6G Urban Innovation
Description
This book presents the 6G powered integration of Artificial Intelligence (AI) and Digital Twin (DT) technology for sustainable smart cities. In the context of smart cities, 6G, AI and DT hold enormous potential for transformation by boosting city infrastructure and planning, streamlining healthcare facilities, and improving transportation. 6G offers high speed and low latency seamless transfer of vast amounts of data which, when analyzed with sophisticated AI models, enhance the decision-making capabilities for smart city infrastructure and urban planning. DT technology, through continuous monitoring and virtual modeling of urban ecosystems, enables predictive maintenance for energy distribution, water management and waste management in a smart city landscape for environmental sustainability.
6G Urban Innovation covers the 6G technological innovations, trends and concerns, as well as practical challenges encountered in the implementation of AI and DT for transforming smart cities for a sustainable future.
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Preface xiii
Ashu TANEJA, Abhishek KUMAR, Suresh Vishnudas LIMKAR, Mariya OUAISSA and Mariyam OUAISSA
Chapter 1 AI-Enabled Energy Management in Mobile Wireless Sensor Network for 6G Internet-of-Things (IoT) 1
Bhanu Partap SINGH, Lav SONI and Ashu TANEJA
1.1 Introduction 1
1.2 6G IoT: a new frontier for MWSNs 2
1.3 Energy management challenges in 6G IoT MWSNs 6
1.4 AI and machine learning for energy management in 6G IoT MWSNs 9
1.5 Cutting-edge research and emerging trends 14
1.6 AI-driven energy harvesting and wireless power transfer 17
1.7 Conclusion 20
1.8 References 20
Chapter 2 Security Solutions for Smart Cities Using Digital Twin 25
Sulakshana MALWADE, SHIKHA, Mandeep KAUR, Vibhuti REHALIA, Kimmi VERMA, Anupma GUPTA and Mohammad Alamgir HOSSAIN
2.1 Introduction 25
2.2 Understanding digital twin technology 27
2.2.1 Definition and basic principles of digital twins 27
2.2.2 Components and architecture of a digital twin system 28
2.2.3 Applications of digital twins in various industries 29
2.3 Security challenges in smart cities 31
2.3.1 Overview of security threats in smart cities 31
2.3.2 Cybersecurity risks in connected infrastructure 31
2.3.3 Privacy concerns in data collection and processing 32
2.4 Integrating digital twin for security solutions 33
2.4.1 Using digital twin for real-time monitoring of infrastructure 33
2.4.2 Enhancing situational awareness through digital replicas 33
2.5 Applications of digital twin in smart city security 34
2.5.1 Intelligent traffic management and accident prevention 34
2.5.2 Surveillance and public safety enhancement 35
2.5.3 Critical infrastructure protection 35
2.6 Privacy and ethical considerations 36
2.6.1 Data privacy issues with digital twin data collection 36
2.6.2 Ethical challenges in implementing surveillance systems 36
2.6.3 Strategies for securing personal and sensitive data 37
2.7 Technological challenges and solutions 37
2.7.1 Integration of diverse technologies 37
2.7.2 Data interoperability and standardization 38
2.7.3 Scalability of digital twin systems for large cities 39
2.8 Results and discussion 39
2.9 Conclusion 44
2.10 References 44
Chapter 3 Security Solutions for Smart Cities Using Digital Twin: A DevOps Approach in the Era of 6G Powered Ultra-Smart Cities 47
C.V. Suresh BABU and Logapadmini B.
3.1 Introduction 47
3.1.1 Overview of digital twin technology 47
3.1.2 The role of 6G in ultra-smart cities 48
3.1.3 The need for security solutions in smart cities 48
3.1.4 The intersection of DevOps and digital twin for security 48
3.2 Background information 49
3.2.1 Evolution of smart cities and digital twins 49
3.2.2 Issues of smart cities 49
3.2.3 Relevance of DevOps in the fighting of security requirements 50
3.3 Relevance to the edited book's theme 50
3.3.1 Convergence of AI, digital twin and DevOps 50
3.3.2 Contribution to sustainable ultra-smart cities 51
3.4 Key questions or problems addressed 51
3.4.1 Integration of digital twins for security applications 51
3.4.2 Role of DevOps in ensuring continuous security updates 52
3.4.3 Addressing security gaps in 6G-enabled environments 52
3.5 Objectives and scope 53
3.5.1 Objectives of the chapter 53
3.5.2 Delimitation of scope 53
3.6 Literature review 55
3.6.1 Summary of existing research 55
3.6.2 Identified gaps in literature 55
3.6.3 The contribution of this chapter 56
3.7 Methodology 56
3.7.1 Software development methodologies 56
3.7.2 Justification of methodology 58
3.8 Discussion of analysis and findings 58
3.8.1 Case studies on digital twin security in smart cities 58
3.8.2 Analysis of DevOps in smart city infrastructure 59
3.9 Suggestions and recommendations 59
3.9.1 Best practices for implementing security solutions 59
3.9.2 Role of standardized protocols in 6G and digital twins 60
3.10 Future scope for research 60
3.10.1 Quantum computing for digital twin security 60
3.10.2 Digital twin ethical challenges for smart cities 61
3.11 Conclusion 61
3.11.1 Summary of key contributions 61
3.11.2 Final thoughts on digital twin and DevOps integration 61
3.12 References 62
Chapter 4 Convergence of Twin Technology with AI for Secure 6G Communication 65
Srinibas PATTANAIK, Jasneet KAUR, Sachin AHUJA, Sartajvir Singh DHILLON and Alessandro VINCIARELLI
4.1 Introduction 65
4.1.1 Significance and relevance of privacy and security in 6G networks 66
4.2 Comprehension of twins' technology 67
4.3 Application of twin technology with AI network security 69
4.4 Network analysis and intelligence of prediction 69
4.5 AI 6G communication infrastructure and security constraints 69
4.6 Development and implement for AI-twin connectivity 70
4.7 Security and privacy in the AI 6G technology 72
4.8 Conclusion 74
4.9 References 75
Chapter 5. AI-driven digital Twin Framework for Securing 6G Networks: Overarching Challenges and the Way Forward 77
Pasham SOWMYA, T. Monika SINGH and C. Kishor Kumar REDDY
5.1 Introduction 77
5.1.1 Understanding digital twin technology 78
5.1.2 Role of AI in digital twin technology 79
5.1.3 Importance of security in 6G networks 80
5.2 Evolution of digital twin technology in telecommunications 80
5.2.1 From 4G to 5G: the transition to digital twins 80
5.2.2 Key advancements leading to 6G 81
5.2.3 Convergence of AI, IoT and digital twins 82
5.3 Architectural foundations of AI-driven digital twins 82
5.3.1 Components of a digital twin framework 82
5.3.2 Integration of AI, ML and big data analytics 83
5.3.3 Real-time data processing and predictive modeling 83
5.4 Security challenges in 6G networks 84
5.4.1 Threat landscape in 6G communication systems 84
5.4.2 Cybersecurity risks and vulnerabilities 85
5.4.3 Privacy and data protection concerns 86
5.5 Role of AI in securing 6G networks 87
5.5.1 AI-based threat detection and mitigation 87
5.5.2 Anomaly detection using machine learning 87
5.5.3 Predictive security models for proactive defense 89
5.6 Digital twin framework for 6G security 89
5.6.1 Real-time network monitoring and simulation 89
5.6.2 AI-powered attack prevention and response 89
5.6.3 Adaptive security policies and autonomous decision-making 90
5.7 Ethical and privacy considerations 91
5.7.1 Ethical use of AI in 6G security 91
5.7.2 Addressing bias, transparency and accountability 91
5.7.3 Privacy-preserving AI models 92
5.8 Potential benefits of AI-driven digital twins in 6G 92
5.8.1 Enhanced network performance and reliability 92
5.8.2 Proactive threat prevention and mitigation 92
5.8.3 Cost reduction and operational efficiency 93
5.9 Challenges and risks in implementing AI-driven digital twins 93
5.9.1 Computational and resource constraints 93
5.9.2 Data integrity and reliability issues 94
5.9.3 Regulatory and compliance barriers 95
5.10 Future directions and innovations 95
5.10.1 Emerging trends in digital twin security for 6G 95
5.10.2 AI-enhanced autonomous network defense mechanisms 96
5.10.3 Policy recommendations and global collaboration 97
5.11 References 98
Chapter 6 Harnessing Artificial Intelligence and Digital Twin Technologies for Sustainable Agripreneurship: A Path Toward Smart Agriculture 107
A. IYAPPAN, G. ILANKUMARAN and Tripuraneni JAGGAIAH
6.1 Introduction 107
6.1.1 The call for sustainable agripreneurship in India 109
6.2 Role of AI and Digital Twin in sustainable agripreneurship 110
6.2.1 Precision farming and resource management 110
6.2.2 Enhanced crop monitoring and predictive analytics 111
6.2.3 Smart irrigation and water management 112
6.2.4 Supply chain optimization 113
6.2.5 Risk management and climate adaptation 115
6.3 Challenges in implementing AI and digital twins for sustainability 116
6.3.1 Data availability and quality 116
6.3.2 Scalability 116
6.3.3 Cybersecurity risks 117
6.3.4 High implementation costs 117
6.3.5 Skill gaps 117
6.3.6 Regulatory and ethical concerns 117
6.3.7 Environmental impact of technology 118
6.4 Overcoming the challenges 118
6.5 Practical applications of harnessing artificial intelligence and digital twin technologies for sustainable agripreneurship 119
6.5.1 A path toward smart agriculture 119
6.6 Conclusion 120
6.7 References 120
Chapter 7 Role of AI and Digital Twin for Smart Transportation 123
Ritesh Gangasingh BAIS, Vipan KUMAR, Manjushri JOSHI, Jainender SHARMA,Pradnya BORKAR and Mohammad Alamgir HOSSAIN
7.1 Introduction 123
7.1.1 Background and current challenges in urban transportation 124
7.1.2 Overview of AI and DT technologies 125
7.2 Related work 125
7.3 Foundational concepts 127
7.3.1 Defining AI in the context of smart transportation 127
7.3.2 Introduction to DTs: concept and components 127
7.3.3 Integration of AI and DT in transportation systems 128
7.4 AI-driven technologies in transportation 129
7.4.1 ML models for traffic prediction and management 129
7.4.2 AI in vehicle autonomy and route optimization 129
7.4.3 Real-time data processing and decision support systems 130
7.5 DT implementation in transportation 131
7.5.1 Architectures and models of DTs for urban mobility 131
7.5.2 Challenges and solutions in digital twinning of transport infrastructure 132
7.6 Data analysis and results 133
7.6.1 Data collection methods and sources 134
7.6.2 Analysis techniques: from descriptive to predictive 135
7.6.3 Presentation of results: case studies and model outputs 137
7.7 Integrating AI and DT for enhanced mobility 138
7.7.1 Synergistic effects of AI and DT on transportation efficiency 138
7.7.2 Future directions: AI and DT in smart city frameworks 139
7.8 Conclusion 140
7.9 References 140
Chapter 8 6G-enabled Digital Twin for Smart Transportation 143
Prashant WAKHARE, Pritesh PATIL, Anuradha Amar BAKARE, Rajshri NIKAM, Vipan KUMAR, Yamini SOOD and Utku KOSE
8.1 Introduction 144
8.1.1 Overview of 6G technology 144
8.1.2 The concept of digital twins 145
8.2 Literature review 146
8.2.1 5G in smart transportation 146
8.2.2 Digital twin technology and its advancements 147
8.2.3 Digital twins in urban infrastructure 147
8.3 6G technology overview 149
8.3.1 Key features of 6G networks 149
8.3.2 Network architecture and communication protocols 150
8.3.3 Enhanced capabilities: speed, latency and reliability 151
8.3.4 Role of AI, ML and IoT in 6G for transportation 152
8.4 Digital twin concept for smart transportation 154
8.4.1 Definition and components of a digital twin 154
8.4.2 Application of digital twins in transportation systems 155
8.4.3 Real-time monitoring and simulation in transportation networks 155
8.5 Result and discussion 156
8.6 Conclusion 160
8.7 References 161
Chapter 9 Digital Twins Supercharges Efficiency-Unlocking the Power of Industry 5.0 163
T. Shirley DEVAKIRUBAI
9.1 Introduction 163
9.2 Review of literature 165
9.3 Research questions 166
9.4 VR and AR prototyping and innovation in Industry 5.0 166
9.5 VR and AR technologies in product customization and personalization 168
9.6 VR and AR technologies changing training and development 170
9.7 Conclusion 171
9.8 References 172
Chapter 10 Role of AI and Digital Twin in Industry 5.0 179
Harashleen KOUR, Shubham GUPTA and Osamah Ibrahim KHALAF
10.1 Introduction 179
10.2 Importance of AI and DT in Industry 5.0 181
10.3 Fundamentals of AI and DT in Industry 5.0 181
10.3.1 AI in Industry 5.0 181
10.3.2 DT technology in Industry 5.0 182
10.3.3 Integration of AI with DT 185
10.4 Applications of AI and DT in Industry 5.0 187
10.4.1 Smart manufacturing 187
10.4.2 Personalized and adaptive production 188
10.4.3 Energy optimization and sustainability 189
10.4.4 Worker safety and human-machine collaboration 189
10.5 Enabling technologies for AI and DT in Industry 5.0 190
10.5.1 IoT for data acquisition 190
10.5.2 Edge computing and cloud computing 191
10.5.3 Advanced communication technologies (5G/6G) 191
10.5.4 Cybersecurity for AI-DT systems 192
10.6 Challenges and limitations 193
10.6.1 Technical challenges 193
10.6.2 Data privacy and security concerns 194
10.6.3 Cost and implementation barriers 194
10.6.4 Ethical considerations 194
10.7 Future trends and research directions 195
10.7.1 AI and DT for hyper-personalization 195
10.7.2 Autonomous and decentralized manufacturing ecosystems 196
10.7.3 Integration with emerging technologies 196
10.7.4 Evolving standards and frameworks 196
10.8 Case studies and real-world implementations 197
10.8.1 Automotive manufacturing 197
10.8.2 Smart factories 198
10.8.3 Sustainable industries 199
10.9 Conclusion 199
10.10 References 200
List of Authors 203
Index 207
1
AI-Enabled Energy Management in Mobile Wireless Sensor Network for 6G Internet-of-Things (IoT)
Mobile wireless sensor networks (MWSNs) within the Internet-of-Things (IoT) are undergoing a significant transformation with the advent of sixth generation (6G) technology, which prioritizes low-latency, high-speed communication, and efficient energy management. This chapter explores the artificial intelligence (AI)-driven strategies that enhance the performance of 6G-enabled MWSNs while optimizing energy consumption and extending network lifespan. The roles of federated learning, deep learning and reinforcement learning are highlighted in addressing energy efficiency challenges. Additionally, innovative approaches for intelligent and sustainable energy management in next-generation wireless networks are introduced, leveraging neuromorphic computing and quantum-inspired algorithms to achieve smarter and more eco-friendly operations. The integration of edge computing and blockchain further strengthens security and data privacy while minimizing energy overhead. Future advancements in 6G MWSNs will continue to rely on AI-driven optimizations to ensure seamless, scalable, and energy-efficient network operations.
1.1. Introduction
Wireless communication and the Internet-of-Things (IoT) are witnessing rapid transformation with the advent of sixth generation (6G) technologies (Mao et al. 2020). The function of mobile wireless sensor networks (MWSNs) becomes ever more important as we move toward this next generation of connection. Comprising many tiny, independent sensors, these networks may gather and broadcast data in many scenarios. However, considering the mobility nature of these sensors and the low-latency, high-speed requirements of 6G networks, energy management becomes somewhat challenging (Maduranga et al. 2024).
This chapter delves into the challenging field of artificial intelligence (AI)-enabled energy management in MWSNs for applications in 6G IoT (Sodhro et al. 2020; Sefati et al. 2024), exploring how the use of AI and machine learning (ML) could provide methods for optimizing lifetime within such networks and reducing the levels of energy usage and increasing performance in these highly demanding systems.
By the end of this chapter, readers will be fully aware of the existing level of the art, challenges and potential routes in this crucial area of research (Alhammadi et al. 2024).
Table 1.1. Evolution of wireless sensor networks (WSNs)
Generation Key features Energy management approach 1st Gen Static nodes, limited resources Fixed duty cycling 2nd Gen Improved hardware, longer range Adaptive duty cycling 3rd Gen Mobile nodes, dynamic topology Mobility-aware protocols 4th Gen (5G) High speed, low latency AI-assisted optimization 5th Gen (6G) Ultra-high speed, massive connectivity Advanced AI/ML techniquesTable 1.1 shows WSN development throughout many generations, therefore stressing the evolving methods of energy management. The complexity of energy management rises as we move toward 6G networks, which calls for the acceptance of powerful AI and ML methods (Manogaran et al. 2021).
1.2. 6G IoT: a new frontier for MWSNs
With unheard-of speeds, ultra-low latency and huge device density, 6G technology promises to bring in a new age of communication. The IoT will be much changed by these developments, allowing a large spectrum of fresh uses and services. In this regard, MWSNs are likely to be rather important in achieving 6G IoT's full possibilities (Zhu et al. 2023).
Expectations for 6G networks include data speeds of up to 1 Tbps, latency as low as 1 microsecond and capacity for up to ten million devices per square kilometer. Among the many fresh uses these features will allow are brain-computer connections, tactile Internet and holographic communications. As the eyes and ears of the 6G IoT ecosystem, MWSNs will be indispensable in gathering the enormous volumes of data needed to enable these uses (Dubey et al. 2023; Abbas et al. 2024).
However, the higher performance standards of 6G networks also provide major difficulties for MWSNs, especially with relation to energy usage. 6G applications' high data rates and low latency will demand sensor nodes to process and transmit data at hitherto unheard-of rates, hence possibly fast depleting energy resources. Moreover, the great device density allowed by 6G networks will lead to higher network complexity, which emphasizes even more effective energy management (Guo et al. 2021).
Figure 1.1 shows the projected growth in IoT devices and the corresponding increase in data generation until 2030.
Figure 1.1. Projected growth of IoT devices and data generation.
This graph shows the predicted exponential expansion in data produced as well as IoT device count. Over 75 billion IoT devices are expected to be running by 2030, producing roughly 600 zettabytes of data yearly (Verma et al. 2020). This enormous scale emphasizes how urgently effective energy management in MWSNs supports the 6G IoT ecosystem (Gera et al. 2023).
The value of MWSNs to 6G IoT transcends simple data collection and dissemination. Difficult tasks such as autonomous decision-making, edge computing and cooperative sensing will fall to these systems. For smart city applications tracking public safety, air quality and traffic flow, MWSNs might be used, for instance. Apart from data collecting, the sensor nodes would localize it, make real-time decisions, and coordinate with other nodes to optimize city operations.
In healthcare environments, MWSNs might be used for remote treatment as well as for ongoing patient monitoring in hospitals (Singh et al. 2024). Wearable sensors would gather vital signs and other health-related data, analyze it at the edge and forward only pertinent information to medical professionals. This method would need advanced energy management to guarantee long-term functioning of the wearable gadgets while preserving the great dependability expected by medical uses (Lv et al. 2021).
Environmental monitoring is another area where MWSNs will be very vital in the 6G IoT ecosystem. Sensor nodes placed in polar areas, seas or forests would have to run for long stretches of time free from human interference. These nodes would gather information on pollution levels, animal migrations or climate change; so, strong energy management techniques would be necessary to live in demanding surroundings and maintain long-term data collecting capacity (Logeshwaran et al. 2024).
Integration of MWSNs with other developing technologies will increase their possibilities in the environment of the 6G IoT. For dynamic 3D sensing and network reconfiguration, for instance, the mix of MWSNs with unmanned aerial vehicles (UAVs) enables their use as mobile base stations or data mules, UAVs gather data from ground-based sensors and convey it to central processing units (Dabas et al. 2024). This hybrid method would provide new dimensions to energy management and call for plans considering the energy limitations of ground sensors and flying vehicles.
Deeper into the 6G era, the distinction between sensing and communication will keep blurring. Ideas such as ambient backscatter communications and combined radar-communication systems will become increasingly frequent (Chen and Okada 2020). Under these conditions, MWSNs will not only detect the surroundings but also use the same signals for communication, thus possibly improving the energy-efficient operations. Realizing these advantages, nevertheless, will depend on advanced energy management strategies able to accommodate the dual character of these systems (Chen et al. 2020).
Furthermore, allowing new paradigms in distributed intelligence and collaborative sensing will be the great connection promised by 6G networks. Large-scale sensing systems will be built on MWSNs, where hundreds or even millions of nodes cooperate to accomplish challenging sensing missions (Xu 2021). By spreading the burden across many nodes, this cooperative strategy might perhaps result in more energy-efficient activities. It does, however, also create fresh difficulties with regard to network coordination and energy balance (Gong et al. 2022).
Examining the following breakdown of energy consumption in a standard MWSN node shown in Figure 1.2 helps us better grasp the energy consumption trends in 6G IoT applications.
Figure 1.2. Energy consumption distribution in a 6G IoT MWSN node.
With 40% of the total energy used, Figure 1.2 shows that for MWSN nodes, communication is still the most energy-intensive activity. Sensing and movement, each using 20% of the energy, come next (Letaief et al. 2021). Processing contributes for 15%; the node uses only 5% of its energy in sleep mode. These ratios demonstrate the need for energy management strategies, emphasizing increasing mobility and communication while also improving the efficiency of sensing and processing operations (Kamruzzaman...
