# 6G Internet of Things: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2108.04973 · HTML: https://mlsanity.com/paper/2108.04973/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2021.

## Built on by (most cited first, 30 of 39)

- [From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks](https://mlsanity.com/paper/2310.18382/) (2023)
- [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024)
- [Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity](https://mlsanity.com/paper/2112.10313/) (2021)
- [Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning](https://mlsanity.com/paper/2404.14497/) (2024)
- [GAC-KAN: An Ultra-Lightweight GNSS Interference Classifier for GenAI-Powered Consumer Edge Devices](https://mlsanity.com/paper/2602.11186/) (2026)
- [From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2411.01312/) (2024)
- [SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification](https://mlsanity.com/paper/2601.12791/) (2026)
- [PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition](https://mlsanity.com/paper/2601.12798/) (2026)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content](https://mlsanity.com/paper/2406.08526/) (2024)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management](https://mlsanity.com/paper/2603.29656/) (2026)
- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)
- [Internet of Things Device Capabilities, Architectures, Protocols, and Smart Applications in Healthcare Domain: A Review](https://mlsanity.com/paper/2204.05921/) (2022)
- [DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things](https://mlsanity.com/paper/2403.00321/) (2024)
- [RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1textpertenthousand Spatial Sampling](https://mlsanity.com/paper/2504.19161/) (2025)
- [AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery](https://mlsanity.com/paper/2309.16706/) (2023)
- [Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks](https://mlsanity.com/paper/2503.19549/) (2025)
- [Federated Learning with Differential Privacy: An Utility-Enhanced Approach](https://mlsanity.com/paper/2503.21154/) (2025)
- [Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting](https://mlsanity.com/paper/2506.12787/) (2025)
- [Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization](https://mlsanity.com/paper/2512.23493/) (2025)
- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026)
- [FiFo: Fishbone Forwarding in Massive IoT Networks](https://mlsanity.com/paper/2211.01213/) (2022)
- [Multi-Power Level Q-Learning Algorithm for Random Access in NOMA mMTC Systems](https://mlsanity.com/paper/2301.05196/) (2023)
- [An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange](https://mlsanity.com/paper/2312.08096/) (2023)
- [Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication](https://mlsanity.com/paper/2403.07255/) (2024)
- [SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things](https://mlsanity.com/paper/2409.12213/) (2024)
- [Federated Structured Sparse PCA for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2503.23981/) (2025)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)

## Abstract (arXiv)

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater communications, Terahertz communications, massive ultra-reliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.