# 6G Internet of Things: A Comprehensive Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li,
Dusit Niyato, , Octavia Dobre, , and H. Vincent Poor Dinh C. Nguyen and Pubudu
N. Pathirana are with the School of Engineering, Deakin University, Waurn
Ponds, VIC 3216, Australia (e-mails: {cdnguyen,
pubudu.pathirana}@deakin.edu.au).Ming Ding is with Data61, CSIRO, Australia
(email: ming.ding@data61.csiro.au).Aruna Seneviratne is with the School of
Electrical Engineering and Telecommunications, University of New South Wales
(UNSW), NSW, Australia (email: a.seneviratne@unsw.edu.au).Jun Li is with the
School of Electrical and Optical Engineering, Nanjing University of Science
and Technology, Nanjing 210094, China (e-mail: jun.li @njust.edu.cn).Dusit
Niyato is with the School of Computer Science and Engineering, Nanyang
Technological University, Singapore (email: dniyato@ntu.edu.sg).Octavia Dobre
is with the Faculty of Engineering and Applied Science, Memorial University,
Canada (e-mail: odobre@mun.ca).H. Vincent Poor is with the Department of
Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544
USA (e-mail: poor@princeton.edu).This work was supported in part by the CSIRO
Data61, Australia, and in part by U.S. National Science Foundation under Grant
CCF-1908308. The work of Jun Li was supported by National Natural Science
Foundation of China under Grant 61872184.

###### Abstract

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.

###### Index Terms:

6G, Internet of Things, network intelligence, wireless communications.

##  I Introduction

Recent advances in wireless communications and smart device technologies have
promoted the proliferation of Internet of Things (IoT) with ubiquitous sensing
and computing capabilities to interconnect millions of physical objects to the
Internet. Nowadays, IoT constitutes an integral part of the future Internet
and has received much attention from both academia and industry due to its
great potential to deliver customer services in many aspects of modern life
[1]. IoT enables seamless communications and automatic management between
heterogeneous devices without human intervention which has the potential to
revolutionize industries and provide significant benefits to society through
fully intelligent and automated remote management systems.

Figure 1: The evolution of wireless networks toward future 6G-IoT.

As an enabler for supporting IoT networks and applications, mobile
technologies from the first to the fifth generation have been already proposed
and deployed commercially, as illustrated in Fig. 1. Notably, enabled by
inherent usage features such as enhanced mobile broadband (eMBB), massive
machine type communication (mMTC), and ultra-reliable and low-latency
communication (URLLC) services, the latest fifth-generation (5G) technology
has been proven to offer different service opportunities to IoT ecosystems
with high throughput, low latency and energy-efficient service provision [2],
[3]. However, with the unprecedented proliferation of smart devices and the
rapid expansion of IoT networks, 5G cannot completely meet the rising
technical criteria, e.g., autonomous, ultra-large-scale, highly dynamic and
fully intelligent services. The fast growth of automated and intelligent IoT
networks is likely to exceed the capability of the 5G wireless systems.
Moreover, the emergence of new IoT services and applications such as remote
robotic surgery and flying vehicles, also requires further advances in current
5G systems for improving the quality of IoT service delivery and business [4].

To pave the way for the development in IoT and beyond, research on sixth-
generation (6G) wireless networks [5] and their accompanying technological
trends has recently received much attention from both academia and industry.
6G is expected to provide an entirely new service quality and enhance user’s
experience in current IoT systems due to its superior features over the
previous network generations, such as ultra low-latency communications,
extremely high throughput, satellite-based customer services, massive and
autonomous networks [6, 7, 8]. These levels of capacity will be unprecedented
and will accelerate the applications and deployments of 6G-based IoT networks
across the realms of IoT data sensing, device connectivity, wireless
communication, and 6G network management. Enabled by the great potential of
6G-IoT, many efforts have been put into research in this promising area. For
instance, Finland has sponsored the first 6G project named 6Genesis [5], and
built the world’s first experimental 6G-IoT research environment. Nokia has
launched Hexa-X [9], a new European 6G flagship research initiative from
January 1, 2021, aiming to develop the vision for future 6G systems for
connecting human, physical and digital worlds in future IoT networks through
the collaboration of prominent European network vendors, communication service
providers, and research institutes. Furthermore, the U.S. Federal
Communications Commission (FCC) has opened the Terahertz (THz) spectrum band
which allows researchers and engineers to test 6G functions on mobile
communications systems and IoT devices [10]. Moreover, the government of South
Korea has planned to launch a pilot project for 6G mobile services from 2026
[11]. In this project, five major IoT areas have been selected for testing and
evaluation of 6G systems, including digital healthcare immersive content,
self-driving cars, smart cities and smart factories. Initial 6G networks could
be deployed in 2028, while mass commercialization of this technology is
expected to occur in 2030. These recent activities have motivated researchers
to look into the significant promise of 6G-IoT and exploit fundamental
technologies for enabling future 6G-IoT, aiming to satisfy the requirements
for the intelligent information society of 2030s. The vision of 6G-IoT
applications that will be discussed in this paper is illustrated in Fig. 2.

Figure 2: Vision of future 6G-based IoT applications.

###  I-A Comparison and Our Key Contributions

Driven by the recent advances of wireless networks and IoT, some research
efforts have been made to review related works. Specifically, the study in
[12] provided a brief discussion on the potential technologies for 6G to
enable mobile AI applications and the analysis of the AI-enabled solutions for
6G network design and optimization. A speculative study on 6G was given in
[13] where the authors indicated the visionary technologies potentially used
in future 6G networks and applications. The use cases in 6G wireless networks
were summarized in [14], while the recent advances in wireless communication
toward 6G were presented in [15]. The works in [16], [17] presented a survey
on the vision of future 6G wireless communication and its network
architecture, with a focus on the analysis of enabling technologies for 6G
networks. A more comprehensive survey on 6G wireless communications and
networks was provided in [18], where the authors paid attention to the
illustration of the tentative roadmap of definition, specification,
standardization, and regulation in 6G technologies. The potential 6G
requirements and the latest research activities related to 6G were also
discussed in [19], while the survey in [20] provided a holistic discussion of
various essential technologies in 6G. The comparison of the related works and
our paper is summarized in Table I.

Although 6G has been studied extensively in the literature, there is no
existing work to provide a comprehensive and dedicated survey on the
integration of 6G and IoT, to the best of our knowledge. Notably, a holistic
discussion on the emerging 6G-IoT applications such as Vehicular Internet of
Things, Autonomous Driving, and Satellite Internet of Things is still missing
in the open literature. These limitations motivate us to conduct a holistic
review on the convergence of 6G and IoT. Particularly, we identify and discuss
the most fundamental 6G technologies for enabling IoT networks, including edge
intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater
communications, THz communications, massive URLLC communications, and
blockchain. It is worth noting that while the existing works [14, 15, 16, 17,
18] only focus on the discussion of fundamental technologies for enabling
wireless communications and networks, we here highlight the 6G technologies
which directly influence the IoT applications and network in a holistic
manner, from intelligence (e.g., edge intelligence), communications (e.g.,
massive URLLC communications) to security (e.g., blockchain). The
representative use cases on the integration of 6G technologies and IoT are
also explored and analyzed. Subsequently, we present an extensive discussion
on the use of these 6G technologies in a wide range of newly emerging IoT
applications via five domains, i.e., Internet of Healthcare Things, Unmanned
Aerial Vehicles, Vehicular Internet of Things and Autonomous Driving,
Satellite Internet of Things, and Industrial Internet of Things. The taxonomy
tables are also provided to give more insights into the convergence of 6G and
IoT. Finally, we discuss a number of important research challenges and
highlight interesting future directions in 6G-IoT. These technical
contributions thus make our article fundamentally different from the existing
survey papers in the open literature. In a nutshell, this article brings a new
set of contributions as highlighted below:

  1. 1.

We present a holistic discussion of the convergence of 6G and IoT, starting
from an introduction to the recent advances in 6G and IoT and the discussion
of the technical requirements of their integration.

  2. 2.

We extensively discuss the fundamental 6G technologies which are envisioned to
enable IoT networks, including edge intelligence, reconfigurable intelligent
surfaces, space-air-ground-underwater communications, THz communications,
massive URLLC, and blockchain.

  3. 3.

We then provide an extensive survey and discussion of the roles of 6G in
prospective IoT applications in five key domains, namely Healthcare Internet
of Things, Unmanned Aerial Vehicles, Vehicular Internet of Things and
Autonomous Driving, Satellite Internet of Things, and Industrial Internet of
Things. The representative use cases in each 6G-IoT application domain are
highlighted and discussed. Moreover, taxonomy tables to summarize the key
technical aspects and contributions of each 6G-IoT use case are also provided.

  4. 4.

We identify several important research challenges and then discuss possible
directions for future research toward the full realization of 6G-IoT.

###  I-B Structure of The Survey

This survey is organized as shown in Fig. 3. Section II discusses the recent
advances and vision of 6G and IoT and highlights the requirements of their
integration. Next, the fundamental technologies enabling the 6G-IoT networks
and applications are analyzed in Section III. The opportunities brought by 6G
in a number of newly emerging IoT applications are explored and discussed in
Section IV in a number of important domains, i.e., Healthcare Internet of
Things (HIoT), Unmanned Aerial Vehicles (UAVs), Vehicular Internet of Things
(VIoT) and Autonomous Driving, Satellite Internet of Things (SIoT), and
Industrial Internet of Things (IIoT). Section V identifies several key
research challenges, including security and privacy in 6G-IoT, energy
efficiency in 6G-IoT, hardware constraints of IoT devices, and standard
specifications for 6G-IoT, along with the discussion of possible directions
for future research. Finally, Section VI concludes the article. A list of key
acronyms used throughout the paper is summarized in Table II.

Figure 3: Organization of this article. TABLE I: Existing surveys on 6G-related topics and our new contributions.  Related works |  Topic |  Key contributions
---|---|---
[12] |  AI-based 6G concept |  A brief discussion on the potential technologies for 6G to enable mobile AI applications and the analysis of the AI-enabled solutions for 6G network design and optimization.
[13] |  Speculative study on 6G |  A short discussion to speculate on the visionary technologies for enabling 6G networks and applications.
[14] |  6G technologies and case studies |  A short discussion on the enabling technologies for 6G and potential case studies.
[15] |  6G concept |  A survey on the concept of 6G and its recent advancements in wireless communication systems.
[16], [17] |  Enabling technologies for 6G wireless communications |  A survey on the vision of future 6G wireless communication and its network architecture, with focus on the analysis of enabling technologies for 6G networks.
[18] |  Technical aspects of 6G |  A discussion on the definition, specification, standardization, and regulation of 6G technologies.
[19] |  Requirements and research activities in 6G |  A survey on the 6G requirements and the latest research activities related to 6G.
[20] |  Essential technologies in 6G |  A holistic discussion of various essential technologies in 6G communications and networks.
Our paper |  6G and IoT |  A comprehensive survey on the convergence of 6G and IoT. Particularly,  • We identify and discuss the fundamental technologies that are envisaged to enable 6G-IoT networks, namely edge intelligence, RISs, space-air-ground-underwater communications, THz communications, massive URLLC, and blockchain. • We provide a holistic discussion on the emerging applications of 6G in IoT, i.e., Healthcare Internet of Things, Unmanned Aerial Vehicles, Vehicular Internet of Things and Autonomous Driving, Satellite Internet of Things, and Industrial Internet of Things. • Taxonomy tables are provided to give more insights into 6G-IoT use cases. Research challenges and directions are also highlighted.
TABLE II: List of key acronyms. Acronyms |  Definitions
---|---
6G |  Sixth-Generation
IoT |  Internet of Things
VIoT |  Vehicular Internet of Things
HIoT |  Healthcare Internet of Things
SIoT |  Satellite Internet of Things
IIoT |  Industrial Internet of Things
AI |  Artificial Intelligence
FL |  Federated Learning
ML |  Machine Learning
DL |  Deep Learning
DRL |  Deep Reinforcement Learning
DNN |  Deep Neural Network
CNN |  Convolutional Neural Network
MEC |  Mobile Edge Computing
UAV |  Unmanned Aerial Vehicle
URLLC |  Ultra-Reliable Low-Latency Communication
mURLLC |  Massive URLLC
THz |  Terahertz
RIS |  Reconfigurable Intelligent Surface
MIMO |  Multiple-Input and Multiple-Output
NOMA |  Non-orthogonal Multiple Access
LEO |  Low Earth Orbit
V2V |  Vehicle-to-vehicle
V2X |  Vehicle-to-Everything
AV |  Autonomous Driving
RSU |  Road Side Unit
QoS |  Quality-of-Service

##  II 6G and Internet of Things: Vision and Requirements

In this section, we discuss the vision of 6G and IoT. The requirements of
6G-IoT networks are also highlighted.

###  II-A 6G

Driven by the unprecedented proliferation of mobile devices and the
exponential growth of mobile traffic, wireless communication technologies have
rapidly developed in recent years as a key enabler for future customer
services and applications. Although the 5G network has been proven to enhance
QoS over previous generations, it will be challenging to fully meet the newly
emerging requirements of future IoT services [16]. More specifically, it can
be foreseen that 5G networks will be unable to accommodate the tremendous
volume of mobile traffic in 2030 and beyond. Due to the popularity of rich-
video applications, enhanced screen resolution, machine-to-machine (M2M)
communications, mobile edge services, etc., the global mobile traffic will
grow exponentially, up to 5016 exabyte (EB) per month in the year of 2030
compared with 62 EB per month in 2020 [18]. The traffic demand per mobile
broadband (MBB) also increases rapidly due to the growth of smartphones and
tablets and the proliferation of mobile data services. For example, there is a
need for new mobile communication technologies to support video services such
as Youtube, Netflix, and recently Tik-Tok since video data traffic accounts
for two thirds of all mobile traffic nowadays and continues to grow in the
coming years [18]. Moreover, the rapid development of data-centric intelligent
systems exposes new latency limitations of 5G wireless systems. For example,
the 5G air interface delay of less than 1 millisecond is inadequate to support
haptic Internet-based applications such as autonomous driving or real-time
healthcare assistance because the required delay is below 0.1 millisecond.

In this context, 6G is envisioned to provide new disruptive wireless
technologies and innovative networking infrastructures to realize a plethora
of new IoT applications by satisfying such stringent network demands in a
holistic fashion, compared to its 5G counterpart [19]. With the advent of
advanced technologies such as edge intelligence, THz, and large-scale
satellite constellation, 6G communication systems are able to evolve towards a
more powerful IoT ecosystem as well as build the fully connected and
intelligent digital world toward the foreseen economic, social, and
environmental ecosystems of the 2030 era. 6G is expected to outperform 5G in
multiple specifications as shown in Table III. While 5G networks remain some
critical limitations in terms of mobile traffic capability, density of device
connectivity, and network latency, 6G is able to bring a new level of network
qualities with the outstanding features as follows:

  * •

Achieving a supper high data rate from 1 Tb/s to address the massive-scale IoT
connectivity where the seamless mobility, spectrum availability, and mobile
traffic co-exist.

  * •

Increasing the mobile traffic capability up to 1 $Gb/s/m^{2}$ to satisfy super
high throughput requirements and IoT device density.

  * •

Achieving an extremely high device connectivity density of $10^{7}$
devices/$km^{2}$ which supports well for massively-dense IoT network
deployments

  * •

Achieving ultra-low network latencies (10-100 $\micro$s) to fulfill the
requirements of haptic applications, such as e-health and autonomous driving.

TABLE III: New features of 6G-IoT versus 5G-IoT.  |  5G-IoT |  6G-IoT
---|---|---
Network Features |  • Data Rate of 20 Gb/s • Mobile Traffic Capability: 10 $Mb/s/m^{2}$ • Connectivity Density: $10^{6}$ devices/$km^{2}$ • Network Latency: 1 ms • Coverage Percentage: about 70 % • Energy Efficiency: 1000x relative to 4G • Spectrum Efficiency: 3-5x relative to 4G |  • Data Rate from 1 Tb/s • Mobile Traffic Capability: 1 $Gb/s/m^{2}$ • Connectivity Density: $10^{7}$ devices/$km^{2}$ • Network Latency: 10-100 $\micro$s • Coverage Percentage: $>$ 99 % • Energy Efficiency: 10x relative to 5G • Spectrum Efficiency: $>$ 3x relative to 5G
Enabling Technologies |  • mm-Wave Communications • URLLC • NOMA • Artificial Intelligence • Cloud/Edge Computing • Software-Defined Networking/ Network Slicing |  • THz Communications • Massive URLLC • Space-air-ground-underwater Communications • Edge Intelligence • RIS • Blockchain

###  II-B Internet of Things (IoT)

As a key technology in integrating heterogeneous electronic devices with
wireless systems, IoT aims to connect different things to the Internet,
forming a connected environment where data sensing, computation and
communications are performed automatically without human involvement. IoT data
can be collected from ubiquitous mobile devices such as sensors, actuators,
smart phones, personal computers, and radio frequency identifications (RFIDs)
to serve end users [21]. It is estimated that IoT will achieve an impressive
development in the next few years. According to Cisco [22], up to 500 billion
IoT devices are expected to be connected to the Internet by 2030, from only 26
billion devices in 2020. Furthermore, in a new analysis of IHS Markit [23], a
world leader in critical information, analytics and solutions, the development
of connected global IoT devices will achieve an impressive rate of 12 percent
annually, from nearly 27 billion in 2017 to 125 billion in 2030. Recently,
GlobeNewswire also forecasts that the global 5G-IoT market will grow from USD
694.0 million in 2020 to USD 6,285.5 million by 2025 [24]. The Internet of
Nano-Things is also significant to build future advanced IoT ecosystems [25],
where the network of objects (nano-devices and things) can sense, transmit,
process, and store data based on nano units (e.g., a nanocontroller) for
supporting customer services such as healthcare monitoring. Seamless
interconnectivity among nano-networks via the available communication networks
and the Internet requires developing new network architectures and new
communication paradigms. In this context, 6G with its exceptional features and
strong capabilities will be a key enabler for supporting future IoT networks
and applications, by providing full-dimensional wireless coverage, and
integrating all functionalities, from sensing, communication, computing, to
intelligence and fully autonomous control. In fact, the next generation 6G
mobile networks are envisaged to provide massive coverage and better
scalability to facilitate IoT connectivity and service delivery, compared to
the 5G mobile network [26].

###  II-C Requirements of 6G-IoT

To fully realize the 2030 intelligent information society of full
intelligence, massive device connectivity and coverage, data-driven services,
and autonomous systems, 6G-IoT will need more stringent requirements over its
5G-IoT counterpart, as highlighted below.

####  II-C1 Massive IoT Connectivity

Driven by the explosion of smart devices and rapid development of wireless
communication technologies, the mobile connectivity has increased tremendously
[16]. It is predicted that the volume of global mobile traffic will grow
exponentially with over 5000 exabyte in the year of 2030 and increase 80 times
compared to the mobile traffic in 2020. Broadband access platforms along with
satellite networks enabled by using low earth orbit (LEO) satellites [27] will
be the key enabler for supporting large-scale communications of future smart
devices. Furthermore, the use of flying platforms such as UAVs are also needed
to support seamless connectivity over the future large-scale IoT networks
where fixed base stations cannot manage to ensure stable and reliable device
communications in moving IoT networks. For example, a distributed UAV
deployment prototype is presented in [28] in the context of 6G networks, by
using 33 UAVs to provide wireless connectivity for 400 terrestrial IoT users
over the coverage of 2000 m x 2000 m area. Given the UE distribution and
corresponding dis-continuous UAV location space, a distributed motion
algorithm is developed which allows each UAV to autonomously obtain the
optimal position in a continuous IoT space. Simulations indicate that the use
of swarm of UAVs is able to obtain the maximum load balance near to 1 by the
distributed deployment solution, with the deployment time reduced by up to 40%
compared to the centralized method.

####  II-C2 Massive Ultra-Reliable Low-Latency IoT Communications

Although ultra-reliable and low-latency communication (URLLC) has been
introduced and used in applied 5G-based IoT use cases [29], it needs to be
improved to massively support emerging applications in 6G-IoT networks such as
fully autonomous IoT and flying IoT systems. For example, in the future
autonomous transportation systems, where vehicles are self-controlled and
navigated in real-time, the massive URLLC is highly necessary for transporting
video feeds from cameras to vehicles and coordinating the timely vehicle
signalling on the roads in an automated and safe manner. In the future,
timeliness of information delivery will be a significant feature for the
intelligent interconnected society where the tactile internet will dominate to
offer haptic communications for mission-critical IoT services with touch and
actuation in real-time. As an case study, the work in [30] implements a
massive URLLC-based IoT simulation for 6G IoT networks by designing a short
range wireless isochronous real-time in-X subnetwork with communication cycles
shorter than 0.1 ms and outage probability below $10^{-6}$. A dense IoT
scenario is considered with up to two devices per $m^{2}$ using a multi-GHz
spectrum for providing high spatial service availability. By conducting a
semi-analytical system evaluation analysis, it is revealed that the cycle
times are a factor of x10 shorter than the latency targets of 5G radio
technologies (i.e., below 0.1 ms) which is potentially applied to future
ultra-low latency IoT applications such as autonomous driving, real-time
health monitoring, and industrial automation.

####  II-C3 Improved 6G IoT Communication Protocols

The introduction of new vertical IoT applications in future intelligent
networks imposes major architectural changes to current mobile networks in
order to simultaneously support a variety of stringent requirements (e.g.,
autonomous driving and e-healthcare). In such a context, network communication
standards and protocols play an important role in deploying 6G-IoT ecosystems
at large-scale due to the integration with other important computing services
such as edge/cloud computing and wireless technologies. For example, the
Industry Specification Group of the European Telecommunications Standards
Institute has released the initiative called ETSI Multi-access Edge Computing
[31], which aims to leverage seamlessly edge computing and communication
frameworks for IoT-based applications originating from vendors, developers and
third-party service providers. This initiative could be a significant step to
the deployment of future IoT applications at the network edge in the 6G
network where IoT computing and storage are expected to be shifted from the
network centre to the network edge. Recently, the IEEE 802.11 working group
has initiated discussions on releasing the next generation of Wi-Fi standard,
referred to as IEEE 802.11be Extremely High Throughput [32], which can meet
the peak throughput requirements set by upcoming IoT applications in the 6G
era. These communication standards are expected to support well the service
providers in deploying intelligent IoT services at the network edge.

####  II-C4 Extended IoT Network Coverage

In the future IoT-based society, it is desirable to achieve a full coverage
beyond the terrestrial networks, from using large-dimensional space-air-
ground-underwater networks [5]. Edge intelligence and UAVs are the keys to
achieving full wireless coverage where the former is able to provide
autonomous and intelligent solutions at the network edge, while the latter can
be used to build flying base stations to extend the coverage of current mobile
networks from only 2D in existing terrestrial networks to 3D in an integrated
terrestrial-satellite-aerial system. For example, high-altitude UAVs can be
exploited as agile aerial platforms to enable on-demand maritime coverage
[33], as a promising solution to establish shore-based terrestrial based
stations to facilitate the deployment of communications infrastructure for
vessels on the ocean. To be clear, a practical automatic identification system
is employed to obtain the vessel distribution with the offshore distance of
the range of [20, 30] km. To provide an on-demand coverage of distributed
vessels, UAVs are coordinated to move along with vessels for ensuring long-
term broadband services. Here, an oil-powered fixed-wing UAV is adopted which
can perform a 740 km round trip over the coastal area of about 370 km. Once
the data transmission task at a vessel is accomplished, the UAV flies back to
the charging station and continues serving the next vessel user in the queue.
This communication pattern is unique from traditional UAV networks where IoT
users are often fixed or have random distributions.

####  II-C5 Next-generation Smart IoT Devices

Future 6G-IoT networks are predicted to rely on smart devices where edge
intelligence and computing can be fully realized at the devices, e.g.,
smartphones, vehicles, machines and robots. Such a device-centric network
poses new challenges and requirements on the design and operation of its
wireless communications, since smart devices will not only generate or exploit
data, but will also actively join the network management and operation
processes. Device-centric wireless solutions can be device-to-device
communications or multi-hop cellular networks that are parts of the 3GPP
roadmap [34]. In the 6G era, each IoT smart device can act as an end-user
terminal which can provide connectivity and services (e.g., intelligent
control, caching, and network signalling) to other devices at the network edge
without the need for a centralized controller [35]. This can be extended to
demand-driven opportunistic networking which is tailored to different user,
service or network demands such as energy cost minimization or spectrum
efficiency maximization for end-user devices [36]. Moreover, recent years have
witnessed a proliferation in wearable devices whose functionalities are
gradually replacing the roles of smartphones that are central in 4G/5G network
generations. These new wearable devices are diverse, ranging from smart
wearables to smart body implants which can play a pivotal role in the
revolution of wearable IoT networks for driving the emerging human-centric 6G
services.

##  III Fundamental Technologies for 6G-IoT

In this section, the fundamental technologies that are envisioned to enable
future 6G-IoT networks and applications are discussed. It is important to
highlight that existing survey papers [14, 15, 16, 17, 18] mostly discuss
enabling technologies for 6G wireless communication; we here focus on
technologies which will directly support 6G-IoT, including edge intelligence,
RISs, space-air-ground-underwater communications, THz communications, mURLLC,
and blockchain, as illustrated in Fig. 4.

Figure 4: Fundamental 6G technologies for IoT.

###  III-A Edge Intelligence

In intelligent 6G systems, AI functions are extended to the network edge
thanks to the computational capabilities of edge nodes [37]. This leads to a
new paradigm called edge intelligence [38], [39], which is envisioned by the
convergence of AI, communications, and edge computation. Recently, some
research efforts have been made to explore the potential of edge intelligence
in 6G-IoT use cases. For instance, the work in [40] suggested a self-learning
edge intelligence solution, aiming to identify and classify emerging unknown
services from raw crowdsourcing data distributed across a wide geographical
area with a case study in 6G-based vehicular networks. A latency-sensitive
connected vehicular system is proposed that consists of six campus shuttles
connected to two edge servers along with a mobile app to monitor the latency
of wireless vehicular data transmission. A highly-efficient edge intelligence
approach is employed, by equipping each vehicle with an AI-based generative
adversarial network. This is able to generate synthetic data that can be
directly produced by the edge server, which helps reduce the data volume
required to upload from the user and mitigate the total traffic transported
throughout the network. However, the proposed self-learning architecture has
not yet considered user mobility that can closely affect the latency in AI
training among distributed vehicles. Moreover, the role of edge intelligence
in 6G-IoT is also discussed in [41] where some dominant use cases are taken
into account, such as autonomous driving. Edge intelligence is foreseen as a
key enabler for dynamic spectrum access to provide fast and reliable AI-based
data processing at the edge devices, e.g., platoons, vehicles on the street,
and road side units. The benefits of edge intelligence are also considered for
collaborative robots (or cobots) in smart manufacturing, with possible applied
domains like automatic monitoring of machine health properties, autonomous or
semi-autonomous navigation, and fine-grained control of cobots. Moreover,
ubiquitous edge devices can run AI functions to offer large-scale edge
intelligence services enabled by big data analytics [42]. In this context, the
information asset characterized by high volume, velocity and variety of big
data is exploited to realize AI-based data analytics for its transformation
into useful information to serve IoT users. Particularly, the 6G-based big
data analysis technology can significantly enhance the large-scale data
transmission and data computation rates based on advanced communication
technologies such as massive URLLC and space-air-ground-underwater
communications. Along with edge computing, fog computing also allows for
relieving the load on cloud servers by offering computation and storage at fog
nodes close to the IoT devices for improving the QoS [43]. Fog nodes would be
useful to incorporate idle and spare resources of all available devices to
further enhance network efficiency, especially in distributed IoT networks
where cloud computing cannot handle all users’ computation demands. Fog
computing can be combined with AI techniques to realize fog intelligence for
providing smart and low-latency IoT services in the future 6G era.

Recently, federated learning (FL) [44] as a distributed collaborative AI
approach is emerging to transform edge/fog intelligence architectures.
Conceptually, FL is a distributed AI approach which enables training of high-
quality AI models by averaging local updates aggregated from multiple learning
edge clients without the need for direct access to the local data [45]. For
example, in the context of intelligent IoT networks, distributed IoT devices
can collaboratively work with a data aggregator (e.g., an edge server) to
perform neural network training where devices only exchange the parameters
while raw data sharing is not needed, as illustrated in Fig. 5. With its
working concept, FL can offer several unique features to IoT networks. One of
the most important features is the ability to enhance data privacy based on
the distributed model training without sharing raw data to external servers.
Following the increasingly stringent data privacy protection legislation such
as the General Data Protection Regulation (GDPR), the capability of protecting
user information in FL is essential for building future safe 6G-IoT systems.
Another feature is that FL enables low-latency network communications by
avoiding the offloading of huge data volumes to the remote server in the
training process. This capability also helps save much network spectrum
resources required for iterative data training. Additionally, the cooperation
of massive IoT devices for contributing large-scale datasets and computation
resources in the FL system would accelerate the convergence rate of the
overall training process and thus improve learning performances, e.g.,
accuracy rates, for better intelligent 6G-IoT services.  A case study of FL-
enabled edge intelligence in the 6G-IoT context is provided in [46]. Motivated
by the privacy leakage issues in AI training, an approach called Air-Ground
Integrated Federated Learning (AGIFL) is employed to evaluate the effect of
different UAV’s hovering location deployment schemes with privacy awareness.
Here, multiple terrestrial nodes (e.g., mobile users) are regarded as clients
to join the collaborative training with the server deployed at the UAV. In
this setting, each user performs local training using its own dataset and
sends the updated parameter to the UAV which aggregates all received updates
to build a global model before giving it back to all participating users for
the next round of training during the location deployment. By implementing
simulations on image classification tasks using convolutional neural networks
(CNNs) based on classical handwritten digit datasets, the proposed AGIFL-based
method shows a promising classification accuracy performance (the accuracy
rate achieves up to 95%), compared to non-federated schemes in the 6G context.
In the near future, the security for local gradient computation at UAVs should
be taken into account in federated training, by integrating attack detection
methods such as access control and data authentication.

Figure 5: Edge intelligence with FL for wireless IoT networks.

In edge intelligence, AI on hardware will be one of the promising technologies
that can accelerate the development of IoT applications in the 6G era. With
the rapid growth of smart mobile devices and advancement in embedded hardware,
there is an increasing interest in deploying AI on the edge devices, e.g.,
mobile computers, smart phones, edge servers, for both on-chip ML and DL
functions. Embedded AI functions can provide on-device learning inference with
low latency compared to cloud-based AI training. The deployment of AI on
hardware opens up new interesting on-device IoT applications, such as mobile
smart object detection, and mobile human recognition. For example, a binary
neural networks (BNNs) engine is developed in [47] based on graphics
processing units (GPUs) for Android devices that optimizes both software and
hardware for running ML functions on resource-constrained devices, e.g.,
Android phones. The computation capability of BNN is exploited on mobile
devices, decoupled with parallel optimizations with the OpenCL library toolkit
for enabling real-time and reliable NN deployments on Android devices. The
experimental results indicate that the mobile BNN architecture can achieve a
learning accuracy of 95.3% and improve the capability of storage and CPU usage
in BNN running by up to 20%. Another case study is presented in [48], where a
MobileNet app is designed for on-device CNN training on mobile devices for
mobile localization applications in IoT networks. The visual images captured
by phone cameras can be learned by the CNN to determine the centroid of the
object (e.g., the human hand) for supporting human motion detection tasks,
such as people counting in social events.

The development of edge intelligence mostly relies on edge devices that play
important roles to perform learning tasks, i.e., classification or regression.
However, adversaries can deploy attacks to steal data and modify parameters
during the data transmission and training, which makes edge intelligence
highly vulnerable. Another security issue comes from untrusted edge devices
that can exploit the private information extracted from gradient exchange in
the collaborative training, which thus also introduces high risks of data
breaches. Therefore, future security solutions should be proposed for a safe
and reliable edge intelligence ecosystem in future 6G-IoT networks. For
example, decentralizing the data learning can be a feasible choice for edge
intelligence systems, where blockchain ledgers can be adopted to verify the
computation process of edge intelligence models. It can be done by using
allowing edge blockhain peers to authorize the accuracy of the common AI model
without revealing the labels of datasets. The data flow inside the AI model
can also be hidden using blockchain-based cryptography techniques to prevent
data modification threats.

###  III-B Reconfigurable Intelligent Surfaces

Recently, reconfigurable intelligent surfaces (RISs) have gained significant
research attention for 6G technology applications [49]. The RIS consists of
arrays of passive scattering elements with artificial planar structures, where
each element is enabled by electronic circuits to reflect the impinging
electromagnetic wave in a software-defined manner [50]. The reconfigurability
makes RISs as a promising solution to support the wireless system design and
optimization by facilitating signal propagation, channel modeling and
acquisition which enables smart radio environments beneficial to 6G-based
applications. Indeed, RIS enables a number of promising applications in the
context of 6G-IoT networks. For instance, RIS is able to simultaneously
enhance the signal gleaned from the serving base stations in multi-cell IoT
networks to reduce inter-cell interference between massive IoT devices [51].
Another application of RIS is the capability to enhance the data offloading
rates for IoT systems. Specifically, the volume of data offloaded to edge
servers is largely dependent on the channel gain of offloading links. RISs can
be deployed for establishing a virtual array gain and reflection-based
beamforming gain to computation offloading links. The use of RISs thus enables
more data to be offloaded to edge servers, where the data can be processed
more time-efficiently than using traditional offloading approaches.

Recent research works have started integrating RISs into 6G-based IoT
applications. The study in [52] provides a range of case studies where RISs
can be applied to IoT such as smart buildings. Indeed, RISs can help establish
the interface between the indoor and outdoor entities, aiming to facilitate
the access of private households in smart buildings. From the wireless
communication benefits, RISs are promising to provide cooperative entity
layers for avoiding interference and improving spectral efficiencies in the
device communications between indoors and outdoors. Another case study is
presented in [53], where RISs are integrated in VIoT-based vehicle-to-vehicle
(V2V) networks consisting of a RIS-based access point for transmission and a
RIS-based relay deployed on a building for coordinating the vehicular
communications.  RISs are also considered in [54] to support radio-frequency
(RF) sensing for human posture recognition in IoT applications such as
surveillance and remote health monitoring. By periodically programming RIS
configurations in a human posture recognition system, the optimal propagation
links can be obtained so that the system can create multiple independent paths
that accumulate the useful information of human postures to estimate better
the human posture recognitions in comparison with the random configuration and
the non-configurable environment cases.

###  III-C Space-air-ground-underwater Communications

6G is envisioned to be a unified communication platform, not only on the land
but also on the sky (such as flight) and underwater (such as voyage), to
achieve an extremely broad coverage and ubiquitous connectivity for fully
supporting future IoT applications. Accordingly, a cell-free and four-tier
large-dimensional communication network for 6G-IoT can be derived, consisting
of four tiers: space, air, terrestrial, and underwater [5].

  * •

Space Communication Tier: This layer will provide wireless coverage using LEO,
medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellites for
areas that are not covered by terrestrial networks. Space communication
technologies can be deployed to support high-capacity space networks for
satellite-ground communications at various atmospheric altitudes [55].

  * •

Air Communication Tier: This layer is well supported by UAVs and balloons
working as flying base stations to provide coverage and connectivity for
managing disaster-stricken areas and supporting public safety networks and
emergency situations when URLLC is required [56]. UAVs also work as aerial
users to incorporate with terrestrial base stations to set up direct air-
ground links to perform cooperative sensing and data transmission in 6G-IoT
environments.

  * •

Terrestrial Communication Tier: This layer aims to wireless coverage and
connectivity for human activities on the ground where physical base stations,
mobile devices and computing servers are interconnected together. In the
context of 6G-based IoT, the THz band will be exploited to enhance spectral
efficiency and accelerate communication speeds, especially in ultra-dense
heterogeneous networks with millions of users [57].

  * •

Underwater Communication Tier: This layer accommodates connectivity services
for underwater IoT devices such as submarines in broad-sea and deep-sea
activities. Bi-directional communications can be necessary for interconnection
between underwater IoT devices and control hubs [58].

Several case studies have been considered for investigating the feasibility of
space-air-ground-underwater communications in 6G-IoT applications.  For
example, a multiuser satellite IoT system is considered in [59] based on LEO
satellite communications. In this regard, a Mobile Edge Computing (MEC) server
is integrated with the full-duplex access points to create satellite-links for
improving communication latency efficiency in mission-critical IoT
applications. The simultaneous wireless information and power transfer (SWIPT)
technology is integrated with a hybrid energy storage method, i.e., the power
is from power grid or renewable sources, to achieve longer battery life and
higher energy efficiency for IoT communications, while the satellite provides
the wide area network connection for terrestrial terminals, including stations
and mobile users. Then, a satellite-terrestrial communication model is
derived, aiming to maximize the achievable rate of IoT terminals. Simulations
with a satellite IoT system with 10 IoT users illustrate an improvement in
achievable rate of terminals by 16% due to the joint optimization of CPU
frequency, computation tasks, and terminal transmitting power. Nevertheless,
the communication latency caused by packet loss checking over satellite
channels has not been considered in the proposed multiuser satellite IoT
system optimization. Another case study is explored in [60], where UAVs are
employed to realize UAV-to-Everything communications for supporting different
data transmission solutions in IoT data sensing, emergency search and
monitoring, and video streaming in wireless 6G scenarios.

###  III-D Terahertz (THz) Communications

THz communications are envisioned as a driving technology for 6G-IoT which
requires 100+ Gbps data rates and 1- millisecond latency. Enabled from the mm-
wave spectrum, the THz band (0.1-10 THz) is promising to fulfill the future
requirements of 6G-IoT applications, including pico-second level symbol
duration, integration of thousands of submillimeter-long antennas, and weak
interference without full legacy regulation [61]. One of the key benefits of
THz spectrum is to deal with the spectrum scarcity problems in wireless
communications and significantly enhance wireless system capacities in 6G-IoT.
Moreover, THz communications also provide ultra-high bandwidth and high
throughput, which support ultra-broadband applications such as virtual reality
and wireless personal area networks. Inspired by the unique features of THz
communications, several IoT studies have been implemented in the context of
6G. Recently, THz communication technologies have been used for user
localization by exploiting the ultra-wide bandwidths available at THz
frequencies [62]. This allows the receivers to address spaced multipath
components and effectively measure the signal sent by the transmitters for
estimating correctly the user location. THz bands are also exploited for UAVs
communications [62], aiming to analyze the coverage probability of UAV
networks with respect to the THz base station density and the strength of THz
signals. The work in [63] focuses on modelling THz communication channels in
VIoT-based vehicular networks and configurations of hybrid beamforming
subarray by taking transmit power consumption into account in different
scenarios, such as the fully-connected, sub-connected and overlapped subarray
structures. By using a cellular infrastructure-to-everything application
consisting of both cellular and vehicular communications with 1000 channels
and multiple pedestrian users and high-mobility vehicles, the analysis of
antenna array structures can be performed. Simulation results show a balanced
performance trade-off in terms of spectral efficiency, energy efficiency, and
hardware costs between the popular fully-connected structures in VIoT
communications with THz massive MIMO. This trend is expected to continue in
various areas of THz band-based IoT communications such as channel modeling
and spectrum allocation. In the future, time-varying THz communication issues
caused by the high mobility of vehicle should be considered, which could be
addressed by predictable AI approaches such as dynamic DRL algorithms.

###  III-E Massive Ultra-Reliable and Low-latency Communications

In the 6G era, it is expected to achieve URLLC for supporting future IoT
services through low-latency and reliable connectivity [64], [65]. For
example, mURLLC is expected to support the timely and highly reliable delivery
of massive health data for facilitating remote healthcare, aiming to provide
better medical services to patients in the remote areas and also reduce
regional imbalance in the health workforce. mURLLC can be deployed in smart
factories to automate the mission-critical processes such as automatic
manufacturing and remote robotic control. Compared with the traditional wired
connections, the use of mURLLC technology allows for optimizing the
operational cost with extremely low latency and ultra-high reliability, e.g.,
the reliability of 99.9999% with block error rate (BLER) of below $10^{-5}$.
Thanks to its outstanding features, mURLLC also well supports transportation
systems, by offering timely data sharing of information among vehicles,
infrastructures, and pedestrians with high reliabilities, which thus enhances
road safety and improves traffic efficiency in vehicular networks. The
integration of mURLLC into smart grid is also an active application, aiming
for replacing cable/fibre based solutions to carry out the real-time
protection and control over the distributed grid lines and stations. As a
result, a wide range of mission-critical services can be realized with mURLLC
such as fast fault diagnosis and accurate positioning, reliable fault
isolation and system restoration as well as remote decoupling protections.

Moreover, the recent advances in AI make it an ideal tool to analyze the
latency and reliability for enabling mURLLC in 6G-IoT, by offering excellent
solutions, e.g., accurate traffic and mobility prediction with deep learning
(DL) and fast network control with deep reinforcement learning (DRL) [66],
[67]. The use of AI becomes more significant when the network information is
unavailable and the network environment is highly dynamic. For example, the
work in [68] exploits DL to a mURLLC-based virtual robotic arm system where
deep neural networks (DNNs) are applied to predict accurately arm positions
and control robotic arms with a duration of 1 ms. Deep transfer learning is
also adopted in the DL architecture to fine-tune the pre-trained DNNs in non-
stationary networks for improving the learning efficiency. Moreover, DRL has
been used in [69] for optimizing the distributed cooperative sub-channel
assignment and transmission power control which aims to provide the strict
reliability and latency requirements of URLLC services. By using DRL, each
mobile IoT device is able to intelligently make decision on its spectrum
access based on its own instantaneous observations, aiming to optimize its
sub-channel assignment and transmission power control. Then, a proper QoS-
aware reward function is derived to manage the energy efficiency and QoS
requirements of all IoT users. Experiments are implemented in a URLLC-based
IoT environment with 2000 devices, the minimum data rate requirement is set to
3.5 bps/Hz, and the reliability requirement varies between 99.9% and
99.99999%. Implementation results demonstrate a much better energy efficiency
by using DRL, compared to other conventional random approaches. A limitation
of this approach is the lack of training latency analysis in the DRL running
under the latency requirements of URLLC.

To achieve the latency and reliability for mURLLC, IoT applications should
meet several critical requirements. System overheads in term of channel
access, user scheduling, and allocation of resources should be minimized. The
recent advances in AI open new opportunities for latency optimization in IoT
networks, such as optimal IoT user selection and scheduling via DRL
approaches, and low-latency resource allocation in intelligent transportation
using distributed training techniques via FL. Furthermore, the packet error
probability should be minimized to achieve lower-latency data transmission
since traditional methods such as hybrid automatic repeat request (HARQ)
processing are not appropriate to achieve a low block error rate (BLER) [70].
Additionally, energy-efficient solutions for IoT devices should be designed,
aiming to solve the issues of continuously checking awaiting packets on the
network that incurs high latency as well. How to implement energy-saving while
offering a solution for high frequency of data checks is needed for URLLC-
based IoT devices.

###  III-F Blockchain

Figure 6: Blockchain for secure 6G UAV networks.

In 6G-based IoT networks, how to achieve a high degree of security and privacy
is a practical challenge since the fact that 6G systems tend to be distributed
and thus suffer from the higher risks of attacks and threats. Moreover, how to
ensure data privacy in the open sharing systems in multi-layer 6G systems such
as vehicular data sharing in autonomous driving is a critical issue.
Blockchain [71], as an emerging disruptive technology, is able to offer
innovative solutions to effectively deal with such privacy and security
challenges in 6G-IoT networks. Conceptually, the blockchain is a
decentralized, immutable and transparent database where no any authority is
needed to manage the data. This is enabled by a peer-to-peer network topology
which allows each entity, e.g., an IoT device, to hold an equal right to
control and authorize the data stored in the blockchain. Generally,
blockchains can be classified as either a public (permission-less) or a
private (permissioned) blockchain. A public blockchain allows anyone to
perform transactions and join in the consensus process. The best-known public
blockchain applications include Bitcoin and Ethereum. Private blockchains on
the other hand are an invitation-only network managed by a central entity, and
a participant who wants to join the blockchain network needs to be
permissioned via a validation mechanism. Enabled by its working concept,
blockchain boasts a number of desirable characteristics, including
decentralization, traceability, trustworthiness, and immutability. These
features make blockchain a promising candidate to be integrated into 6G-IoT
ecosystems for security and privacy provision. For secure access control in
6G-based IoT communication environments, a blockchain-based approach is
proposed in [72]. The authors exploit the decentralization of immutability
features of blockchain to develop a security mechanism for reliable resource
access control and user privacy preservation. Specifically, for a secure
access control, the states of the virtualized resources are modelled using a
Q-learning approach that can learn the resource usage patterns to detect
abnormal data access behaviours. In terms of privacy preserving, a joint
method of low-latency and memory saving is used to augment the response
success ratio, and to obtain the false positives of the connected users. The
benefits of the proposed solution are verified by the high true positives
(nearly 94%), accurate access denial and success ratio (nearly 92%). However,
the mining latency caused by block verification in blockchain has not been
considered yet, and the evaluation of data leakage probability is still
missing.

Moreover, a roadmap for the applications of blockchain in 6G-IoT automation is
drawn in [73] where blockchain is particularly useful for applied domains such
as UAVs, smart grid, and food industry. In fact, blockchain is able to
establish secure autonomous systems where UAVs can act as blockchain clients
to communicate with ground base stations, aiming to exchange and share data to
fulfil their missions such as emergency search or environmental monitoring via
a peer-to-peer ledger, as illustrated in Fig. 6. By using blockchain, UAVs,
terrestrial users, and network operators can trust the data stored on the
ledger with a shared control and tracing right provided over the distributed
environment. Another possible IoT application is smart healthcare; the
verification of health data 6G-based healthcare systems can be implemented by
blockchain and its inherent smart contract technology [74], where no third-
party is required while a high degree of trust is ensured. Moreover, the
application of blockchain in future 6G-IoT networks also might result in costs
in terms of latency and energy usage. The mining process, e.g., block
verification and information exchange among miners, leads to high network
delays and consumes excessive energy. For example, in the Ethereum blockchain
platform, miners need to run Proof-of-Work (PoW) which is computationally
extensive and time-consuming. Further, the repeated information exchange among
multiple miners in the block verification also needs large bandwidth
resources. Hence, it is essential to take operational costs into account when
applying blockchain in future IoT networks.

To this end, we summarize the fundamental 6G technologies along with their key
features and important use cases in IoT networks in Table IV.

TABLE IV: Taxonomy of fundamental 6G technologies for IoT.

Fundamental technology |  Key features |  Potential applications in 6G-IoT
---|---|---
Edge Intelligence |  • Enabled by the seamless integration of AI, communications, and edge computing where AI functions are deployed at decentralized edge nodes to make intelligence close to the data source where they are generated, e.g., mobile devices. • Privacy-enhanced edge intelligence can be realized via distributed collaborative data training with FL. This learning paradigm allows distributed IoT devices to collaborate with an aggregator to perform AI training while raw data sharing is not needed. |  • A self-learning edge intelligence solution is proposed in [40] with a case study in 6G-based vehicular networks where an AI-based GAN is deployed on each vehicle to perform personalized classification of vehicular latency data without the central processing at a centralized server. • The work in [41] illustrates the roles of edge intelligence in 6G-IoT via some dominant use cases such as autonomous driving and collaborative robots in smart manufacturing. Compared with [40], this scheme still has high data communication latency due to the lack of synthetic data production with GANs at edge nodes. • Edge intelligence in the 6G-IoT context is enhanced with FL [46] for UAV networks where multiple mobile users collaboratively join the FL process with the server deployed at the UAV.
RISs |  • RISs are man-made surfaces of electromagnetic material that are electronically controlled with integrated electronics to enable reconfigurable propagation environments [50]. • The reconfigurability makes RISs as a promising solution to support the wireless system design and optimization to enable smart radio environments and benefit 6G-based IoT applications. |  • RISs can help establish the interface between the indoor and outdoor entities [52], aiming to facilitate the access of private households in smart buildings. RISs are also promising to provide cooperative entity layers for avoiding interference and improving spectral efficiencies in the device communications between indoors and outdoors. • RISs are also considered in [54] to support RF sensing for human posture recognition in IoT applications such as surveillance and remote health monitoring.
Space-air-ground -underwater Communications |  • A cell-free and four-tier large-dimensional communication network for 6G-IoT can be derived, consisting of four tiers: space, air, terrestrial, and underwater [5]. • UAVs can be used as flying stations to provide coverage and connectivity for disaster-stricken areas and supporting public safety networks and emergency situations when URLLC is required. |  • A multiuser satellite IoT system is considered in [59] based on LEO satellite communications where an MEC server is integrated with the full-duplex access points to create satellite-links for improving communication latency efficiency in mission-critical IoT applications. • Different from [59], UAVs are exploited in [60] for supporting different data transmission solutions in IoT data sensing and video streaming, where a new DRL algorithm is integrated to optimize the trajectory and power control of UAVs.
THz Communications |  • THz communications are envisioned as a driving technology for 6G-IoT which requires 100+ Gbps data rates and 1- millisecond latency [75]. • The THz spectrum is able to deal with the spectrum scarcity problems in wireless communications and significantly enhance wireless system capacities in 6G-IoT. |  • Recently, THz communication technologies have been used for user localization by exploiting the ultra-wide bandwidths available at THz frequencies [62]. • THz bands are also exploited for UAVs communications [63], aiming to analyze the coverage probability of UAV networks. However, unlike [62], this scheme leverages THz base station density and the strength of THz signals for UAVs’ location and trajectory estimation.
mURLLC |  • Massive URLLC in 6G-IoT can be realized by mMTC and 5G URLLC integration for enabling mURLLC [65] to provide extremely low latency, extremely reliable connectivity, high availability and scalability • The recent advances in AI make it an ideal tool to model the latency and reliability for enabling mURLLC in 6G-IoT, by offering excellent solutions, e.g., accurate traffic and mobility prediction with DL, and fast network control with DRL [66]. |  • The work in [68] exploits DL to a mURLLC-based virtual robotic arm system where DNNs are applied to predict accurately arm positions. Deep transfer learning is also adopted in the DL architecture to fine-tune the pre-trained DNNs in non-stationary networks for improving the learning efficiency. • The work in [69] offers a more advanced learning approach, by integrating DNNs with reinforcement learning for optimizing the distributed cooperative sub-channel assignment and transmission power control, which aim to provide the strict reliability and latency requirements of URLLC services.
Blockchain |  • Although 6G has the capability to provide exceptional service qualities to IoT applications, there exist critical issues in terms of risks of data interoperability, network privacy and security vulnerabilities. • Blockchain is able to offer many innovative solutions to effectively deal with such privacy and security challenges in 6G-IoT networks [71]. Technically, the blockchain is a decentralized, immutable and transparent database where no any authority is needed to manage the data. |  • A blockchain-based approach is proposed in [72] for secure access control and privacy preservation in 6G-based IoT communication environments. • A roadmap for the applications of blockchain in 6G-IoT automation is drawn in [73] where blockchain is particularly useful for applied domains such as UAVs, smart grid, and food industry. For example, blockchain is able to establish secure autonomous systems where UAVs act as blockchain clients to communicate with ground base stations in a secure manner. • The verification of smart healthcare in 6G-based healthcare systems can be implemented by blockchain [74] with the integration of smart contract for self-executing data access evaluation, which has not been considered in [72] and [73].

##  IV 6G for IoT Applications

Enabled by the fundamental technologies as described in the previous section,
6G is envisioned to realize new applications for IoT. In this section, we
explore and discuss extensively the emerging applications of 6G in a wide
range of important IoT domains, including HIoT, VIoT and Autonomous Driving,
UAVs, SIoT, and IIoT.

Figure 7: Edge intelligence for COVID-19 data analytics in federated
healthcare.

###  IV-A 6G for Healthcare Internet of Things (HIoT)

The integration of 6G will revolutionize HIoT [76] by using its enabling
technologies. The work in [77] discussed the potential use of 6G technologies
such as mURLLC and THz communications for supporting extremely low-latency
healthcare data transmission and accelerating medical network connections
between wearables and remote doctors. In fact, healthcare domains such as
remote health monitoring requires low-latency communications (below 1 ms) with
the reliability requirement of above 99.999% to achieve nearly real-time
health provision with a fast and reliable remote diagnosis. Interestingly, 6G
robotics can be applied to implement remote surgery in a fashion that remote
doctors can manage the surgery via the robotic systems at a latency of
milliseconds and high reliability. Recently, a telesurgery system is also
studied in [78] in the context of 6G by using UAVs and blockchain. Given the
security risks in existing mobile surgery networks, blockchain is integrated
into the robotic system where each robot acts as a data node so that surgical
information is stored securely in the database ledger without the need of
centralized authority. Particularly, smart contracts are also adopted that can
provide automatic authentication for health data requests and control over the
health data sharing during the surgery [79]. To solve the slow healthcare
response rates, UAVs are employed as relays to transport light-weight
healthcare items such as medicines and surgical tools among hospitals in
emergent situations, which helps avoid road-traffic congestions and thus
mitigates data exchange latency. Meanwhile, to achieve future requirements in
terms of ultra-high data rates of medical data communications, the mURLLC
technology is an ideal solution by using the THz bands in 6G-based healthcare
networks [80]. Accordingly, nano-devices, implants, and on-body sensors can
communicate and transmit data in real-time with extremely high reliability and
availability to edge devices or cloud centres for short- and long-term medical
analysis. In particular, mURLLC also plays a key role in hospital-based
telestration where doctors can monitor and manage the surgery procedure
remotely using real-time video streaming from medical robotics and assistant
devices interconnected by 6G core networks [81]. Moreover, 6G-based URLLC has
been exploited to facilitate connected ambulance in future healthcare, by
allowing real-time video streaming with high color resolution for reliable
diagnosis to clinicians and paramedical staff from the hospital at moderately
high speeds (up to 100 km/h) [82]. For example, electroencephalogram data from
clinical examination can be conducted on-board and through URLLC-based real-
time teleconsultation, hospital doctors are able to provide urgent indications
to paramedical staff in the ambulance. In this scenario, a very small survival
time must be ensured (below 2 ms) although the ambulance can run at high
speeds.

To realize intelligent 6G-based healthcare, AI can be exploited for data
learning and analytics. The study in [83] uses various machine learning (ML)
techniques such as Bayesian classifier, logistic regression, and decision tree
to analyze historical health records of stroke out-patients collected from
wearable sensors in heathcare-based 6G heterogeneous networks. To accelerate
the stroke care for patients, an uplink radio resource allocation optimization
solution is integrated where the assigned resources are proportional to the
stroke likelihood of patients. Another ML-based solution for intelligent
6G-healthcare networks is suggested in [84], where edge-cloud computing is
adopted to provide low-latency health data analytics for healthcare services
such as diagnosis, disease prediction, and intelligent decision making tasks
for physical medicine and rehabilitation. In this context, ML is also useful
to optimize mobility management processes in 6G-based health networks, by
taking data rates, traffic flows, data processing delays, and bandwidth
resource allocation into account. Implementation results show a good trade-off
between time and energy efficiency by using ML techniques while effectively
managing and monitoring the mobility of the IoT driven devices in 6G-empowered
industrial applications including healthcare services. Recently, COVID-19 has
spread rapidly across the globe and become a major health concern of many
countries. Wireless communication technologies such as URLLC, edge
intelligence, and cloud computing have been applied to combat the COVID-19
pandemic in different ways [85]. For example, high-speed live video
conferencing based on URLLC enables healthcare professionals to discuss with
patients in a low-latency and reliable manner for timely COVID-19 outbreak
analysis. COVID-19 data can be processed using data-driven edge intelligence
techniques by integrating edge computing and AI for accurate and fast disease
diagnosis [86]. Fig. 7 illustrates a case study of using edge intelligence for
COVID-19 analysis, by applying FL at the network edge. Each edge server
located at a local hospital institution is equipped with a local GAN
consisting of a discriminator and a generator based on CNNs to learn the
COVID-19 data distribution using its own local image dataset. Then, the local
GANs synchronize and exchange the learned model parameters for aggregation at
a cloud server, which then returns a new version of the global model to all
institutions for the next training round. This process is repeated until a
desired accuracy is achieved, aiming to generate realistic COVID-19 images for
the detection of COVID-19. The use of edge intelligence thus offers unique
benefits to COVID-19 analytics, including privacy protection and large-scale
data processing capabilities.

Figure 8: DL for V2V network management.

###  IV-B 6G for Vehicular Internet of Things (VIoT) and and Autonomous
Driving

The advances in 6G technologies have greatly transformed vehicular Internet of
Things (VIoT) networks and thus revolutionized intelligent transportation
systems (ITSs). The work in [87] exploits mMTCs to enable vehicle-to-
everything (V2X) connectivity for the transmission of short vehicular
information payloads by a high number of vehicles without human interaction in
6G-based VIoT networks. To do so, the idiosyncrasies of V2X are taken to
strike a trade-off between scalability, reliability, and latency via a vehicle
discovery approach in which a discovery entity located at the base station
collects information about the proximity of the vehicles. Accordingly, to
optimize the discovery scheme, signature properties such as time slots and
hash functions are tuned, aiming to minimize the false-positive probability to
schedule the radio resources for V2X data communications under the available
spectrum budget. In future 6G-based VIoT, data rate prediction is a
challenging task due to the complex interdependency between factors such as
mobility, channels, and networking. ML can be an efficient approach to mimic
the possible behavior of network-assisted throughput prediction in future 6G
vehicular networks [88] by learning the historic network load information
based on control channel analytics.  To unleash the potential of vehicular
intelligence in VIoT, edge intelligence functions with ML are integrated into
road side units (RUSs) that are responsible for performing the estimation of
traffic volume and weather forecast based on the aggregation of local
observations from vehicles [89]. To further enhance the scalability of
vehicular systems in the context of 6G, a distributed estimation approach is
proposed, by allowing for local estimation at distributed vehicles through
wireless data exchange with neighbouring vehicles within the communication
range.

The role of DL in providing intelligence for 6G-based VIoT is also examined in
[90] by leveraging its high-dimensional generalization ability to model
vehicular communication channels and support networking management, such as
optimal resource allocation using DRL algorithms [91], as illustrated in Fig.
8. In this context, how to build trust for data learning and reasoning is
highly important as the data training is normally treated as a process in a
black box where the input and output are known only. Therefore, a trust broker
entity is proposed for 6G-VIoT networks, which is able to provide reasoning
for learning actions at the DL controller, e.g., the base station. Thus, the
learning process can be interpreted so that business stakeholders can
understand the data training process, e.g., which data features cause which
decisions. Vehicular intelligence is also the focus in [92] where DL
techniques are adopted to autonomously schedule the data transmission. This is
enabled by using three approaches, supervised learning for data rate
estimation, unsupervised learning for recognizing geospatially-dependent
uncertainties of the estimation model, and reinforcement learning (RL) for
autonomously coordinating data transmissions based on anticipated resource
efficiency. This combined scheme is promising to achieve multi-objective
optimization in VIoT, from resource allocation to data rate maximization. In
the future, it is useful to develop cooperative DL approaches like FL since
6G-VIoT networks are expected to be highly scalable and distributed. In
addition to network management, DL-based intelligent software is deployed at
the vehicular data controller that is able to provide useful solutions for
security protection in 6G-based VIoT networks [93]. A new weight-based
ensemble DL approach is suggested to detect intrusion and attack risks in
vehicular communications. Support vector machine is firstly used to map data
to a high-dimensional space through a kernel function and build the optimal
classification hyperplane to extract the sample classes. K-neighborhood is
then applied to determine the categories of extracted samples, which is then
classified by a decision tree. Numerical results demonstrate the high
performances of DL in terms of high attack detection accuracy (an increase of
5%-20%, compared to non-learning approaches), which thus helps improve the
reliability and security of vehicle networks in 6G.

In the future vehicular networks, autonomous driving (AV) will play a
significant role in improving transportation quality, road safety, and
vehicular energy efficiency. 6G technologies are expected to provide exciting
opportunities to meet the stringent service requirements of AV applications
for reliability and high-speed communications [94]. To fully realize AV in the
6G, it is important to investigate the communication performances of the V2V
networks since each vehicle is regarded as an entity with full control and
recognition in the interconnected vehicular system. In this context,
cooperative driving is enabled through information sharing and driving
coordination among vehicles, where DL can come as a natural solution to
perform fast prediction of the V2V communication performance bounds for
intelligent control of inter-vehicle distances. Edge intelligence is also of
paramount importance for providing intelligent functions at the network edge,
e.g., RSUs, for controlling the AV system [95]. For example, AV controllers
can be located at edge servers embedded with DL processors to train vehicular
data for implementing autonomous driving decision making and high-definition
mapping for navigation. FL can also be exploited to provide cooperative
learning and perform federated vehicular communications among vehicles and
edge servers, while preserving user privacy and reducing network overheads
caused by raw data sharing [96].

###  IV-C 6G for Unmanned Aerial Vehicles (UAVs)

Enabled by the emerging wireless technologies, many research efforts have been
put into exploring the applications of 6G-UAV networks. The work in [97]
considers a cell-free UAV network for 6G-based wide-area IoT with focus on a
UAV flight process-oriented optimization. This can be done by formulating a
data transmission efficiency maximization problem that can be solved by taking
large-scale channel state information, on-board energy, and interference
temperature constraints into account. The proposed approach is also promising
to identify the cell-free coverage patterns to support massive access for
wide-area IoT devices in the future 6G era. The authors in [98] propose a UAV-
supported clustered non-orthogonal multiple access (C-NOMA) scheme [99] for
supporting wireless powered communications in 6G-enabled IoT networks. Given
the popularity of cluster IoT terminals, a terminal clustering strategy is
adopted based on intra-cluster NOMA communications which allows UAV to
transmit radio signals to IoT terminals. A synergetic optimization solution
for UAV trajectory planning and subslot allocation is derived by portioning
the downlink energy transfer subslot and uplink information transmission
subslot. This aims to maximize the achievable sum rates of all IoT terminals
which are confirmed via numerical simulations. Unlike the work in [98], the
project in [100] pays attention to characterize the UAV-to-ground channel with
arbitrary three-dimensional (3D) UAV trajectories for UAV-based 6G networks.
To do so, a 3D nonstationary geometry-based stochastic model is derived by
using the multiple-input multiple-output (MIMO) channel configuration, with
respect to distinctions between AV altitudes, spatial consistency, and 3D
arbitrary UAV movement trajectories. Meanwhile, a collaborative multi-UAV
trajectory optimization and resource scheduling framework is studied in [101]
in a 6G-IoT network, where multiple UAVs are used as flying base stations to
transfer energy to multiple terrestrial IoT users. The design is focused on
the association between UAVs and users, by implementing a user association
solution to select the most appropriate user to upload data to a specific UAV.
Accordingly, the ultimate objective is to optimize the average achievable rate
among all IoT users, with respect to the UAV trajectory, sub-slot duration,
and user transmit power. A joint algorithm based on the relaxation and
successive convex optimization methods is derived to solve the proposed
problem, showing a better achievable rate compared to existing schemes.
Similarly, the research in [102] concentrates on optimizing the transmission
rates of UAVs, where UAVs act as mobile relays in NOMA-based cognitive 6G-IoT
networks. A flexible approach is adopted that allows for optimally selecting
relays to provide higher transmission rates under fixed power.

In addition, AI techniques have been used to provide intelligent solutions for
6G-based UAV networks. In fact, UAVs can provide wireless communication
services, edge computing services, and edge caching services when empowered
AI-based solutions [103]. For example, to control the UAV mobility and mission
scheduling for the UAV trajectory planning, AI techniques like ML can be
exploited to predict the future demands of users and service areas based on
historical datasets of movement trajectories and user requests. This not only
adjusts optimally the UAV trajectories to save transmit power but also
enhances the quality of users’ experience. Moreover, AI approaches are very
useful to build proactive edge data caching in UAV-based IoT networks, based
on the data training and prediction capabilities through learning and feedback
processes, e.g., data caching with DRL [104]. Very recently, FL has been
applied in [105] to provide privacy-preserved intelligence for UAV-based 6G
networks. Here, each UAV runs a DL model and exchanges learned parameters with
an MEC server for aggregation. To accommodate the federated data training in
the UAV network with limited batteries and bandwidth spectrum, a resource
allocation problem is considered that is then solved by a DRL algorithm. In
the future, to facilitate operations of UAVs in 6G-IoT networks, regulations
should be put into place to provide guidelines for the deployment of UAVs in
IoT systems, to ensure safety and privacy [106]. Moreover, local licensing
regulations are also important, especially when countries are still in the
process of defining rules for spectrum access rights to UAV manufacturers.
Regulatory authorities should collaborate to address critical issues in UAV
integration into the existing IoT networks, from pricing strategies to network
deployment choices, interference protection and aerial service coordination.

###  IV-D 6G for Satellite Internet of Things (SIoT)

In the 6G era, it is highly essential to integrate satellite communications
into current wireless networks for massive IoT coverage which gives birth a
new domain called SIoT [107], [108]. Conceptually, satellites consist of three
main network tiers, namely LEO, MEO, and GEO to offer global services to the
terrestrial IoT users. Compared to MEO and LEO, the LEO system has been
received priorities in 5G-network generation research due to its lower orbit
height and useful features to support IoT connectivity, such as shorter
transmission delay, smaller path loss. However, in the 6G era, thanks to the
advanced satellite technologies, multiple satellites can be deployed at dozens
of orbits above the earth, LEO systems can thus authentically realize the
global coverage and more efficiency by frequency reuse. Moreover, it is
envisioned to establish inter-satellite links to enable inter-satellite
communications based on THz bands, which can accommodate more satellites and
achieve higher link performances due to its much wider bandwidth, compared to
existing spectrum resources in mmWave communication and optical communication
counterparts in the 5G era [107]. As a case study in SIoT, the work in [109]
focuses on research of a LEO satellite network that can support the navigation
of UAV trajectories for IoT data collection missions. A two-mode communication
model is derived, i.e., the UAV carry-store mode and the satellite network
relay mode for IoT data transmission. In this context, a UAV energy cost
optimization problem is formulated that is then solved by a column generation
based algorithm. To provide further insights into SIoT, the authors in [110]
build a comprehensive model along with highlighting technical discussions and
challenges; this includes satellite base stations and a network of terrestrial
users where satellite communications can be realized via relays located at
terrestrial base stations. Satellite communications can be integrated with the
RIS technology to enhance transmit power consumption at the satellites, as
shown in [111]; thus, a RIS-assisted LEO satellite framework is created. RIS
units can be set up on the rectenna arrays to support IoT data broadcasting
and beamforming based on transmit features such as carrier frequencies. In
line with the energy discussion, an energy-aware massive random access scheme
for satellite communications in 6G-enabled global SIoT is considered in [112].
In reality, to communicate to the base station via the uplinks, each IoT
device needs to create a random access process, by selecting an available
preamble from the provided preamble set for data transmission. Accordingly, an
enhanced preamble sequence scheme is proposed to perform one-step fractional
timing advance estimation to avoid additional signalling overhead and energy
costs. Simulation results confirm a high performance in terms of an increase
of up to 15% in timing estimation accuracy compared to existing approaches.

In addition to energy management, spectrum sharing is another critical issue
in the satellite communications for 6G-based IoT networks [113]. The
combination of NOMA and cognitive radio technologies can help to overcome the
spectrum scarcity. This is based on that fact that NOMA is able to enhance the
spectrum efficiency by enabling users to transmit on the same carrier and
classifying users by different power levels. Furthermore, the cognitive radio
facilitates dynamic spectrum sharing for efficient spectrum usage. Towards a
trade-off between system flexibility, network performance, latency and energy
management in 6G-based SIoT networks, a holistic architecture is provided in
[114] with different useful applied domains, as illustrated in Fig. 9. For
instance, SIoT is flexible to offer remote connectivity where terrestrial
networks are overloaded or not possible, like seas and deserts. Further, it is
possible to provide data services, such as data offloading and caching to
support service delivery for terrestrial base stations with billions of
connected users. SIoT platforms can enable energy sustainability via the use
of aerial IoT devices such as UAVs and balloons, with renewable sources from
the space that may be not available at the ground-based stations [115].

Figure 9: The 6G-based SIoT network and use cases.

###  IV-E 6G for Industrial Internet of Things (IIoT)

Recently, the roles of 6G have been investigated in the IIoT domain. For
example, ML approaches have been applied to provide intelligence for 6G-based
IIoT networks. The study in [116] investigates the potential of ML-based CNNs
in optimizing resource allocation in massive IIoT systems with 6G through a
multi-agent system. Due to the limited resource of IIoT sensors, the
deployment of sensor nodes is often implemented randomly, introducing
unnecessary energy costs. CNNs are useful to perform intelligent sensor
clustering via data mining and predictions based on neural backpropagation and
interaction in the training process with historical datasets. Numerical
simulation verifies a better resource allocation with lower power consumption
and reduced complexity. Meanwhile, transfer learning is adopted in [117] to
coordinate the data distribution and transmission in a blockchain-based 6G
IIoT network, where blockchain is integrated into edge nodes and a cloud
server for building a secure data sharing platform. An integrated fog-cloud
computing model is integrated to handle the data analytics and blockchain
data, by taking the block size, CPU and memory usage, and network delays into
account. The reasoning ability of ML is exploited in [118] for dealing with
fault detection and fault prediction problems in 6G-based IIoT systems with
industrial sensors and actuators. To this end, a long short-term memory
network with a DNN is integrated to perform online fault prediction in a mini
manufacturing system, aiming to optimize plant operations and accelerate the
production process. Online learning enabled by data-driven ML is also helpful
to 6G-based IIoT network operation interpretation thanks to the reasoning
ability of the deep neural structures [119]. This is particularly important
for future 6G-IIoT applications where data volumes are extremely large and its
architecture is highly complex which can be explained via data learning.

Intelligent agriculture can be realized where AI techniques would be very
useful to optimize agricultural processes, while blockchain is able to enable
secure production and logistics, e.g., secure package delivery from the farms
to the supermarkets via immutable block ledger [120]. Further, space-
terrestrial communications with UAVs has the great potential to advancing
precision agriculture, by allowing for aerial-based soil measurement via the
sensing capabilities of UAVs over the large-scale coverage [121]. UAVs can be
also exploited to support crop imaging from a low altitude, aiming to provide
a holistic view on the farm for automatic management of agriculture
production. Beyond these sectors, mining is another industrial domain that can
be also received much benefits from communication advances [122], through
service management solutions offered by 6G technologies, e.g., massive URLLC-
enabled robotics for low-latency and automatic mineral manufacturing, and AI
for mineral predictive production control and pricing prediction.

Although 6G brings unprecedented benefits to IIoT, security and privacy
represent significant challenges to be addressed. Blockchain with its
decentralization and immutability has emerged as a promising solution to
provide security and trust for 6G-based IIoT networks. A secure data
aggregation approach for 6G-IIoT systems is considered in [123] based on
blockchain. This can build decentralized databases stored over the IIoT
devices without the need for an authority. Instead, it relies on the consensus
of all participating nodes that can verify and authenticate the task
assignment and the task data. In an effort to further enhance privacy
protection, a sensitive tasks decomposition and task receivers grouping method
is integrated; hence, the direct disclosure of privacy in sensitive tasks.
Another blockchain-based solution for security enhancement in 6G-IIoT networks
is also suggested in [124] along with FL that manages the industrial data
learning process via the collaboration of end users and a base station. In
this context, to reduce the unreliable and long-distance communications
between end users and edge servers, digital twins are exploited to bridge the
physical IIoT systems with the digital world for robust FL training.

In summary, we list the 6G-IoT applications in Table V to give better insights
into the technical aspects of each reference work.

TABLE V: Taxonomy of 6G-IoT applications.  Applications |  Ref. |  Use Case |  Applied 6G technology |  Key Contributions
---|---|---|---|---
6G-based HIoT |  [77] |  Remote health monitoring |  mURLLC and THz communications |  A solution for remote health monitoring with extremely low-latency data transmission and network connections between wearables and remote doctors.
[78] |  Remote surgery |  UAVs and blockchain |  A more comprehensive model using the UAVs and blockchain integration that can provide both fast and secure features for surgery processes.
[80] |  Healthcare data communications |  mURLLC |  An improved healthcare communication approach using mURLLC for communication between nano-devices, implants, and on-body sensors and edge/cloud servers.
[82] |  Connected ambulance |  URLLC |  A scheme using 6G-based URLLC to facilitate connected ambulance in future healthcare, by allowing real-time video streaming with high color resolution for reliable diagnosis.
[83] |  Health data analytics and resource allocation optimization |  ML |  A ML-based framework to analyze historical health records of stroke out-patients collected from wearable sensors and radio resource allocation optimization in heathcare-based 6G heterogeneous networks.
[84] |  Mobility management |  ML |  A solution using ML for ML to optimize mobility management processes in 6G-based health networks.
[86] |  COVID-19 data analytics |  Edge intelligence |  Edge intelligence-based solutions for COVID-19 data learning and diagnosis in hospital networks. Compared with other works [83, 84], this scheme involves high training latency due to highly deep CNN structure.
6G-based VIoT |  [87] |  Enabling V2X connectivity |  mMTC |  An approach using mMTC to enable V2X connectivity for the transmission of short vehicular information payloads in 6G-based VIoT networks.
[89] |  Vehicular network estimation |  ML |  An ML-based scheme to perform estimation of traffic volume and weather forecast based on the aggregation of local observations from vehicles at RUSs.
[90] |  Vehicular communication modelling |  DL |  Unlike [89], this scheme relies on the high-dimensional generalization ability of DL to model vehicular communications in 6G-based VIoT.
[92] |  Vehicular intelligence |  DL |  An approach for enabling vehicular intelligence where DL techniques are adopted to autonomously schedule the data transmission.
[94] |  Autonomous driving |  DL |  A scheme for cooperative driving enabled by DL-based fast prediction of the V2V communication performance bounds towards intelligent control of inter-vehicle distances.
[95] |  Autonomous driving |  DL |  A model using DL-based edge intelligence that is mainly applied to implement autonomous driving decision making and high-definition mapping for vehicular navigation.
6G-based UAV |  [97] |  UAV flight process-oriented optimization |  UAV |  A cell-free UAV network for 6G-based wide-area IoT with focus on a UAV flight process-oriented optimization.
[98] |  UAV wireless powered communications |  UAV |  A UAV-based scheme for supporting wireless powered communications in 6G-enabled IoT networks. Compared with [97], UAV communications are further supported by the NOMA technology, which allows UAV to transmit radio signals to IoT terminals.
[101] |  Multi-UAV trajectory optimization and resource scheduling |  UAV |  A collaborative multi-UAV trajectory optimization and resource scheduling framework in a 6G-IoT network.
[103] |  Intelligent UAV platforms |  ML |  UAVs platforms can provide wireless communication services, edge computing services, and edge caching services with ML techniques.
[105] |  Federated UAV communications |  FL |  As a further design advancement, a privacy-enhanced ML approach is employed via FL to provide reliable intelligence for UAV-based 6G networks.
6G-based SIoT |  [109] |  Satellite IoT data collection |  UAV |  A LEO satellite network to support in the navigation of UAV trajectories for IoT data collection missions.
[111] |  RIS-based satellite communications |  RIS |  A model for RIS-based satellite communications to enhance transmit power consumption at the satellites, aiming to form a RIS-assisted LEO satellite framework.
[112] |  Energy-aware massive random access |  Satellite communications |  An energy-aware massive random access scheme for satellite communications in 6G-enabled global SIoT.
[113] |  Satellite spectrum sharing |  Satellite communications |  A study for spectrum sharing in satellite communications for 6G-based IoT networks. Moreover, this scheme is integrated with NOMA to further enhance the spectrum efficiency.
6G-based IIoT |  [116] |  Optimal industrial resource allocation |  ML |  An ML-based CNNs approach for optimizing resource allocation in massive IIoT systems with 6G through a multi-agent system.
[117] |  Transmission latency minimization |  ML |  A transfer learning-based method to coordinate the intelligent data distribution and sharing. Particularly, this scheme offers security features by using an immutable data control solution with blockchain.
[123] |  Secure industrial data aggregation |  Blockchain |  A secure data aggregation approach for 6G-IIoT systems based on blockchain that can build decentralized databases stored over the IIoT devices.
[124] |  Security enhancement in IIoT |  Blockchain and FL |  A blockchain-based solution for security enhancement in 6G-IIoT networks. Compared with related works [117, 123], this scheme is integrated with FL that further improves data privacy without sharing of raw data.

##  V Research Challenges and Future Directions

In this section, we highlight several interesting research challenges and
point out possible future directions in 6G-IoT.

###  V-A Security and Privacy in 6G-IoT

The use of 6G technologies will revolutionize the IoT networks and services,
with many network features such as high reliability, ultra-low latency, and
massive wireless coverage.  However, the integration of 6G into IoT networks
may be vulnerable to threats related to wireless interface attacks such as
unauthorized access to data at computing units/servers, threats to integrity
in the access network infrastructure, and denial of service (DoS) to software
and data centres [125]. For example, the diversity of IoT devices and access
mechanisms as well as massive device connectivity in large-scale IoT access
networks brings new security challenges as handovers between different access
technologies increase the risk of attacks. The growth of connections between
devices and computing nodes at the network edge also increases the security
and privacy threats, where eavesdropping attacks, hijacking attacks, spoofing
attacks and DoS attacks may occur in data communications and data management
centres. Additionally, to realize intelligent 6G-IoT networks, AI functions
can be deployed at distributed edge nodes, where the data training can be
manipulated in a spectrum access system by inserting fake signals or modified
parameters. Thus, a malicious attack can illegally take advantage of a large
portion of spectrum by denying the spectrum to other users. Attackers can also
exploit the distributed data training nature and the dependencies on edge
computing to launch different attacks such as malicious data injection, data
poisoning or spoofing that adversely affect the training outputs of AI
functions in intelligent 6G-IoT systems.  Also, edge intelligence can face
security vulnerabilities due to the distribution of AI functions at the
network edge, where attacks can deploy data breaches or modifications while
the management of remote 6G core network controllers is limited [126].
Moreover, the deployment of satellite-UAV-IoT communications over the
untrusted environments in space can be hindered by data privacy leakage caused
by third-parties and adversaries during the data exchange and transmission
between satellite base stations, UAVs, and terrestrial IoT users.

Therefore, the risk mitigation must be considered to ensure high degrees of
security and privacy for 6G-IoT. For example, perturbation techniques [127]
such as differential privacy or dummy can be used to protect training datasets
against data breaches in the edge intelligence-based 6G-IoT networks, by
constructing composition theorems with complex mathematical solutions [128].
As an example, differential privacy is applied in [129] by inserting
artificial noise (e.g., Gaussian noise) to the gradients of NN layers to
preserve training data and hidden personal information against external
threats while guaranteeing convergence. A novel privacy-preserving data
aggregation solution is also integrated under fog computing architecture to
satisfy $\epsilon$ differential privacy in the fashion that the aggregation
results are close to the actual results while adversaries cannot extract the
ground truth in the exchanged gradients. A Reference Energy Disaggregation
dataset including specific information about the electricity consumption of
households and a healthcare dataset including more than 1 million medical
records are employed for simulations. Implementation results demonstrate that
differential privacy helps achieve an up to 6% higher data protection degree,
compared to traditional Laplace differential privacy approaches, under various
privacy budget settings. However, the application of differential privacy also
comes with the cost of training quality degradation. In future work, it is
suggested to develop accuracy-aware differential privacy designs to strike the
trade-off between training quality and privacy protection. Moreover,
blockchain is a promising solution to build trust and establish secure
decentralized communications for UAV-IoT networks [130]. Each UAV can act as a
blockchain node to perform decentralized data sharing and communications,
where lightweight mining mechanisms should be also designed for low-latency
data consensus with respect to the resource constraints of flying devices like
UAVs. This blockchain technique becomes more important in the 6G era since IoT
networks tend to be decentralized and deployed over the large scale that is
well suitable for the decentralization feature of blockchain.

###  V-B Energy Efficiency in 6G-IoT

In future 6G-based IoT networks, how to achieve high energy efficiency is a
major concern. The data communications and service delivery services, e.g.,
vehicular data sharing in autonomous driving, packet delivery in the space
with UAV communications, requires significant energy resources to ensure the
network operations. Besides, each base station in wireless cellular networks
normally consumes 2.5 kW to 4 kW [131], which means that the deployment of
massive 6G-IoT networks with thousands of stations results in enormous energy
consumption that also increases carbon emissions. Designing energy-efficient
communication protocols via optimization is desired to realize green 6G-IoT
networks. For example, the work in [132] jointly optimizes the QoS and energy
consumption in 6G-based smart automation systems. This can be done by
implementing a 6G-driven multimedia data structure model with respect to QoS
parameters, such as packet loss ratio and average transfer delay during
energy-efficient multimedia transmission.

Energy harvesting techniques to exploit the renewable energy resources would
be very useful to build green 6G-IoT systems, e.g., IoT devices can harvest
power from ambient environments, such as wind power, solar power, vibration
power, and thermal power to serve their communications and computing services
[133]. For example, a solar energy harvesting solution is considered in [134]
for HIoT networks, where implantable sensors can harvest solar power from
natural sunlight for serving the sensory data transmissions via a Bluetooth
low energy module in a transparent silicon housing for HIoT-based healthcare
monitoring. Experiments are conducted via a wireless implantable sensor
prototype with a solar panel and access point working over a 10 minutes
operation cycle, showing a stable energy harvesting while the lifetime of the
HIoT-based healthcare system is significantly improved. The future researchers
should further investigate the energy efficiency issues in 6G-IoT networks at
higher altitudes, such as satellite networks with flying devices and base
stations, where space communication technologies are adopted and energy
harvesting is dependent on device trajectories and ambient environments.

###  V-C Hardware Constraints of IoT Devices

The hardware constraint of IoT devices is another possible challenge in
communications and computations 6G-based IoT networks. For example, in
intelligent 6G-based healthcare, wearable sensors and mobile devices should be
able to simultaneously run AI functions to achieve edge intelligence and
implement data transmission with URLLC. Due to the constraints of hardware,
memory, and power resources, certain IoT sensors cannot meet the corresponding
computational requirements [135]. The data exchange between IoT sensors and
the network server also incurs communication overhead which scales up with the
task sizes. Thus, new hardware design is needed toward future smart and
powerful IoT devices. For instance, the work in [136] introduces a software-
based DL accelerator to support data training on mobile sensor hardware. The
key idea is to use a set of heterogeneous processors (e.g., GPUs) where each
computing unit exploits distinct computational resources for processing
different inference phases of DL models. This aims to optimize hardware usage
for data training without compromising the accuracy performances, enabled by
two algorithms, i.e., runtime layer compression and deep architecture
decomposition. Simulation results demonstrate the superior performance of the
proposed approach in terms of low execution time and energy consumption in AI
hardware running, compared to cloud offloading-based approaches. This research
is promising to develop mobile AI inference for edge intelligence, which would
enable on-device IoT implementation in the future wireless networks. Moreover,
a scheme called Tiny-transfer learning (TinyTL) is considered in [137] for
memory-efficient on-device sensor learning. To compensate for the capacity
loss, a memory-efficient bias module, called lite residual module is
integrated which enhances the model capacity by refining the intermediate
feature maps of the feature extractor with a minimal memory overhead.
Numerical simulations are implemented using image classification datasets,
showing that the proposed on-device learning approach can achieve a
competitive accuracy performance (above 90%), compared to the traditional
training solutions (e.g., Inception-V3), while reducing the training memory
footprint by up to 12.9. More research efforts are needed to provide hardware-
based AI training solutions on nano IoT devices and embedded wearables in
future intelligent 6G IoT networks, such as intelligent-enhanced living
assistance services.

###  V-D Standard Specifications for 6G-IoT

The emergence of 6G technologies potentially transforms the shape of IoT
markets and revolutionizes the IoT ecosystems with advanced wireless
networking features. However, the development of 6G-IoT systems requires
stringent standard specifications that calls the collaboration of all business
stakeholders such as network operators, services providers, and customers
[138]. The lack of system standards can hinder the deployment of 6G functions
and technologies in customer IoT systems. Moreover, the introduction of
vertical 6G-IoT use cases in future intelligent networks imposes major
architectural changes to current mobile networks in order to simultaneously
support a diverse variety of stringent requirements (e.g. autonomous driving,
e-healthcare, etc.) In such a context, network standards hold an important
role in deploying 6G-IoT ecosystems at large-scale due to the reliance of
other important services such as computing and 6G server-IoT device
communication protocols. A popular protocol is MODBUS [139] which is the
communication standard for connectivity of computer servers, industrial
electronic equipments, and sensor devices in IoT environments. MODBUS is
developed based on a variety of enabling protocols such as remote terminal
unit (RTU), TCP/IP, and UDP. It relies on mesh networking architectures and
can provide industrial communications with the supervisory control over the
industrial radio bands. Recently, the Industry Specification Group of the
European Telecommunications Standards Institute has released the initiative
called ETSI Multi-access Edge Computing [140], which aims to leverage
seamlessly edge computing and communication frameworks for integrating various
edge-based IoT applications originating from the vendors and service providers
in the next-generation wireless networks. This would facilitate various IoT
services such as video analytics, augmented reality, data caching, and content
delivery. In the near future, the interested stakeholders should pay more
attention to develop new standard specifications for new space-air-ground-
underwater communications, e.g., new IoT satellite IoT communications, which
would be significant to the future deployment of new commercial IoT
applications such as space travel and deep sea marine services. The summary of
challenges and potential directions in 6G-IoT research is presented in Table
VI.

TABLE VI: Summary of key research challenges and possible directions for 6G-IoT. Challenges |  Description |  Possible directions
---|---|---
Security and Privacy in 6G-IoT |  • The developments of 6G-IoT come at the cost of new security and privacy concerns, e.g., unauthorized access to data at computing units/servers, threats to integrity in the access network infrastructure, and data breaches in edge intelligence [126]. • The deployment of satellite-UAV-IoT communications over the untrusted environments in the space can be hindered by data privacy leakage caused by third-parties and adversaries during the data exchange and transmission. |  • Perturbation techniques such as differential privacy or dummy can be useful to protect training datasets against data breach in the edge intelligence-based 6G-IoT networks [127]. • Blockchain is a promising solution to build trust and establish secure decentralized communications for 6G-IoT networks [130] over the space and untrusted wireless environments.
Energy Efficiency in 6G-IoT |  • In future 6G-based IoT networks, how to achieve high energy efficiency is a major concern, e.g., energy resources needed for data transmission, communications, and service delivery services. • Building energy-efficient wireless communication protocols is highly needed for green 6G-IoT networks. |  • The [132] jointly optimizes the QoS and energy consumption in 6G-based smart automation systems, by implementing a 6G-driven multimedia data structure approach. • Energy harvesting techniques to exploit the renewable energy resources will be very useful to build green 6G-IoT systems [133].
Hardware Constraints of IoT Devices |  • The participations in 6G communications and computation tasks poses new challenges into hardware designs for IoT devices. • Due to the constraints of hardware, memory, and power resources, certain IoT sensors cannot meet these computational requirements in 6G-based customer applications [135]. |  • The work in [136] introduces a software-based deep learning accelerator to support AI/DL training on mobile sensor hardware. • In the future, it is desired to develop lightweight on-device hardware platforms to meet service computation demands, e.g., on-device edge intelligence in mobile 6G networks.
Standard Specifications in 6G-IoT |  • The development of 6G-IoT systems requires stringent standard specifications that calls the collaboration of all business stakeholders such as network operators, services providers, and customers [138]. • The introduction of vertical 6G-IoT use cases in future intelligent networks imposes major architectural changes to current mobile networks for supporting a diverse variety of stringent requirements. |  • It is important to establish new standards for computing and 6G server-IoT device communication protocols. • A standard initiative ETSI Multi-access Edge Computing is introduced in [140], allowing for leveraging seamlessly edge computing and communication resources in various edge-based IoT applications.

##  VI Conclusions

6G has recently sparked much interest in both industry and academia due to its
appealing features compared to previous generations of wireless networks. In
this article, we have explored the opportunities brought by the 6G
technologies to support IoT networks through a holistic survey based on the
emerging study activities in the field. This work is motivated by the lack of
a comprehensive survey on the use of 6G for IoT. To bridge this gap, we have
first introduced the recent advances in FL and IoT and discussed the key
requirements of 6G-IoT integration. We have then identified and analyzed the
key 6G technologies for enabling IoT networks, ranging from edge intelligence,
RISs, space-air-ground-underwater communications, THz communications to mURLLC
and blockchain. Next, we have provided a holistic discussion on the use of 6G
in emerging IoT applications, such as HIoT, VIoT and autonomous driving, UAVs,
SIoT, and IIoT. From the extensive survey, the key technical aspects and
emerging use cases in 6G-IoT have been also summarized and analyzed via
taxonomy tables. Finally, we have identified potential challenges and
highlighted possible directions for future research.

Research on 6G-IoT networks and applications is still in its infancy. This
being said, it is envisioned that 6G will transform the current IoT network
infrastructures and bring new levels of service quality and user experience in
the future applications. We believe our timely work will shed valuable light
on the research of the 6G-IoT integration topics as well as motivate
researchers and stakeholders to augment the research efforts in this promising
area.

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