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. 2023 May 25;23(11):5074.
doi: 10.3390/s23115074.

Trends and Challenges in AIoT/IIoT/IoT Implementation

Kun Mean Hou  1 , Xunxing Diao  2 , Hongling Shi  3 , Hao Ding  3 , Haiying Zhou  4 , Christophe de Vaulx  1
Affiliations

Trends and Challenges in AIoT/IIoT/IoT Implementation

Kun Mean Hou et al. Sensors (Basel). .

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.

Keywords: AIoT platform; ANN; IMC; Keras; OpenThread; SNN; TensorFlow; TensorFlow Lite; TensorFlow for microcontroller; TinyML; Zephyr; digital twin; metaverse; neuromorphic computing.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 3
Figure 3
A typical structure of a biological neuron and synapse [22].
Figure 1
Figure 1
IoT platform.
Figure 2
Figure 2
Basic AIoT platform.
Figure 4
Figure 4
Basic hardware architecture of AIoT/IIoT/IoT devices.
Figure 5
Figure 5
Cadence multi-IP co-design tools.
Figure 6
Figure 6
(a) MEMS and CMOS in the same substrate [47]; (b) MEMS/NEMS compatible CMOS switch [48].
Figure 7
Figure 7
Zephyr firmware structure [52].
Figure 8
Figure 8
(a) Basic Thread network topology; (b) OpenThread devices; (c) matter application layer.
Figure 9
Figure 9
TensorFlow framework for microcontrollers.
Figure 10
Figure 10
µTVM toolchain [63].
Figure 11
Figure 11
Deep learning development workflow to target the ST MCU portfolio.
Figure 12
Figure 12
(a) Healthy leaves; (b) scorched leaves.
Figure 13
Figure 13
MCU resource requirement validated using the X-CUBE-AI expansion pack of STM32CubeMX.
Figure 14
Figure 14
Application memory code sizes generated by STM32CubeIDE.
Figure 15
Figure 15
C code model displayed on the STM32CubeIDE screen.
Figure 16
Figure 16
Results of embedded inference running on the STL32L4S5i discovery kit (screen copy).
Figure 17
Figure 17
Bloc diagram of a low-power and low-cost BLE AIoT device dedicated to plant disease detection.
Figure 18
Figure 18
AIoT device integrating analog switch.
See this image and copyright information in PMC

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