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| Title | Trends And Challenges In Aiot/Iiot/Iot Implementation |
|---|---|
| ID_Doc | 58981 |
| Authors | Hou K.M.; Diao X.; Shi H.; Ding H.; Zhou H.; de Vaulx C. |
| Year | 2023 |
| Published | Sensors, 23, 11 |
| DOI | http://dx.doi.org/10.3390/s23115074 |
| Abstract | 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. © 2023 by the authors. |
| Author Keywords | AIoT platform; ANN; digital twin; IMC; Keras; metaverse; neuromorphic computing; OpenThread; SNN; TensorFlow; TensorFlow for microcontroller; TensorFlow Lite; TinyML; Zephyr |
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