Security and Privacy for 6G Massive IoT E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Anticipate the security and privacy threats of the future with this groundbreaking text
The development of the next generation of mobile networks (6G), which is expected to be widely deployed by 2030, promises to revolutionize the Internet of Things (IoT), interconnecting a massive number of IoT devices (massive IoT) on a scale never before envisioned. These devices will enable the operation of a wide spectrum of massive IoT applications such as immersive smart cities, autonomous supply chain, flexible manufacturing and more. However, the vast number of interconnected IoT devices in the emerging massive IoT applications will make them vulnerable to an unprecedented variety of security and privacy threats, which must be anticipated in order to harness the transformative potential of these technologies.
Security and Privacy for 6G Massive IoT addresses this new and expanding threat landscape and the challenges it poses for network security companies and professionals. It offers a unique and comprehensive understanding of these threats, their likely manifestations, and the solutions available to counter them. The result creates a foundation for future efforts to research and develop further solutions based on essential 6G technologies.
Readers will also find:
- Analysis based on the four-tier network architecture of 6G, enhanced by Edge Computing and Edge Intelligence
- Detailed coverage of 6G enabling technologies including blockchain, distributed machine learning, and many more
- Scenarios, use cases, and security and privacy requirements for 6G Massive IoT applications
Security and Privacy for 6G Massive IoT is ideal for research engineers working in the area of IoT security and designers working on new 6G security products, among many others.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Acknowledgments
Notes
Introduction
1 Threat Landscape for 6G-Enabled Massive IoT
1.1 Introduction
1.2 6G Vision and Core Values
1.3 Emerging Massive IoT Applications Enabled by 6G
1.4 Overview of a 6G Network Architecture to Enable Massive IoT
1.5 Security Objectives in Massive IoT in 6G
1.6 Security Threats in Massive IoT in 6G
1.7 Conclusion
References
Notes
2 Secure Edge Intelligence in the 6G Era
2.1 Introduction
2.2 Background
2.3 Security Challenges in 6G EI
2.4 Privacy Challenges in 6G EI
2.5 Trust Challenges in 6G EI
2.6 Security Standardization for EI and 6G
2.7 Conclusion
References
3 Privacy-Preserving Machine Learning for Massive IoT Deployments
3.1 Introduction
3.2 PPML for IoT
3.3 Secure Multiparty Computation
3.4 Fully Homomorphic Encryption
3.5 Oblivious Neural Networks (ONN)
3.6 Decision Trees
3.7 Software and Frameworks
3.8 Lightweight FHE and MPC for IoT
3.9 Alternative Solutions
3.10 Conclusions
References
Notes
4 Federated Learning-Based Intrusion Detection Systems for Massive IoT
4.1 Introduction
4.2 Intrusion Detection Systems (IDSs) in IoT
4.3 Federated Learning: A Decentralized ML Approach
4.4 Federated Learning-Based IDSs for IoT
4.5 Model Aggregation Approaches and Algorithms in FL
4.6 Challenges and Future Directions in FL-Based IDS for IoT
4.7 Conclusion
List of Abbreviations
References
5 Securing Massive IoT Using Network Slicing and Blockchain*
,†
5.1 Introduction
5.2 Background
5.3 Challenges on Massive IoT
5.4 Securing IoT Using Network Slicing and Blockchain
5.5 Open Challenges
5.6 Conclusion
References
Notes
6 Physical Layer Security for RF-Based Massive IoT
6.1 Introduction
6.2 Physical Layer-Based Key Establishment
6.3 Physical Layer-Based Node Authentication
6.4 Physical Layer-Based Data Confidentiality
6.5 Physical Layer-Based Detection of Malicious Nodes
6.6 Conclusion
List of Abbreviations
References
Note
7 Quantum Security for the Tactile Internet
7.1 Introduction
7.2 Preliminaries – Quantum Mechanics
7.3 Preliminaries – Security
7.4 Quantum Key Distribution
7.5 Integrated Physical Layer Security
7.6 Challenges and Known Attacks on Quantum Security
7.7 Oblivious Transfer (OT)
7.8 Conclusion
Acknowledgment
References
Note
8 Physical Layer Security for Terahertz Communications in Massive IoT
8.1 Introduction
8.2 Information Theoretic Analysis of Eavesdropping
8.3 Eavesdropping THz Links
8.4 Multi-path THz Communications
8.5 Absolute Security
8.6 Jamming THz Links
8.7 Summary and the Road Ahead
References
Note
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Privacy and output guarantees for MPC protocols with parties ove...
Table 3.2 Usage of FHE schemes according to functionality.
Table 3.3 Summary of PPML frameworks (only a representative subset).
Table 3.4 MPC frameworks.
Table 3.5 TTP-based frameworks for MPC as presented by [113–115].
List of Illustrations
Chapter 1
Figure 1.1 The Hexa-X 6G vision of connected worlds and key values.
Figure 1.2 Categorization of the representative use case families and respec...
Figure 1.3 Enabling sustainability 6G use case family.
Figure 1.4 Massive twinning 6G use case family.
Figure 1.5 Telepresence 6G use case family.
Figure 1.6 From robots to cobots 6G use case family.
Figure 1.7 Trusted embedded networks 6G use case family.
Figure 1.8 Hyperconnected resilient network infrastructures 6G use case fami...
Figure 1.9 Space–air–ground–sea/underwater network architecture for 6G-enabl...
Figure 1.10 Fundamental Security Objectives in Massive IoT in 6G.
Chapter 2
Figure 2.1 Cloud computing to edge computing shifting for IoT applications....
Figure 2.2 Schematic diagram of edge intelligence.
Figure 2.3 Challenges related to the security of machine learning.
Figure 2.4 Methods for privacy-preserving machine learning.
Chapter 3
Figure 3.1 IoT data flow and trust. Data at rest in the edge (sensors, actua...
Figure 3.2 Private data processing by means of: (a) MPC and (b) FHE.
Figure 3.3 Example of Garble Circuits composed of a simple AND gate.
Figure 3.4 Example of a Garbled Circuit (GC) with three AND gates. Input gar...
Figure 3.5 Basic operations in a secret-shared MPC scheme with Shamir secret...
Figure 3.6 Optimization of private multiplication in secret-sharing MPC, bas...
Figure 3.7 Conversion from secret sharing-based 2PC to GC. The output of the...
Figure 3.8 GC outsourcing. The client secret shares its input and sends both...
Figure 3.9 The left side shows how a convolution would normally be between a...
Figure 3.10 On top, Delphi’s preprocessing phase for the convolutional layer...
Figure 3.11 set up with three servers. The output is given to the relevant...
Figure 3.12 Typical decision tree architecture without padding.
Figure 3.13 Typical scenario for offloading private computation to the cloud...
Figure 3.14 MPC outsourcing to a set of servers.
Figure 3.15 Conventional FHE (a) and hybrid-FHE (b). In hybrid-FHE, homomorp...
Figure 3.16 Hybrid-FHE with homomorphic data aggregation in the IoT hub.
Chapter 4
Figure 4.1 FL is categorized into three primary types based on the distribut...
Figure 4.2 HFL architecture.
Figure 4.3 Training process of VFL.
Figure 4.4 FL-based IDS in IoT use case.
Chapter 5
Figure 5.1 A simplified network slicing example based on TS 23.501.
Figure 5.2 Lifecycle of network slicing.
Figure 5.3 Network slice management in an NFV framework.
Figure 5.4 An example of blockchain-based slice-brokering system.
Figure 5.5 Categorization of blockchain-based solutions for threats in netwo...
Chapter 6
Figure 6.1 Organization of this chapter.
Figure 6.2 The main categorization of PLKE schemes.
Figure 6.3 The process for channel reciprocity-based key establishment.
Figure 6.4 Example of the exchange for signal source indistinguishability-ba...
Figure 6.5 The main categorization of PLA schemes.
Figure 6.6 Illustration of the proposed tag-embedding PLA scheme of [71]....
Figure 6.7 RFF methodology.
Figure 6.8 The main categorization of PL-based data confidentiality schemes....
Figure 6.9 The model for channel coding-based data confidentiality.
Figure 6.10 The main categorization of PL-based malicious node detection sch...
Chapter 7
Figure 7.1 The Tactile Internet network architecture in the 5G and beyond ec...
Figure 7.2 Architecture of a tactile device at the Tactile Edge according to...
Figure 7.3 Geometrical representation of a two-dimensional quantum state (Bl...
Figure 7.4 Diagram of a quantum measurement. The result of the measurement i...
Figure 7.5 Entanglement swapping (a) Each node shares an EPR pair with quant...
Figure 7.6 Various types of QKD. (a) Prepare-and-measure QKD, where Alice tr...
Figure 7.7 An illustration of the DI-QKD. Entangled photon pairs are distrib...
Figure 7.8 Schematic of the MDI-QKD. Alice and Bob send their quantum states...
Figure 7.9 Satellite-based QKD between three cooperating ground stations bet...
Figure 7.10 The four possible polarizations in BB encoding.
Figure 7.11 Diagram of the BB encoding.
Figure 7.12 Seeded modular coding scheme for the wiretap channel, consisting...
Figure 7.13 Timing attack. The scheme of Eve’s attack. HOS: high-speed optic...
Figure 7.14 A diagram of the time-dependent efficiency of the two Detectors:...
Figure 7.15 Trojan-horse attack. An attacker injects a bright light into tra...
Chapter 8
Figure 8.1 (a) Shannon’s cipher model and (b) Wyner’s wiretap channel.
Figure 8.2 Eavesdropping geometry. Alice and Bob are in a direct line-of-sig...
Figure 8.3 Blockage (filled squares, left axis) and secrecy capacity (op...
Figure 8.4 (a) Is a prototype of the MSITM. Eve employs a low-cost and rapid...
Figure 8.5 (a) MSITM attack from perspective of Eve and (b) from perspective...
Figure 8.6 Secure multi-path THz communications.
Figure 8.7 (a) Eavesdropping probability and (b) capacity of THz communicati...
Figure 8.8 (a) Comparison of unjammed (top) and jammed (bottom) eye diagrams...
Figure 8.9 (a) Comparison of unjammed (top) and jammed (bottom) eye diagrams...
Figure 8.10 (a) Unjammed (light gray) and jammed (dark gray) spectra. (b) Ja...
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Acknowledgments
Introduction
Begin Reading
Index
End User License Agreement
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Security and Privacy for 6G Massive IoT
Edited by
Georgios Mantas
Instituto de Telecomunicações, Aveiro, Portugal
Faculty of Engineering and Science
University of Greenwich, Chatham Maritime, UK
Firooz B. Saghezchi
Chair for Distributed Signal Processing
RWTH Aachen University, Aachen, Germany
Jonathan Rodriguez
Faculty of Computing, Engineering, and
Science, University of South Wales, Pontypridd, UK
Victor Sucasas
Cryptography Research Center
Technology Innovation Institute, Abu Dhabi, UAE
This edition first published 2025© 2025 John Wiley & Sons, Ltd.
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Library of Congress Cataloging-in-Publication Data
Names: Mantas, Georgios, editor. | Saghezchi, Firooz, editor. | Rodriguez, Jonathan, editor. | Sucasas, Victor, editor.Title: Security and privacy for 6G massive IoT / edited by Georgios Mantas, Firooz Saghezchi, Jonathan Rodriguez, Victor Sucasas.Description: Hoboken, NJ : Wiley, 2025. | Includes index.Identifiers: LCCN 2024037423 (print) | LCCN 2024037424 (ebook) | ISBN 9781119987970 (hardback) | ISBN 9781119987987 (adobe pdf) | ISBN 9781119987994 (epub)Subjects: LCSH: 6G mobile communication systems–Security measures. | Internet of things–Security measures.Classification: LCC TK5103.252 .S42 2024 (print) | LCC TK5103.252 (ebook) | DDC 005.8–dc23/eng/20241030LC record available at https://lccn.loc.gov/2024037423LC ebook record available at https://lccn.loc.gov/2024037424
Cover Design: WileyCover Image: © Just_Super/Getty Images
List of Contributors
Najwa Aaraj
Cryptography Research Center
Technology Innovation Institute
Abu Dhabi
UAE
Lalita Agrawal
Department of Computer Science and Engineering
Indian Institute of Technology Indore
Indore
India
Ijaz Ahmad
VTT Technical Research Centre of Finland
Espoo
Finland
Saud Althunibat
Faculty of Engineering
Al-Hussein Bin Talal University
Ma’an
Jordan
Abdelrahaman Aly
Cryptography Research Center
Technology Innovation Institute
Abu Dhabi
UAE
Shihan Bao
State Key Laboratory of Wireless Mobile Communications
China Academy of Telecommunications Technology
Beijing
China
Riccardo Bassoli
Deutsche Telekom Chair of Communication Networks
Technische Universität Dresden
Dresden
Germany
and
Centre for Tactile Internet with Human-in-the-Loop (CeTI)
Cluster of Excellence
Dresden
Germany
and
Quantum Communication Networks Research Group
Fakultät Elektrotechnik und Informationstechnik
Institut für Nachrichtentechnik
Technische Universität Dresden
Dresden
Germany
Holger Boche
Lehrstuhl für Theoretische Informationstechnik
TUM School of Computation, Information and Technology
Technische Universität München
Karlsruher Institut für Technologie
München
Germany
Raúl Santos de la Cámara
R&D Department
HI Iberia Ingeniera y Proyectos, S.L.
Madrid
Spain
Haitham Cruickshank
Institute for Communication Systems (ICS), 5G&6G Innovation Centre (5G&6GIC)
University of Surrey
Guildford
UK
Seda Dogan-Tusha
Division of Information and Computing Technology
College of Science and Engineering
Hamad Bin Khalifa University
Qatar Foundation
Doha
Qatar
and
Wireless Institute, College of Engineering
University of Notre Dame
South Bend, IN
USA
Frank H. P. Fitzek
Deutsche Telekom Chair of Communication Networks
Technische Universität Dresden
Dresden
Germany
and
Centre for Tactile Internet with
Human-in-the-Loop (CeTI)
Cluster of Excellence
Dresden
Germany
Alvaro Garcia-Banda
Cryptography Research Center
Technology Innovation Institute
Abu Dhabi
UAE
Felipe Gil-Castiñeira
Telematics Engineering Department
University of Vigo
Vigo
Spain
Hichem Guerboukha
School of Science and Engineering
University of Missouri – Kansas City
Kansas, MO
USA
Erkki Harjula
Center for Wireless Communications
University of Oulu
Oulu
Finland
Shima Hassanpour
Chair of Privacy and Network Security
Technische Universität Dresden
Dresden
Germany
Waleed Hathal
Institute for Communication Systems (ICS), 5G&6G Innovation Centre (5G&6GIC)
University of Surrey
Guildford
UK
Elmehdi Illi
Division of Information and Computing Technology
College of Science and Engineering
Hamad Bin Khalifa University
Qatar Foundation
Doha
Qatar
Josep M. Jornet
Institute for the Wireless Internet of Things
Northeastern University
Boston, MA
USA
Georgios Kambourakis
Department of Information and Communication Systems Engineering, School of Engineering
University of the Aegean
Samos
Greece
Edward W. Knightly
Department of Electrical and Computer Engineering
Rice University
Houston, TX
USA
Abhishek Kumar
Center for Ubiquitous Computing
University of Oulu
Oulu, Oulun Yliopisto
Finland
Tanesh Kumar
Department of Information and Communications Engineering
Aalto University
Espoo
Finland
Georgios Mantas
Instituto de Telecomunicações
Aveiro
Portugal
and
Faculty of Engineering and Science
University of Greenwich
Chatham Maritime
UK
Chiara Marcolla
Cryptography Research Center
Technology Innovation Institute
Abu Dhabi
UAE
Parya H. Mirzaee
Institute for Communication Systems (ICS), 5G&6G Innovation Centre (5G&6GIC)
University of Surrey
Guildford
UK
Daniel M. Mittleman
School of Engineering
Brown University
Providence, RI
USA
Ayan Mondal
Department of Computer Science and Engineering
Indian Institute of Technology Indore
Indore
India
Tri Nguyen
Center for Ubiquitous Computing
University of Oulu
Oulu, Oulun Yliopisto
Finland
Janis Nötzel
Lehrstuhl für Theoretische Informationstechnik
TUM School of Computation, Information and Technology
Technische Universität München
Karlsruher Institut für Technologie
München
Germany
Maria Papaioannou
Faculty of Engineering and Science
University of Greenwich
Chatham Maritime
UK
Juha Partala
Center for Machine Vision and Signal Analysis
University of Oulu
Oulu
Finland
Filippos Pelekoudas-Oikonomou
Faculty of Engineering and Science
University of Greenwich
Chatham Maritime
UK
Ella Peltonen
M3S Research Unit
University of Oulu
Oulu
Finland
Vitaly Petrov
Division of Communication Systems
KTH Royal Institute of Technology
Stockholm
Sweden
Susanna Pirttikangas
Center for Ubiquitous Computing
University of Oulu
Oulu, Oulun Yliopisto
Finland
Marwa Qaraqe
Division of Information and Computing Technology
College of Science and Engineering
Hamad Bin Khalifa University
Qatar Foundation
Doha
Qatar
Marcus de Ree
Instituto de Telecomunicações
Universidade de Aveiro
Aveiro
Portugal
Jonathan Rodriguez
Faculty of Computing, Engineering and Science
University of South Wales
Pontypridd
UK
Firooz B. Saghezchi
Chair for Distributed Signal Processing
RWTH Aachen University
Aachen
Germany
Zhambyl Shaikhanov
Department of Electrical and Computer Engineering
Rice University
Houston, TX
USA
Thorsten Strufe
Chair of Privacy and Network Security
Technische Universität Dresden
Dresden
Germany
and
Centre for Tactile Internet with
Human-in-the-Loop (CeTI)
Cluster of Excellence
Dresden
Germany
and
Chair of IT-Security
Karlsruher Institut für Technologie
Karlsruhe
Germany
Victor Sucasas
Cryptography Research Center
Technology Innovation Institute
Abu Dhabi
UAE
Zhili Sun
Institute for Communication Systems (ICS), 5G&6G Innovation Centre (5G&6GIC)
University of Surrey
Guildford
UK
Ajith Suresh
Cryptography Research Center
Technology Innovation Institute
Abu Dhabi
UAE
Acknowledgments
We thank the 6G-XR project1 and the MATRIS project2 for their contribution toward Chapter 1, and the PHYSEC project3 for its contribution toward Chapter 6. Furthermore, we extend our deepest gratitude to our families, whose patience, cooperation, encouragement, and support have been the cornerstone of this book.
Notes
1
6G-XR project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101096838 and from the Swiss State Secretariat for Education, Research and Innovation (SERI).
2
MATRIS project has received funding from the FCT/PTDC through national funds under grant 2022.07313.PTDC.
3
PHYSEC project has received funding from the NATO Science for Peace and Security Programme under grant SPS G5797.
Introduction
As the fifth-generation (5G) mobile networks are entering the deployment and commercialization phase across the world, the next-generation mobile communications systems (6G) have already come into the discussion, driven by the strict network performance requirements of the emerging disruptive massive IoT applications and the limitations of the existing 5G networks to meet 6G IoT-enabled use cases such as holographic telepresence, fully autonomous driving, and five-sense communications. In fact, the mismatch between the capabilities of the existing 5G systems and the stringent requirements of the future advanced applications leveraging massive IoT is one of the drivers behind the efforts toward the realization of the 6G systems by 2030. Therefore, the 6G systems are expected to extend the capabilities of their predecessors (5G) to a considerably higher level in order to enable the upcoming massive IoT applications that are highly demanding in terms of data rates, latency, reliability, energy efficiency, coverage, and connectivity.
However, the exponential growth in the number and diversity of IoT devices, along with the unprecedented increase in the volume and types of connections in the upcoming 6G-enabled massive IoT ecosystems are factors that are expected to pose many security and privacy challenges in future massive IoT applications. At the same time, attackers are becoming more and more powerful to carry out new and more sophisticated attacks on the massive IoT applications. Therefore, novel security solutions are essential to protect the 6G-enabled massive IoT applications, before they gain the trust of all involved stakeholders and fully harness their potential in the 6G era.
Toward this direction, this book intends to (i) give in-depth insights into the potential security and privacy challenges of the future 6G-enabled massive IoT applications and develop a comprehensive understanding of how security and privacy attacks against such applications can be carried out and how to counter them in the future; and (ii) provide a foundation for organizing research efforts to design and develop proper security and privacy-preserving solutions, based on the key 6G-enabling technologies and for these 6G-enabling technologies, in order to protect massive IoT applications in the 6G era, by building on existing 5G security research in a methodical way. To achieve both objectives, the following two key elements of the sixth-generation (6G) mobile communications systems are taken into consideration: (i) the new envisioned four-tier network architecture of 6G (i.e. space-air-ground-underwater/sea network architecture), enhanced by edge computing (EC) and edge intelligence (EI); and (ii) the main key 6G-enabling technologies including blockchain, distributed machine learning (ML), quantum computing, and technologies closely coupled with physical layer security (PLS) such as Terahertz (THz) communication.
Chapter 1 gives a comprehensive overview of representative massive IoT applications envisioned to be deployed in the 6G era to improve people’s lives and identifies the threat landscape of these applications. The aim is to shed light on their potential key threats and identify the security and privacy issues that should be addressed before the 6G-enabled massive IoT applications build trust among all relevant stakeholders and realize their full potential.
Chapter 2 mainly explores potential security, privacy, and trust issues for 6G EI systems, such as threats related to computational offloading and distributed service provisioning on the edge, vulnerabilities due to the complex integration of various enabling technologies, and trust issues when multiple devices and other stakeholders (e.g. service providers and network operators) share information and resources through a common platform. In addition to the identified security, privacy, and trust issues, the work also briefly presents some of the potential solutions to these issues. Moreover, a brief discussion about ongoing security-related standardization activities for edge intelligence and 6G systems is provided.
Chapter 3 examines two novel techniques within the realm of privacy-enhancing technologies (PETs) that address privacy concerns in IoT and that can scale to massive IoT. Specifically, it covers secure multi-party computation (MPC) and fully homomorphic encryption (FHE), which have thrived in the last few years and paved the way toward efficient Privacy-Preserving Machine Learning (PPML) frameworks. This chapter centers around PPML, explaining the main functionalities and building blocks of MPC and FHE, without delving into mathematical nuance. Furthermore, it discusses the latest PPML frameworks and explains how FHE and MPC can be applied to IoT while complying with the hardware and bandwidth limitations in massive IoT.
Chapter 4 carries out an analysis of current federated learning (FL)-based intrusion detection systems (IDSs) in order to provide a roadmap to support the design and development of effective, efficient, and privacy-preserving IDS for protecting the emerging disruptive massive IoT applications in the 6G era. In particular, it provides the fundamentals of IDSs and ML techniques for IDSs in IoT, as well as the limitations that arise from their centralized nature. In addition, it explores the three primary types of FL, based on the distribution of data, along with their corresponding architectures. Furthermore, it presents use cases, definitive examples, as well as state-of-the-art research works of FL-based IDS for IoT that can be expanded into a more massive network scale, specifically that of massive IoT. Moreover, Chapter 4 explores the concept of model aggregation in FL in detail, including model aggregation approaches and model aggregation algorithms. Finally, it presents challenges and open research directions that require further exploration so that FL-based IDSs can become widely adopted for securing massive IoT environments.
Chapter 5 provides a comprehensive study on securing massive IoT with blockchain and network slicing. It shows an overview of the basic background of massive IoT, IoT-relevant blockchain applications, and its consensus mechanisms. Chapter 5 also presents security and privacy requirements and challenges as well as explores security countermeasures that could be applied to secure massive IoT. Moreover, a review of network slicing security, privacy, and trust threats for 5G and beyond is given. Finally, potential blockchain-based approaches for secure network slicing to protect massive IoT are discussed.
Chapter 6 introduces the reader to the concept of PLS and covers a wide spectrum of PLS methods for achieving node authentication, data confidentiality, and malicious node detection. Furthermore, this chapter discusses two approaches of physical-layer-based key establishment that enable any pair of nodes to establish a shared secret key which is a prerequisite to key-based node authentication and data confidentiality schemes. The covered PLS approaches are aligned with the characteristics of IoT devices which makes them suitable solutions for massive IoT networks.
Chapter 7 introduces the concept of Tactile Internet and the challenges that are posed to future communication networks’ technologies to enable it. Next, Chapter 7 explains the main characteristics of quantum technologies and paradigms in order to take advantage of their security benefits and enable the Tactile Internet efficiently and effectively. Some critical considerations are mentioned in order to highlight the main pros and cons of each solution.
Chapter 8 provides a detailed review on physical layer attacks against THz communications and examines existing PLS schemes to identify potential PLS countermeasures for protecting THz communications and the massive IoT applications relying on them in the 6G era. First, the fundamental trade-offs associated with data eavesdropping from directional THz links are presented. Then, potential attacks are detailed and analyzed specifically targeting THz communications. Finally, in Chapter 8, the discussion proceeds with possible countermeasures to further increase the level of security in IoT services relying on directional THz communications.
1Threat Landscape for 6G-Enabled Massive IoT
Maria Papaioannou1, Georgios Mantas1,2, Firooz B. Saghezchi3, Georgios Kambourakis4, Felipe Gil-Castiñeira5, Raúl Santos de la Cámara6, and Jonathan Rodriguez7
1Faculty of Engineering and Science, University of Greenwich, Chatham Maritime, UK
2Instituto de Telecomunicações, Aveiro, Portugal
3Chair for Distributed Signal Processing, RWTH Aachen University, Aachen, Germany
4Department of Information and Communication Systems Engineering, School of Engineering, University of the Aegean, Samos, Greece
5Telematics Engineering Department, University of Vigo, Vigo, Spain
6R&D Department, HI Iberia Ingenier..a y Proyectos, S.L., Madrid, Spain
7Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK
1.1 Introduction
The strict network performance requirements of the emerging disruptive massive Internet of Everything (IoT) applications cannot be satisfied by the currently developing 5th Generation (5G) mobile networks due to the inherent limitations of these networks [1–5]. Therefore, the focus on the next 6th Generation (6G) of mobile networks with significantly improved key performance indicators (e.g. 1 Tbps peak data rate, 0.1 ms air latency, 1 μs delay jitter, and 1 Gb/m2 area traffic capacity) is inevitable to overcome the limitations of 5G and truly meet the stringent network performance requirements of the emerging disruptive massive IoT applications [2, 5].
6G mobile networks are expected to be commercially available from 2030 onwards and be a key enabler to support a great number of applications with a vast number of interconnected IoT devices [1, 2, 4, 5]. This massive number of 6G-enabled IoT devices will serve for reliable and efficient operation of different verticals such as smart cities, smart homes, smart manufacturing, smart education/training, Connected Autonomous Vehicles (CAVs), and intelligent healthcare. For example, 6G-enabled IoT devices can be used for either healthcare monitoring purposes, e.g. to timely detect any health deterioration of patients suffering from chronic diseases, or collecting mass quantities of data from a wide spectrum of smart city applications (e.g. urban planning, garbage collection), to increase the quality of the services provided to the citizens and reduce the operational costs of the public administrations. However, the high volume of 6G-enabled IoT devices and the increasing number of their interconnections will increase the vulnerabilities of the 6G-enabled massive IoT applications and pose significant security and privacy risks. Therefore, this chapter gives a comprehensive overview of representative massive IoT applications envisioned to be deployed in the 6G era to improve people’s lives and identifies the threat landscape of these applications. The aim is to shed light on their potential key threats and identify security and privacy issues that should be addressed before the 6G-enabled massive IoT applications build trust among all relevant stakeholders and realize their full potential.
Following the Introduction, the rest of the chapter is organized as follows: Section 1.2 presents the vision and values of 6G. Section 1.3 discusses a set of representative massive IoT applications envisioned to be deployed in the 6G era to improve people’s lives. Section 1.4 gives an overview of a 6G network architecture to enable massive IoT. In Section 1.5, the major security objectives of 6G-enabled massive IoT are discussed. Section 1.6 provides a categorization of the security threats targeting the 6G networks based on the security objectives that they intend to compromise. Finally, the chapter is concluded in Section 1.7.
1.2 6G Vision and Core Values
The 6G vision, as conceptualized by European 6G Flagship Project Hexa-X [2, 6], aims to strengthen and enhance the connections between three distinct worlds: the human world, encompassing our senses, bodies, intelligence, and values; the digital world, comprising information, communication, and computing; and the physical world of objects and organisms. This integration will establish a seamless cyber–physical continuum, leveraging networks as powerful tools to enhance our quality of life. However, it is imperative that future networks, facilitating interactions between these worlds, are designed with core principles in mind, including sustainability, trustworthiness, and digital inclusion. Figure 1.1 presents the Hexa-X 6G vision, illustrating the interactions between these worlds and the fundamental values that 6G should uphold. This vision of technological and societal transformation holds the potential for unprecedented economic growth and addressing societal challenges in the 2030s and beyond, as explored further in the subsequent sections. Moreover, achieving this vision will necessitate a fundamental paradigm shift in the design of mobile networks. Multiple key requirements must be reconciled, including catering to the exponential growth in traffic and the proliferation of devices, subnetworks, and markets while upholding high standards of energy efficiency, security, privacy, trust, and efficient deployment for coverage, capacity, and specialized operations. This will enable sustainable growth and foster innovation across sectors and industries.
Figure 1.1 The Hexa-X 6G vision of connected worlds and key values.
Source: Maria Papaioannou.
Additionally, the trend of enhancing human intelligence will persist through closer integration and seamless intertwining of network and digital technologies. With advancements in artificial intelligence (AI) and machine learning (ML), machines will continue to transform data into reasoning and practical insights, enabling humans to better understand and interact with our world. The interactions between the three worlds will pave the way for advanced sensing capabilities, robust experimentation in the digital world, and high-performance and reliable actuation abilities. As the current dedicated machines in domestic and industrial settings evolve into collaborative multipurpose robots and drones, these future networks will incorporate new interfaces for haptic feedback and human–machine interaction, allowing control and interaction with these machines from anywhere to become an integral part of the network.
1.3 Emerging Massive IoT Applications Enabled by 6G
The new types of upcoming IoT-enabled interactions such as holographic communications, five-sense communications, and Wireless Brain–Computer Interfaces (WBCI) or Wireless Mind–Machine Interfaces (WMMI) are anticipated to lead to new massive IoT applications with far more stringent performance requirements (e.g. sub-ms latency, Tbps data throughput, extreme energy efficiency, and ultralow energy consumption) that will not be matched by 5G capabilities [7]. Therefore, 6G is expected to outperform 5G, meet the new demanding levels of performance requirements of the future massive IoT applications, and become their major key enabler [8, 9]. In this section, we discuss a set of representative 6G-enabled massive IoT applications envisioned to be deployed in the 6G era to improve people’s lives.
To organize the discussion of representative 6G-enabled massive IoT applications in a more comprehensive manner, the clustering into use case families (i.e. groups of use cases formed based on the type of usage as well as the research challenges and values addressed), as proposed in [10, 11] in the context of the European 6G Flagship Project Hexa-X [6], has been adopted. In particular, the following six use case families have been considered, as also shown in Figure 1.2, to categorize representative 6G-enabled massive IoT applications: (i) enabling sustainability, (ii) massive twinning, (iii) telepresence, (iv) robots to cobots, (v) trusted embedded networks, and (vi) hyperconnected resilient network infrastructures. These six families of 6G use cases comprise a first baseline to guide the future research directions on 6G, relying on the view of the current European research activities on 6G driven through the 5G PPP and 6G-IA initiatives [3]. Thus, these six use case families are not meant to be exhaustive but are representative of use cases foreseen in the 6G era. In addition, it is worthwhile mentioning that although the use cases described below have been included in the most relevant family, this assignment is not exclusive as there are use cases that may have connections to multiple use case families [11]. Besides, the below-mentioned use cases include several usages characterized as evolutionary or disruptive. The evolutionary ones are those that extend and enhance the 5G usages with new capabilities, while the disruptive ones open up new horizons where 6G could benefit and transform society [3].
Figure 1.2 Categorization of the representative use case families and respective use cases for 6G-enabled massive IoT applications.
Source: Maria Papaioannou.
1.3.1 Enabling Sustainability
The emergence of the 6G system will enable a wide spectrum of applications that go far beyond the current and past generations of mobile networks. Moreover, it is expected that the 6G system will have the ability to make a significant impact on sustainability and societal concerns by contributing to meeting United Nations’ Sustainable Development Goals (UN SDGs) and reducing environmental impact for various industries.
Sustainability is explicably the foremost research challenge addressed by this use case family, including environmental sustainability and sustainable development of human societies. Nevertheless, to fulfill the UN SDGs, this use case family will also embrace other research challenges, such as extreme performance and global service coverage, to make sustainable development a reality. In particular, it is anticipated that the extreme performance and global service coverage will empower underserved populations, bridge the digital divide, monitor and counteract environmental challenges, and optimize operations for sustainable performance. On top of that, trustworthiness is essential for any system addressing societal and environmental challenges to gain widespread confidence in its operation and execution.
The enabling sustainability use case family comprises the following use cases, as also depicted in Figure 1.3: (i) e-health for all, (ii) institutional coverage, (iii) earth monitor, (iv) autonomous supply chain, (v) sustainable food production, (vi) network trade-offs for minimized environmental impact, and (vii) network functionality for crisis resilience.
Figure 1.3 Enabling sustainability 6G use case family.
Source: Maria Papaioannou.
1.3.1.1 E-health for All
UN SDG #3 – “ensure healthy lives & promote wellbeing for all” focuses on the major health challenges that we are facing nowadays. The most important current health challenges that are being addressed are to save lives and reduce deaths. For instance, the associated targets of UN SDG #3 aim to (i) culminate epidemics such as acquired immunodeficiency syndrome (AIDS); tuberculosis, and malaria; (ii) reduce illness and death provoked by drug abuse, traffic, and pollution; (iii) reduce maternal and child mortality rates; and (iv) promote mental health. Nevertheless, as we progressively move toward a more developed world with substantial demographic, environmental, and economic changes, new challenges arise, such as an increase in elderly populations in rural areas due to urbanization, a rise in “welfare diseases” (e.g. obesity and diabetes), as well as a rise in the inequalities between urban areas and less privileged neighborhoods, and the effects of climate change leading to extreme weather conditions and new pandemics. All these challenges are critical considerations, while technology development increases expectations for reproductive and universal healthcare.
To address these challenges, accessible and remote healthcare becomes increasingly important. Nevertheless, for all remote applications, trusting connected services to be reliable, private, and trustworthy is key to accepting technology solutions. Emerging technologies, such as lightweight devices and sensors for on- and in-body monitoring, augmented reality (AR), and haptic and neuro-based interfaces, can provide new opportunities for remote care. Furthermore, Mobile Broadband (MBB) connections to medical expertise can deliver basic e-health services anywhere, complemented by local analysis of samples with dedicated devices and AI agents for first-line support. Local mobile e-health hubs can also provide last-mile connectivity in areas with infrastructure challenges.
Although providing virtual doctor visits to everyone who needs them worldwide would also be an enormous health benefit, to make this reality, a full expansion of cellular network coverage capable of supporting these services is demanded. The development of 6G networks is expected to enable cost-efficient deployments and operations, even in underserved regions, where it is currently cost-prohibitive. The integration of different topologies, such as Non-terrestrial Networks (NTNs) and satellite communication systems, will ensure service availability with affordable solutions. 3GPP Release 171 is already moving in this direction, and in 6G, we can expect to see a tighter integration between different topologies to address healthcare needs.
1.3.1.2 Institutional Coverage
The primary objective should be to ensure that all communities have access to high-quality wireless services, which is the essence of digital inclusion for networks. A more practical target might be to guarantee that educational institutions, healthcare facilities, and other essential organizations worldwide have access to full 6G services, even in underdeveloped nations and isolated rural areas in developed countries. This encompasses not only video services but also immersive and precise communication, such as telepresence, remote virtual education, and medicine. In certain regions, installing fiber communication networks may be prohibitively expensive, such as in remote locations, islands with long distances, or areas with political instability. By expanding and enhancing the deployment and performance of 5G Fixed Wireless Access (FWA), for example, by incorporating the 3GPP Release 16 Integrated Access/Backhaul (IAB) capabilities and 3GPP Release 17 NTN features, wireless backhaul based on 6G can be provided to specialized localities that will act as complete 6G digital havens. These critical social institutions connected to 6G services can promote local connectivity, benefit local businesses and infrastructure, and ensure that society is included in the digital revolution. In 6G, we predict that the wireless backhaul capacity will increase exponentially, allowing for cost-effective provision of 6G services, even in presently remote areas.
1.3.1.3 Earth Monitor
Near-real-time monitoring of system-critical environmental aspects such as biodiversity or climate change can be granted by incorporating ubiquitous bio-friendly energy-harvesting sensors. It is expected that this kind of sensor can effortlessly be deployed in any place (e.g. isolated areas and islands) with cost-effective connectivity via, for example, NTN, local mesh, or long-range terrestrial networks. Thus, sampling substantial environmental data will be feasible not only in well-connected areas such as big cities but also in truly inaccessible areas located far from infrastructure. This worldwide telemetry system offers a variety of potential applications, including enhancing climate and weather forecasting models, monitoring and surveillance of environmental conditions, and enabling early warning systems to detect and prevent natural calamities like floods and landslides. It can also aid in protecting endangered ecosystems and species from poaching and illegal logging.
1.3.1.4 Autonomous Supply Chains
A fully integrated autonomous supply chain presupposes an automated demand for scoping, ordering, sourcing, packaging, routing, and delivery. This can be accomplished by integrating local and central AI agents into the supply chain to continuously optimize the overall process, for instance, in relation to unforeseen occurrences such as political or social unrest, or natural disasters. It is expected that 6G will enable a fully automated supply chain at rational complexity and cost. Moreover, decreased energy and material consumption, as well as greater resource efficiency, can be achieved through worldwide end-to-end lifecycle tracking of supplies (from goods production, shipping, distribution, and usage to their end-of-life and recycling). In particular, by integrating 6G-connected micro tags on goods, the processes of goods tracking, customs, safety checks, and bookkeeping can be simplified while at the same time, being entirely automated without requiring any kind of human interference.
The successful realization of autonomous supply chains involves using New Radio Reduced Capability (NR-RC) with Network Time Protocols (NTPs) and Narrowband Internet of Things (NB-IoT) and the integration of AI/ML. This will require a global network of integrated systems, incorporating various technologies for unified and cost-effective access, which can orchestrate an efficient end-to-end supply chain. On top of that, this network should be able to adapt locally to any changes using the most suitable connectivity solution. Additionally, the system will track the current state of businesses and factories to forecast when sourcing is required, determine optimal routes for delivery, and provide last-mile delivery. At every stage, the position and condition of the products should be observed to ensure a flexible and adaptable supply chain.
1.3.1.5 Sustainable Food Production
Ending hunger and achieving food security for all, while ensuring sustainable production, comprise a critical aim of the UN SDGs. However, providing remote, rural, and in-sea areas with higher network capacity and performance is a major challenge toward succeeding in this aim. This is necessary for real-time monitoring of micro-locations (soil conditions, microclimate), developing optimized plant treatments, experimenting with various strategies (e.g. spraying strategies, alternate cultivations, removal of plants), and controlling semi-autonomous ground robots. By closely synchronizing digital representations of the physical world, human experts can inspect and experiment with actions in the digital realm to optimize agricultural production and prevent threats. Despite the need for vast amounts of data transfer from underserved areas, close synchronization is essential to address important sustainability, global coverage, inclusion, and opportunity challenges of our time.
1.3.1.6 Network Trade-Offs for Minimized Environmental Impact
Users desire to have the option to use “green” Information and Communications Technology (ICT) services, which have a decreased environmental impact compared to traditional services, in a comprehensive approach. This approach includes considering not just the applications and materials but also the technology and End-to-End (E2E) network design. As environmentally conscious users, they will be encouraged to weigh the potential trade-offs between performance, cost, and environmental impact, allowing them to monitor the overall environmental impact of their products/services. These services aim to mainly tackle the sustainability and global service coverage research challenges.
This service considers the end-to-end energy consumption, taking into account the environmental impact of all aspects of the service, such as the application, network, terminal, and more. These energy-efficient services will require not only energy-optimized networks but also energy-optimized applications, the appropriate upcycling of materials, and other measures. Creating a comprehensive view will necessitate new metrics for environmental impact and the aggregation of these metrics to form a global view.
For instance, regarding video service delivery, users might have the option to choose from a variety of services that have different environmental impacts and trade-offs in terms of video resolution. They can opt for high-resolution videos that require more energy and new terminals or accept a lower-quality resolution with reduced energy consumption. The user’s preferences can determine the video quality they desire and the level of experience they are willing to compromise. The end-to-end environmental impact should be considered, including the user equipment. Standardized indicators for energy consumption and footprint are necessary for each network component to enable the network’s tuning toward the targeted trade-off between Quality of Experience (QoE) and environmental footprint, such as selecting paths powered by renewable electricity or avoiding paths that involve noncircular material usage.
1.3.1.7 Network Functionality for Crisis Resilience
Mobile networks are progressively used for more and more fundamental services such as healthcare and education, and thus, their resilience is becoming a major requirement, particularly in crises or extreme situations. In most cases, the resilience of network operations depends on their power supply, which might be easily disrupted in extreme situations such as natural disasters (e.g. floods or storms). In these circumstances, small diesel generators are likely to be used for backup power generation. Nevertheless, these kinds of generators provide a limited power source before they need refueling, while they are considered far more polluting than large-scale power production and much less efficient. Consequently, minimizing energy consumption during network operation on backup power would be highly advantageous. This can be achieved by rerouting traffic through nodes still powered by the power grid, reducing power-consuming functionality, and focusing on essential functions such as communication or prioritizing first responder calls. To reduce baseline energy consumption, it may also involve sacrificing latency performance by increasing the periodicity of always-on signals. For wireless devices, which rely on battery power and require frequent recharging during emergencies, the network can instruct them to conserve power by disabling certain functions, such as high-bandwidth services or processing heavy applications.
1.3.2 Massive Twinning
In 6G, it is expected that the application of the fundamental concept of Digital Twin (DT) in a wide spectrum of use cases, also referred to as massive twinning, will become progressively important. Massive twinning aims to create a complete digital representation of our environment, not only in manufacturing but also in areas such as logistics, transportation, digital health, social interactions, entertainment, public safety, and defense.
DTs are virtual models that provide real-time representations of physical assets, including their structures, roles, and behaviors. The use of DTs is essential in different fields, such as manufacturing, urban quality of life assessment, increasing productivity, improving sustainability, and transforming sectors such as healthcare and public security. These areas require vast amounts of data transfer, low delays, high reliability, and capacity levels that exceed current limitations.
In all cases, the creation of a fully synchronized and accurate digital representation of the physical and human worlds is crucial. However, it is important to highlight that this will also be resource-intensive, requiring precise representations of the physical world, enhanced means for generating insights and predictions, and ways to experiment with different scenarios. Therefore, it is necessary to ensure that the solutions are sustainable, reliable, and trustworthy, as well as globally applicable. To achieve this, high-resolution and interactive 4D mapping is necessary, along with means to influence the physical world. High-resolution indoor and outdoor mapping will drive use case scenarios of dynamic DTs and virtual worlds in conjunction with real-time, multisensory mapping and rendering, movement prediction, and real-time analytics. The availability of both the virtual and physical worlds will allow for the mapping and analysis of data and the monitoring of systems to predict performance and operational issues, minimizing downtime and improving contextual awareness.
Figure 1.4 Massive twinning 6G use case family.
Source: Maria Papaioannou.
The Massive Twining use case family comprises the following use cases, as also depicted in Figure 1.4: (i) DTs for manufacturing, (ii) immersive smart cities, and (iii) Internet of tags.
1.3.2.1 Digital Twins for Manufacturing
DTs in production/industrial environments will continue to grow, enabling us to exceed the existing levels of production agility while facilitating more effective interaction of production means. This will allow us to incorporate a larger extent of the respective processes, as well as attain the transfer of massive volumes of data and, often, extreme reliability and performance compared to current levels achieved. In the following, some examples of DTs valuable applications in the realm of logistics and production are given:
To ensure accuracy and efficiency, new products must be designed and automatically twinned to their DT. The process involves creating a virtual version of the product and testing it in a digital environment before moving to physical production. Only when the digital version performs according to specifications, physical manufacturing begins. Once manufactured, the physical product is connected to its DT through sensors and actuators, allowing the DT to contain all the relevant information about its physical counterpart.
Another practical application of DTs includes tracking the history of every part of a system (e.g. production line) through “digital threads.” This requires fast and reliable communication at higher capacity levels than what 5G can provide, due to increasing adoption. To increase the chances of commercial success, Ultrareliable Low-Latency Communications (URLLC) is necessary, with high capacity and efficiency. Trustworthiness is also crucial for widespread adoption, which includes performance, dependability, security, and resource efficiency/cost.
The individual in charge of the production facility is experimenting with different scenarios using the DT’s “what-if” feature. These scenarios are aided by real-time communication with the physical world. The operator chooses a specific configuration that is implemented quickly and reliably. In case of an emergency, the DT analyzes the situation using advanced techniques such as reasoning, ML, and prediction methods, as well as conventional polling and alarm functions. The DT then identifies the best course of action and enforces it at the highest possible performance levels with the utmost security.
To ensure that abnormalities in the physical world are efficiently detected and properly mitigated through dynamic adaptations of the production process and/or system reconfigurations, the successful communication and collaboration of multiple DTs in a flexible production process will also be required. This means that the respective DTs need to ensure access to all pertinent communication, production, and sensing data that are essential to take the necessary mitigation actions in case of system abnormalities or to increase efficiency, while also guaranteeing essential requirements of the production processes: privacy, safety, and real time. This use case is also closely related to the “From robots to cobots” use case family, discussed in
Section 1.3.4
.
Using AI, it is expected that 6G will attain ultrareliable and ultramassive connectivity enabling fully automated control of processes, devices, and systems. On top of that, with the incorporation of Virtual Reality (VR)/AR/holographic communications in industrial environments, 6G will also enable remote maintenance. Hence, workers in physical industrial environments can work together timely with experts in remote locations to solve problems, resulting in lower costs and higher efficiency. Another application includes remote control of industrial facilities, which ensures remote machine control. This ensures the safety of the workers while reducing costs and increasing efficiency. Nevertheless, this demands rigorous low-latency, broadband, and reliable transmission of 6G.
1.3.2.2 Immersive Smart Cities
The livability of a city is determined by a complex set of factors, which are weighted and correspond to various areas including education and culture (e.g. access to education and culture for all), environment (e.g. air quality), healthcare, infrastructure (e.g. networks and roads), safety/stability, and others. Managing these factors effectively poses technical challenges but also provides societal and business opportunities. For example, the mapping and planning of smart cities is a potential use case for DT technology. In the future, cities will be dynamic systems with various elements that can be modeled and managed using real-time feedback and an interactive 4D map. This can improve the efficiency and sustainability of city operations, from traffic management to utility control and more. By overlaying physical modeling and historical data, the 4D map can be used to predict and manage behaviors and activities, while human and AI operators can modify and manage tasks and schedules in real time. Ultimately, this optimized management can contribute to the transformation of cities into more sustainable models. For this particular use case, there is one more time the need to transmit large quantities of data within specified timeframes, ranging from ultralow latency for health-related purposes to near-real time. The main objective is to enable actions that will have a positive impact on the city’s functioning and improve its overall livability. Simultaneously, it is crucial to achieve the utmost levels of sustainability while ensuring trustworthiness, which is a fundamental requirement and will be expected by the public.
In the following, some valuable applications in the realm of immersive smart city use cases are given:
Smart Education/Training
: The utilization of 6G wireless systems will bring significant advantages to smart education and training
[1]
. The incorporation of cutting-edge technologies such as holographic communications, five-sense communications, high-quality VR and AR, mobile-edge computing, and AI will contribute to the development of intelligent education and training systems. These advancements will enable students to experience 3D representations of structures and models and even receive instruction from renowned teachers located remotely, resulting in interactive and immersive online education. In terms of training, the application of holography allows learners to observe live processes and interact with objects or trainers. This approach enhances information retention, reduces costs, and eliminates the need for exposure to hazardous environments that are commonly associated with conventional training methods. Additionally, 6G technology enables the creation of intelligent classrooms, where sensors collect data and transmit them to cloud or edge cloud platforms for analysis. The insights obtained can then be utilized to enhance the quality of education and foster improved student interaction.
Super Smart City/Home
: The advanced functionalities of 6G will bring about significant enhancements in quality of life, intelligent monitoring, and automation, thereby expediting the development of highly intelligent cities and homes
[1]
. A city attains the status of being “smart” when it can operate intelligently and autonomously by gathering and analyzing vast amounts of data from various sectors, ranging from urban planning to waste management. This enables more efficient utilization of public resources, improves the quality of services (QoSs) provided to residents, and reduces operational costs for public administrations. The incorporation of smart mobile devices, autonomous vehicles, and similar technologies in 6G will contribute to the intelligence of cities. On the other hand, a smart home is not solely characterized by its connectivity to internet-enabled devices that assist in managing and monitoring appliances and systems via mobile phones. It is also an intelligent entity that possesses instantaneous and distributed decision-making capabilities. Additionally, individuals can control lighting, heating, multimedia entertainment, and more through voice commands or even Brain–Computer Interfaces (BCI), or delegate these tasks to AI systems, which can analyze behavioral patterns to enhance safety and convenience in people’s lives. 6G will play a crucial role in transforming the concept of smart homes into a reality. However, achieving the vision of smart cities and homes poses significant challenges for 6G, particularly in terms of connectivity and coverage capabilities, given the large number of sensors and intelligent devices involved.
1.3.2.3 Internet of Tags
Tags are expected to appear everywhere, enabling numerous capabilities and operations to facilitate daily life. In particular, they will obtain data through the use of label tags and then monitor and influence the environment by employing more advanced tags with not only communication but also sensing and actuation capabilities. The massive deployment and use of tags, as well as energy harvesting, can be extended to various applications, such as tracking merchandise for logistics, monitoring temperature and light to optimize energy consumption, and activating lights and heating with the push of a button. On top of that, this precise monitoring of goods and merchandise will permit stock tracking of the monitored assets and improvement of the general supply chain efficiency, while also being a tool to improve circularity, diminish waste, and improve reuse. To reduce the impact on the environment, the tags will not be powered but will use energy harvested from ambient or renewable sources like Radio Frequency (RF) waves, solar energy, wind, vibration, or mechanical push. Additionally, the use of “zero-environmental-cost” tags, such as those made of bio-degradable materials or printed electronics, can be considered.
Figure 1.5 Telepresence 6G use case family.
Source: Maria Papaioannou.
1.3.3 Telepresence
Telepresence refers to the “human experience of being fully present at a live real-world location anytime and anywhere, remote from one’s own physical location,” 2 using all five senses if so desired. It enables enhanced human interactions not only with each other but also with any physical and/or digital object in the physical and digital worlds, respectively. On top of that, telepresence envisions the use of all five human senses to exchange sensory information, as well as extend the current capabilities of these senses. Nevertheless, in order for telepresence to effectively come to realization, there are several research challenges that need to be met. In particular, to provide a seamless telepresence experience, high data rates are required, and low latency and reliability are necessary to avoid negative effects such as incomplete experience or discomfort (e.g. nausea). The basis for this connectivity is a network of networks, while connected intelligence can enhance performance. Again, in this use case family, sustainability and trustworthiness are critical considerations. Telepresence should be available globally and delivered sustainably, which can contribute to meeting the UN SDGs by reducing the need for travel.
The telepresence use case family comprises the following use cases, as also depicted in Figure 1.5: (i) fully merged cyber–physical worlds, (ii) mixed reality co-design, (iii) immersive sport event, and (iv) merged reality game/work.
1.3.3.1 Fully Merged Cyber–Physical Worlds
The use of Mixed Reality (MR) and holographic telepresence will become widespread for both professional and personal communication. With holographic telepresence, one can appear to be in a particular location while in reality being somewhere else, such as appearing to be in the office while actually being in the car. This technology can facilitate collaboration and remote working for white-collar workers, enhance diagnosis during teleconsultations, improve teacher-student interactions in e-learning classes, and enable virtual travel and telepresence meetings with friends and family. The user will experience the world where their hologram is, with a highly sophisticated sensory experience, synchronized to devices on their body. Users desire high-quality communication with distant individuals, with realistic interaction and perception of body language, surrounding sounds, expressions, intonation, and other senses, including touch.
Through MR telepresence, one can
