mlsanityData to 7 Oct 2026

Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review

arXiv 2312.16174 ↗First posted 2 Dec 2023cs.AI cs.CYcited by 8

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Abstract (arXiv)

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It's time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 248 references that are in the map. First posted on arXiv 2 Dec 2023.

Builds on

21 of 248 references.

References · 21

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9 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 1.

8 papers.

  1. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  2. 2025A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systemscited by 2
  3. 2024Federated Progressive Self-Distillation with Logits Calibration for Personalized IIoT Edge Intelligencecited by 1
  4. 2025PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVcited by 1
  5. 2025Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Gamecited by 0
  6. 2025An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0cited by 0
  7. 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Thingscited by 0
  8. 2026IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systemscited by 0