Evidence map›Paper›PMID 37299800›Full record

ArticleSensors (Basel, Switzerland)2023

Trends and Challenges in AIoT/IIoT/IoT Implementation.

Kun Mean Hou, Xunxing Diao, Hongling Shi, Hao Ding, Haiying Zhou, Christophe de Vaulx

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Two-Dimensional Flash Memory: Materials, Interfaces, and Storage-Computing Convergence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Digital Twins in Agriculture: Orchestration and Applications.Journal of agricultural and food chemistry · 2024
    Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kun Mean HouUniversité Clermont-Auvergne, CNRS, Mines de Saint-Étienne, Clermont-Auvergne-INP, LIMOS, F-63000 Clermont-Ferrand, France.
Xunxing DiaouSuTech Company, 63173 Aubière, France.
Hongling ShiCollege of Electronics and Information Engineering, South Central Minzu University (SCMZU), Wuhan 430070, China.ORCID 0000-0002-1309-3934
Hao DingCollege of Electronics and Information Engineering, South Central Minzu University (SCMZU), Wuhan 430070, China.
Haiying ZhouDong Feng Company, Wuhan 430050, China.ORCID 0000-0002-3637-9652
Christophe de VaulxUniversité Clermont-Auvergne, CNRS, Mines de Saint-Étienne, Clermont-Auvergne-INP, LIMOS, F-63000 Clermont-Ferrand, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AIoT platformANNdigital twinIMCKerasmetaverseneuromorphic computingOpenThreadSNNTensorFlowTensorFlow for microcontrollerTensorFlow LiteTinyMLZephyr

Identifiers

PMID37299800
PMCPMC10255551

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.