# A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions

arXiv: https://arxiv.org/abs/2406.03820 · HTML: https://mlsanity.com/paper/2406.03820/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 416 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 30 of 33)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications](https://mlsanity.com/paper/1801.03239/) (2018)
- [FedLab: A Flexible Federated Learning Framework](https://mlsanity.com/paper/2107.11621/) (2021)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (2020)
- [Defeating Image Obfuscation with Deep Learning](https://mlsanity.com/paper/1609.00408/) (2016)
- [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://mlsanity.com/paper/1707.01926/) (2017)
- [Differentially Private Meta-Learning](https://mlsanity.com/paper/1909.05830/) (2019)
- [A Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2207.12515/) (2022)
- [Learning Differentially Private Recurrent Language Models](https://mlsanity.com/paper/1710.06963/) (2017)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [My House, My Rules: Learning Tidying Preferences with Graph Neural Networks](https://mlsanity.com/paper/2111.03112/) (2021)
- [SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining](https://mlsanity.com/paper/1805.00904/) (2018)
- [Semi-supervised Federated Learning for Activity Recognition](https://mlsanity.com/paper/2011.00851/) (2020)
- [Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods](https://mlsanity.com/paper/2003.09424/) (2020)
- [Differentially Private Imaging via Latent Space Manipulation](https://mlsanity.com/paper/2103.05472/) (2021)
- [Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning](https://mlsanity.com/paper/2210.06771/) (2022)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey](https://mlsanity.com/paper/2310.10549/) (2023)
- [Generating Artificial Data for Private Deep Learning](https://mlsanity.com/paper/1803.03148/) (2018)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses](https://mlsanity.com/paper/2306.10309/) (2023)
- [Cross-City Transfer Learning for Deep Spatio-Temporal Prediction](https://mlsanity.com/paper/1802.00386/) (2018)
- [Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications](https://mlsanity.com/paper/2105.13296/) (2021)

## Built on by (most cited first, 10 of 10)

- [Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices](https://mlsanity.com/paper/2603.19972/) (2026)
- [Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization](https://mlsanity.com/paper/2511.09775/) (2025)
- [Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles](https://mlsanity.com/paper/2501.00890/) (2025)
- [Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems](https://mlsanity.com/paper/2502.05425/) (2025)
- [Federated Structured Sparse PCA for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2503.23981/) (2025)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT](https://mlsanity.com/paper/2510.05180/) (2025)
- [Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions](https://mlsanity.com/paper/2510.22909/) (2025)
- [Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection](https://mlsanity.com/paper/2602.12622/) (2026)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)

## Abstract (arXiv)

The rapid advances in the Internet of Things (IoT) have promoted a revolution in communication technology and offered various customer services. Artificial intelligence (AI) techniques have been exploited to facilitate IoT operations and maximize their potential in modern application scenarios. In particular, the convergence of IoT and AI has led to a new networking paradigm called Intelligent IoT (IIoT), which has the potential to significantly transform businesses and industrial domains. This paper presents a comprehensive survey of IIoT by investigating its significant applications in mobile networks, as well as its associated security and privacy issues. Specifically, we explore and discuss the roles of IIoT in a wide range of key application domains, from smart healthcare and smart cities to smart transportation and smart industries. Through such extensive discussions, we investigate important security issues in IIoT networks, where network attacks, confidentiality, integrity, and intrusion are analyzed, along with a discussion of potential countermeasures. Privacy issues in IIoT networks were also surveyed and discussed, including data, location, and model privacy leakage. Finally, we outline several key challenges and highlight potential research directions in this important area.