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

arXiv: https://arxiv.org/abs/2312.16174 · HTML: https://mlsanity.com/paper/2312.16174/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 248 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Learning and Evaluating Representations for Deep One-class Classification](https://mlsanity.com/paper/2011.02578/) (2020)
- [SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2011.07215/) (2020)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Event-Driven Visual-Tactile Sensing and Learning for Robots](https://mlsanity.com/paper/2009.07083/) (2020)
- [Truncated Matrix Power Iteration for Differentiable DAG Learning](https://mlsanity.com/paper/2208.14571/) (2022)
- [Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications](https://mlsanity.com/paper/2008.06701/) (2020)
- [A Survey on Edge Computing Systems and Tools](https://mlsanity.com/paper/1911.02794/) (2019)
- [Resource Scheduling in Edge Computing: A Survey](https://mlsanity.com/paper/2108.08059/) (2021)
- [Dynamic Low-light Imaging with Quanta Image Sensors](https://mlsanity.com/paper/2007.08614/) (2020)
- [Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning](https://mlsanity.com/paper/2107.02339/) (2021)
- [When Deep Learning Meets Digital Image Correlation](https://mlsanity.com/paper/2009.03993/) (2020)
- [HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction](https://mlsanity.com/paper/2011.03614/) (2020)
- [Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research](https://mlsanity.com/paper/1803.07673/) (2018)
- [Single-shot fringe projection profilometry based on Deep Learning and Computer Graphics](https://mlsanity.com/paper/2101.00814/) (2021)
- [Diffeomorphic Particle Image Velocimetry](https://mlsanity.com/paper/2108.07438/) (2021)

## Built on by (most cited first, 8 of 8)

- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems](https://mlsanity.com/paper/2512.05971/) (2025)
- [Federated Progressive Self-Distillation with Logits Calibration for Personalized IIoT Edge Intelligence](https://mlsanity.com/paper/2412.00410/) (2024)
- [PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIV](https://mlsanity.com/paper/2504.14952/) (2025)
- [Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things](https://mlsanity.com/paper/2512.20627/) (2025)
- [Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game](https://mlsanity.com/paper/2502.06909/) (2025)
- [An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0](https://mlsanity.com/paper/2510.25813/) (2025)
- [IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems](https://mlsanity.com/paper/2606.01691/) (2026)

## 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.