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Trends and Challenges in AIoT/IIoT/IoT Implementation.

Kun Mean Hou1, Xunxing Diao2, Hongling Shi3

  • 1Université Clermont-Auvergne, CNRS, Mines de Saint-Étienne, Clermont-Auvergne-INP, LIMOS, F-63000 Clermont-Ferrand, France.

Sensors (Basel, Switzerland)
|June 10, 2023
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Summary
This summary is machine-generated.

The Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) are key for future technologies like the metaverse and autonomous vehicles. This study analyzes AIoT trends, challenges, and a TinyML case study for disease detection.

Keywords:
AIoT platformANNIMCKerasOpenThreadSNNTensorFlowTensorFlow LiteTensorFlow for microcontrollerTinyMLZephyrdigital twinmetaverseneuromorphic computing

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Area of Science:

  • * Emerging technologies: Metaverse, Digital Twin, Autonomous Vehicles.
  • * Enabling technologies: AIoT/IIoT/IoT, process modeling, supercomputing, cloud data analytics, communication networks, AI.
  • * Application domains: Health and life sciences, smart home/agriculture/city/car, logistics, Industry 4.0, entertainment, social media.

Background:

  • * AIoT/IIoT/IoT provides essential data for advanced applications like metaverse, digital twins, Industry 4.0, and autonomous vehicles.
  • * The multidisciplinary nature of AIoT makes its evolution and impact challenging to comprehend.
  • * Rapid advancements in AIoT/IIoT/IoT technologies are driving innovation across various sectors.

Purpose of the Study:

  • * To analyze and highlight trends and challenges within the AIoT technology ecosystem.
  • * To explore the core hardware, software, and middleware components of AIoT.
  • * To investigate the potential of low-powered AI technologies like TinyML and neuromorphic computing.

Main Methods:

  • * Analysis of the AIoT technology ecosystem, including hardware (MCU, sensors, wireless), software (OS, protocols), and middleware (TinyML).
  • * Exploration of emerging low-powered AI technologies: TinyML and neuromorphic computing.
  • * Case study: Implementation of an AIoT/IIoT/IoT device using TinyML for strawberry disease detection.

Main Results:

  • * Identification of key trends and challenges in the AIoT ecosystem.
  • * Demonstration of a TinyML-based AIoT device for practical application (strawberry disease detection).
  • * Highlighting the emergence of TinyML and neuromorphic computing as low-power AI solutions.

Conclusions:

  • * AIoT/IIoT/IoT technologies are foundational for next-generation applications.
  • * Challenges in safety, security, latency, interoperability, and data reliability must be addressed for widespread adoption.
  • * TinyML presents a viable approach for implementing AI on resource-constrained IoT devices.