mlsanityData to 14 Oct 2026

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

arXiv 2406.03820 ↗First posted 6 Jun 2024cs.NI cs.AI cs.CRcited by 10

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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.

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

33 of 416 references.

References · 33

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21 more
  1. 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecastingcited by 41
  2. 2019Differentially Private Meta-Learningcited by 41
  3. 2022A Survey on Trustworthy Recommender Systemscited by 41
  4. 2017Learning Differentially Private Recurrent Language Modelscited by 36
  5. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
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  7. 2018SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Miningcited by 21
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  21. 2023Learn to Unlearn: A Survey on Machine Unlearningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2026Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devicescited by 2
  2. 2025Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularizationcited by 1
  3. 2025Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehiclescited by 0
  4. 2025Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systemscited by 0
  5. 2025Federated Structured Sparse PCA for Anomaly Detection in IoT Networkscited by 0
  6. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  7. 2025OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoTcited by 0
  8. 2025Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directionscited by 0
  9. 2026Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detectioncited by 0
  10. 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmapcited by 0