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6G Internet of Things: A Comprehensive Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor

arXiv:2108.04973v1eess.SP

TL;DR

The paper addresses the lack of a comprehensive survey of how 6G technologies support IoT networks and applications. It conducts a holistic survey spanning enabling technologies and five application domains, then identifies research challenges and future directions.

  • Problem

    Existing literature lacks a comprehensive, dedicated survey integrating 6G technologies with IoT networks and applications.

  • Method

    The paper holistically surveys six enabling technologies and their roles across healthcare, vehicular, UAV, satellite, and industrial IoT domains.

  • Results

    The survey identifies key technical aspects, emerging use cases, research challenges, and possible directions for future 6G-IoT development.

  • Takeaways & Limitations

    6G-IoT research spans intelligence, communications, security, application integration, energy efficiency, and standardization.

Abstract

from arXiv · show

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.

I. INTRODUCTION

IoT has become a major platform for connected sensing, computation, and automated management, while 6G is envisioned to extend IoT capabilities beyond 5G. The paper surveys this emerging 6G-IoT convergence and its prospective applications.

  • IoT connects heterogeneous physical objects through ubiquitous sensing and computing, enabling communication and automated management without human intervention.
  • 5G supports IoT through enhanced broadband, massive machine-type communication, and ultra-reliable low-latency communication services.These services provide high throughput, low latency, and energy-efficient service provision.
  • 6G is expected to provide ultra-low latency, extremely high throughput, satellite-based services, and massive autonomous networks for future IoT systems.
  • South Korea selected digital healthcare, self-driving cars, smart cities, and smart factories for a 6G pilot project.The project planned pilot services from 2026, initial deployments in 2028, and mass commercialization in 2030.

A. Comparison and Our Key Contributions

The paper addresses a gap in prior 6G surveys by providing a dedicated, holistic review of 6G-IoT integration. It combines enabling technologies, application domains, technical requirements, and future research challenges.

  • Prior surveys discuss 6G technologies, use cases, or network advances, but comprehensive coverage of 6G-IoT integration and applications remains missing.
  • The article surveys six enabling technologies: edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater communications, THz communications, massive URLLC, and blockchain.
  • Its application survey covers healthcare, unmanned aerial vehicles, vehicular IoT and autonomous driving, satellite IoT, and industrial IoT.
  • The survey concludes by identifying research challenges and possible directions toward realizing 6G-IoT.
  • 6G-IoT is motivated by projected mobile traffic of 5016 EB per month in 2030, compared with 62 EB per month in 2020.
  • 6G-IoT requirements include 10^7 devices/km^2 connectivity density and 10-100 µs network latency for applications such as e-health and autonomous driving.

B. Internet of Things (IoT)

IoT integrates heterogeneous devices that automatically sense, compute, and communicate data for end users. Its projected growth motivates new 6G-IoT capabilities and features.

  • IoT connects sensors, actuators, smartphones, computers, and RFIDs to the Internet for automated data sensing, computation, and communication.
  • 6G-IoT is presented as adding new features beyond 5G-IoT to support the expanding connected-device ecosystem.
  • Up to 500 billion IoT devices are expected to connect to the Internet by 2030, compared with 26 billion in 2020.

C. Requirements of 6G-IoT

6G-IoT must support the future intelligent information society through massive connectivity, data-driven services, and autonomous systems. These goals require stricter requirements than 5G-IoT.

  • 6G-IoT is intended to support full intelligence, massive device connectivity and coverage, data-driven services, and autonomous systems by 2030.
  • The paper frames 6G-IoT as requiring more stringent requirements than its 5G-IoT counterpart.

1) Massive IoT Connectivity:

Future 6G-IoT networks must support massive traffic, stringent requirements, and diverse coverage environments. The envisioned architecture combines device-centric intelligence with space-air-ground-underwater connectivity and autonomous-network capabilities.

  • Global mobile traffic is predicted to exceed 5000 exabytes in 2030, 80 times the 2020 level.
  • Massive URLLC is considered necessary for emerging applications including fully autonomous IoT, flying IoT, and real-time autonomous transportation.
  • 6G-IoT must simultaneously support stringent requirements from applications such as autonomous driving and e-healthcare.
  • Full wireless coverage is envisioned through large-dimensional space-air-ground-underwater networks, with edge intelligence and UAVs extending intelligent services and network reach.
  • Future smart devices may perform edge intelligence and computing while actively participating in network management and operation.

III. FUNDAMENTAL TECHNOLOGIES FOR 6G-IOT

The survey identifies six fundamental technologies for enabling 6G-IoT and examines edge intelligence as a central mechanism for distributed, privacy-aware, and low-latency learning. It also discusses hardware-based AI and associated security risks.

  • The survey focuses on edge intelligence, RISs, space-air-ground-underwater communications, THz communications, mURLLC, and blockchain as technologies directly supporting 6G-IoT.
  • Edge intelligence combines AI, communications, and edge computation to support learning and service identification near IoT devices.
  • Federated learning lets distributed IoT devices train neural networks by exchanging parameters without sharing raw data, improving privacy and reducing remote data offloading.
  • On-device AI enables low-latency learning and inference for IoT applications on mobile computers, smartphones, and edge servers.
  • 95.3% learning accuracy was achieved by a mobile BNN architecture, which also improved storage and CPU usage by up to 20%.
  • Edge intelligence remains vulnerable to data theft, parameter modification, and privacy breaches from untrusted devices, motivating blockchain-based security approaches.

B. Reconfigurable Intelligent Surfaces

RISs are presented as software-controlled passive surfaces that reshape wireless propagation for 6G-IoT. Their applications include interference reduction, improved offloading, smart-building connectivity, and broader multi-tier coverage.

  • RISs use electronically controlled passive scattering elements to reflect electromagnetic waves and create software-defined radio environments.
  • RIS deployment can reduce inter-cell interference among massive IoT devices and increase data offloading toward edge servers.
  • In smart buildings, RISs can connect indoor and outdoor entities while supporting interference avoidance and spectral-efficiency improvements.
  • 6G-IoT is envisioned as a unified platform spanning space, air, terrestrial, and underwater communication tiers for broad coverage and ubiquitous connectivity.
  • The space tier uses LEO, MEO, and GEO satellites, while the air tier uses UAVs and balloons as flying base stations for coverage and emergency connectivity.
  • The terrestrial tier targets high-speed, high-spectral-efficiency communication using THz bands, whereas the underwater tier connects devices such as submarines with control hubs.
  • A multiuser LEO satellite IoT design integrates mobile edge computing with full-duplex access points to improve latency efficiency for mission-critical applications.

D. Terahertz (THz) Communications

THz communications are presented as a driving 6G-IoT technology for extreme data rates and low latency, with hybrid beamforming requiring trade-offs among efficiency and hardware cost. AI-based approaches are identified for addressing mobility-related channel variation.

  • THz communications target 100+ Gbps data rates and 1-millisecond latency for future 6G-IoT applications.The envisioned 0.1–10 THz band also supports picosecond-level symbol duration and thousands of submillimeter-long antennas.
  • Hybrid beamforming in THz vehicular networks balances spectral efficiency, energy efficiency, and hardware costs across antenna-array structures.The comparison considers fully-connected, sub-connected, and overlapped subarray configurations while accounting for transmit power consumption.
  • Dynamic DRL algorithms are suggested for future time-varying THz communication problems caused by high vehicle mobility.
  • mURLLC supports timely, highly reliable health-data delivery and mission-critical automation in smart factories.
  • DRL optimizes IoT sub-channel assignment and transmission-power control through device-level observations and a QoS-aware reward function.In an environment with 2000 devices, reliability requirements ranged from 99.9% to 99.99999%, and DRL achieved better energy efficiency than random approaches.
  • mURLLC design must reduce access, scheduling, allocation, packet-error, and energy costs while maintaining low latency and reliability.The passage notes that conventional HARQ processing is unsuitable for achieving a low block error rate in this setting.

F. Blockchain

Blockchain is surveyed as a decentralized mechanism for addressing security, privacy, trust, and data-sharing challenges in distributed 6G-IoT systems. Its applications span UAVs and healthcare, but mining and information exchange can impose latency and energy costs.

  • Distributed 6G-IoT systems face heightened security and privacy risks, especially in open multi-layer data-sharing environments.
  • Blockchain provides a decentralized, immutable, and transparent database in which IoT entities share control over stored data without a central authority.Public and private blockchains differ in whether participation and consensus are open or permissioned.
  • Blockchain-based UAV systems use peer-to-peer ledgers to support trusted data exchange with ground stations during missions such as emergency search and environmental monitoring.UAVs, terrestrial users, and network operators receive shared control and tracing rights over ledger data.
  • Blockchain smart contracts can verify healthcare data without a third party while maintaining a high degree of trust.
  • Blockchain mining and repeated miner communication may increase network delay and energy consumption in 6G-IoT applications.Ethereum’s Proof-of-Work mining is described as computationally intensive and time-consuming.
  • The survey organizes fundamental 6G technologies by their key features and IoT use cases in a taxonomy.

B. 6G for Vehicular Internet of Things (VIoT) and and Autonomous Driving

6G technologies support VIoT through scalable V2X connectivity, edge intelligence, and deep learning for vehicular networking. These capabilities also support autonomous-driving coordination and communication-performance prediction.

  • VIoT: mMTC enables V2X connectivity for many vehicles transmitting short information payloads without human interaction.The design addresses a trade-off among scalability, reliability, and other V2X requirements.
  • VIoT: Edge intelligence at roadside units estimates traffic volume and weather conditions from aggregated vehicle observations.Distributed estimation can additionally allow local estimation at individual vehicles.
  • VIoT: Deep learning models vehicular communication channels and supports networking management, including DRL-based resource allocation.A trust broker is proposed because training data and reasoning are treated as a black-box process.
  • Autonomous Driving: 6G is expected to address autonomous-driving requirements for reliable, high-speed communications while improving transportation quality, road safety, and energy efficiency.
  • Autonomous Driving: Cooperative driving shares information and coordinates vehicles, while deep learning predicts V2V communication-performance bounds for intelligent inter-vehicle control.Edge intelligence is also identified as important for autonomous-driving systems.

C. 6G for Unmanned Aerial Vehicles (UAVs)

6G-UAV and satellite-IoT research applies optimization, NOMA, AI, and THz-enabled satellite links to improve aerial networking, coverage, relay operation, and IoT data collection. Key challenges include energy management and spectrum sharing.

  • UAVs: Cell-free 6G-UAV networks formulate data-transmission-efficiency maximization around flight-process optimization.The optimization considers channel state information and onboard energy.
  • UAVs: Clustered NOMA supports wireless-powered 6G-IoT communications by clustering terminals and jointly optimizing UAV trajectory planning and subslot allocation.
  • UAVs: UAVs can act as mobile relays in NOMA-based cognitive 6G-IoT networks, with relay selection optimized for higher transmission rates under fixed power.
  • UAVs: AI enables UAV wireless communication, edge computing, edge caching, mobility control, and mission scheduling based on predicted user and service-area demand.
  • Satellite IoT: Satellite-IoT integrates satellite communications into wireless networks to provide massive IoT coverage across LEO, MEO, and GEO tiers.Inter-satellite THz links are envisioned to support more satellites and higher link performance.
  • Satellite IoT: LEO satellite networks can support UAV navigation for IoT data collection through UAV carry-store and satellite-network relay communication modes.
  • Satellite IoT: Energy management and spectrum sharing remain critical satellite-IoT issues, with NOMA and cognitive radio proposed to improve spectrum efficiency and dynamic sharing.

E. 6G for Industrial Internet of Things (IIoT)

6G technologies are surveyed as enablers for Industrial Internet of Things networks, with applications spanning intelligent resource management, agriculture, security, and trustworthy learning.

  • Intelligent IIoT: Machine-learning CNNs can optimize resource allocation and intelligently cluster sensors in massive 6G-based IIoT systems.The approach addresses unnecessary energy costs caused by random sensor deployment and uses data mining, prediction, and neural backpropagation.
  • Intelligent agriculture: AI can optimize agricultural processes, while blockchain supports secure production and logistics through immutable block ledgers.UAV-enabled space-terrestrial communications can additionally support aerial soil measurement over large-scale agricultural areas.
  • Security and trust: Blockchain is presented as a promising means to provide security and trust in 6G-based IIoT networks facing significant security and privacy challenges.The survey describes blockchain-based secure data aggregation and other security-enhancement solutions.
  • Federated learning: Digital twins can bridge physical IIoT systems and the digital world to support robust federated-learning training over unreliable, long-distance links.The federated-learning process involves collaboration between end users and a base station.
  • Survey organization: The survey organizes 6G-IoT applications in a taxonomy table to clarify the technical aspects of reference works.The section summarizes applications across the surveyed IIoT-related research.

V. RESEARCH CHALLENGES AND FUTURE DIRECTIONS

The survey identifies security, energy efficiency, and device capabilities as major challenges for future 6G-IoT networks, and discusses privacy protection and renewable-energy approaches.

  • Security and privacy: 6G-IoT integration may expose wireless interfaces, computing units, access infrastructure, and data centres to unauthorized access, integrity threats, and denial-of-service attacks.The survey therefore emphasizes risk mitigation to preserve security and privacy.
  • Security and privacy: Differential privacy can improve data protection by up to 6% over traditional Laplace differential privacy approaches, but it can degrade training quality.The reported result applies under various privacy budget settings and motivates accuracy-aware privacy methods.
  • Energy efficiency: Energy efficiency is a major concern because communications, service delivery, and massive base-station deployments require substantial energy and increase carbon emissions.Each base station normally consumes 2.5 kW to 4 kW, making green communication protocols important for large-scale 6G-IoT.
  • Energy efficiency: Optimization can jointly address quality of service and energy consumption in 6G-based smart automation systems.The described multimedia transmission model considers packet loss ratio and average transfer delay as QoS parameters.
  • Energy efficiency: Renewable-energy harvesting from wind, solar, vibration, and thermal sources is proposed for building greener 6G-IoT systems.A healthcare example uses solar power harvested by implantable sensors for sensory-data transmission through a Bluetooth Low Energy module.

C. Hardware Constraints of IoT Devices

Hardware, memory, and power constraints can prevent some IoT devices from simultaneously supporting edge intelligence and ultra-reliable low-latency transmission. The survey therefore identifies hardware-based AI training for nano-IoT devices and wearables as an important research direction.

  • C. Hardware Constraints of IoT Devices: Wearable sensors and mobile devices in intelligent 6G healthcare may need to run AI functions and URLLC transmission simultaneously.Hardware, memory, and power limitations mean that some IoT sensors cannot satisfy the corresponding computational requirements.
  • C. Hardware Constraints of IoT Devices: Future research should develop hardware-based AI training solutions for nano-IoT devices and embedded wearables.The stated target includes intelligent-enhanced living assistance services.
  • D. Standard Specifications for 6G-IoT: 6G-IoT deployment requires stringent standards and stakeholder collaboration because diverse vertical use cases impose major architectural changes on current mobile networks.The survey identifies network operators, service providers, and customers as relevant stakeholders and notes the importance of communication protocols such as MODBUS.
  • VI. Conclusions: The survey integrates requirements, enabling technologies, application domains, taxonomy tables, and future challenges into a holistic review of 6G-IoT.Covered technologies include edge intelligence, RISs, space-air-ground-underwater communications, THz communications, mURLLC, and blockchain.
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