case study. So far, despite the very rapid progress of AIoT/IIoT/IoT technologies, several challenges remain to be overcome such as safety, security, latency, interoperability and reliability of sensor data, which are essential characteristics to meet the requirements of metaverse, digital twin, autonomous vehicle and Industry 4.0. applications.
在未来几年中，元宇宙、数字孪生和自动驾驶汽车应用将成为许多复杂应用的领先技术，这些应用在健康与生命科学、智能家居、智能农业、智慧城市、智能汽车与物流、工业4.0、娱乐（视频游戏）和社交媒体应用等领域，由于最近在过程建模、超级计算、云数据分析（深度学习等）、通信网络以及人工智能物联网/工业物联网/物联网技术方面取得了巨大进展，此前一直无法实现。人工智能物联网/工业物联网/物联网是一个关键的研究领域，因为它为推动元宇宙、数字孪生、实时工业4.0和自动驾驶汽车应用提供了至关重要的数据。然而，人工智能物联网科学本质上是多学科的，因此读者很难理解其发展历程和影响。本文的主要贡献在于分析并突出人工智能物联网技术生态系统的趋势和挑战，包括核心硬件（微控制器、微机电系统/纳米机电系统传感器和无线接入介质）、核心软件（操作系统和协议通信栈）以及中间件（微控制器上的深度学习：TinyML）。出现了两种低功耗人工智能技术：TinyML和神经形态计算，但仅以一个使用TinyML专门用于草莓病害检测的人工智能物联网/工业物联网/物联网设备实现作为案例研究。到目前为止，尽管人工智能物联网/工业物联网/物联网技术取得了非常迅速的进展，但仍有若干挑战有待克服，例如传感器数据的安全性、可靠性、延迟、互操作性和可靠性，这些都是满足元宇宙、数字孪生、自动驾驶汽车和工业4.0应用要求的关键特性。
