6G Urban Innovation

Ashu Taneja is Associate Professor at the Centre for Research Impact and Outcome (CRIO), Chitkara University, India.Abhishek Kumar is a Senior Member of IEEE and works as Assistant Director and Professor in the Computer Science & Engineering Department at Chandigarh University, India.Suresh Vishnudas Limkar is Assistant Professor at the Department of Computer Science and Engineering at the Central University of Jammu, India.Mariya Ouaissa is Professor of Cybersecurity and Networks at the Faculty of Sciences Semlalia, Cadi Ayyad University, Morocco.Mariyam Ouaissa is Assistant Professor of Networks and Systems at ENSA, Chouaib Doukkali University, Morocco.

• Preface xiiiAshu TANEJA, Abhishek KUMAR, Suresh Vishnudas LIMKAR, Mariya OUAISSA and Mariyam OUAISSAChapter 1 AI-Enabled Energy Management in Mobile Wireless Sensor Network for 6G Internet-of-Things (IoT) 1Bhanu Partap SINGH, Lav SONI and Ashu TANEJA1.1 Introduction 11.2 6G IoT: a new frontier for MWSNs 21.3 Energy management challenges in 6G IoT MWSNs 61.4 AI and machine learning for energy management in 6G IoT MWSNs 91.5 Cutting-edge research and emerging trends 141.6 AI-driven energy harvesting and wireless power transfer 171.7 Conclusion 201.8 References 20Chapter 2 Security Solutions for Smart Cities Using Digital Twin 25Sulakshana MALWADE, SHIKHA, Mandeep KAUR, Vibhuti REHALIA, Kimmi VERMA, Anupma GUPTA and Mohammad Alamgir HOSSAIN2.1 Introduction 252.2 Understanding digital twin technology 272.2.1 Definition and basic principles of digital twins 272.2.2 Components and architecture of a digital twin system 282.2.3 Applications of digital twins in various industries 292.3 Security challenges in smart cities 312.3.1 Overview of security threats in smart cities 312.3.2 Cybersecurity risks in connected infrastructure 312.3.3 Privacy concerns in data collection and processing 322.4 Integrating digital twin for security solutions 332.4.1 Using digital twin for real-time monitoring of infrastructure 332.4.2 Enhancing situational awareness through digital replicas 332.5 Applications of digital twin in smart city security 342.5.1 Intelligent traffic management and accident prevention 342.5.2 Surveillance and public safety enhancement 352.5.3 Critical infrastructure protection 352.6 Privacy and ethical considerations 362.6.1 Data privacy issues with digital twin data collection 362.6.2 Ethical challenges in implementing surveillance systems 362.6.3 Strategies for securing personal and sensitive data 372.7 Technological challenges and solutions 372.7.1 Integration of diverse technologies 372.7.2 Data interoperability and standardization 382.7.3 Scalability of digital twin systems for large cities 392.8 Results and discussion 392.9 Conclusion 442.10 References 44Chapter 3 Security Solutions for Smart Cities Using Digital Twin: A DevOps Approach in the Era of 6G Powered Ultra-Smart Cities 47C.V. Suresh BABU and Logapadmini B.3.1 Introduction 473.1.1 Overview of digital twin technology 473.1.2 The role of 6G in ultra-smart cities 483.1.3 The need for security solutions in smart cities 483.1.4 The intersection of DevOps and digital twin for security 483.2 Background information 493.2.1 Evolution of smart cities and digital twins 493.2.2 Issues of smart cities 493.2.3 Relevance of DevOps in the fighting of security requirements 503.3 Relevance to the edited book’s theme 503.3.1 Convergence of AI, digital twin and DevOps 503.3.2 Contribution to sustainable ultra-smart cities 513.4 Key questions or problems addressed 513.4.1 Integration of digital twins for security applications 513.4.2 Role of DevOps in ensuring continuous security updates 523.4.3 Addressing security gaps in 6G-enabled environments 523.5 Objectives and scope 533.5.1 Objectives of the chapter 533.5.2 Delimitation of scope 533.6 Literature review 553.6.1 Summary of existing research 553.6.2 Identified gaps in literature 553.6.3 The contribution of this chapter 563.7 Methodology 563.7.1 Software development methodologies 563.7.2 Justification of methodology 583.8 Discussion of analysis and findings 583.8.1 Case studies on digital twin security in smart cities 583.8.2 Analysis of DevOps in smart city infrastructure 593.9 Suggestions and recommendations 593.9.1 Best practices for implementing security solutions 593.9.2 Role of standardized protocols in 6G and digital twins 603.10 Future scope for research 603.10.1 Quantum computing for digital twin security 603.10.2 Digital twin ethical challenges for smart cities 613.11 Conclusion 613.11.1 Summary of key contributions 613.11.2 Final thoughts on digital twin and DevOps integration 613.12 References 62Chapter 4 Convergence of Twin Technology with AI for Secure 6G Communication 65Srinibas PATTANAIK, Jasneet KAUR, Sachin AHUJA, Sartajvir Singh DHILLON and Alessandro VINCIARELLI4.1 Introduction 654.1.1 Significance and relevance of privacy and security in 6G networks 664.2 Comprehension of twins’ technology 674.3 Application of twin technology with AI network security 694.4 Network analysis and intelligence of prediction 694.5 AI 6G communication infrastructure and security constraints 694.6 Development and implement for AI-twin connectivity 704.7 Security and privacy in the AI 6G technology 724.8 Conclusion 744.9 References 75Chapter 5. AI-driven digital Twin Framework for Securing 6G Networks: Overarching Challenges and the Way Forward 77Pasham SOWMYA, T. Monika SINGH and C. Kishor Kumar REDDY5.1 Introduction 775.1.1 Understanding digital twin technology 785.1.2 Role of AI in digital twin technology 795.1.3 Importance of security in 6G networks 805.2 Evolution of digital twin technology in telecommunications 805.2.1 From 4G to 5G: the transition to digital twins 805.2.2 Key advancements leading to 6G 815.2.3 Convergence of AI, IoT and digital twins 825.3 Architectural foundations of AI-driven digital twins 825.3.1 Components of a digital twin framework 825.3.2 Integration of AI, ML and big data analytics 835.3.3 Real-time data processing and predictive modeling 835.4 Security challenges in 6G networks 845.4.1 Threat landscape in 6G communication systems 845.4.2 Cybersecurity risks and vulnerabilities 855.4.3 Privacy and data protection concerns 865.5 Role of AI in securing 6G networks 875.5.1 AI-based threat detection and mitigation 875.5.2 Anomaly detection using machine learning 875.5.3 Predictive security models for proactive defense 895.6 Digital twin framework for 6G security 895.6.1 Real-time network monitoring and simulation 895.6.2 AI-powered attack prevention and response 895.6.3 Adaptive security policies and autonomous decision-making 905.7 Ethical and privacy considerations 915.7.1 Ethical use of AI in 6G security 915.7.2 Addressing bias, transparency and accountability 915.7.3 Privacy-preserving AI models 925.8 Potential benefits of AI-driven digital twins in 6G 925.8.1 Enhanced network performance and reliability 925.8.2 Proactive threat prevention and mitigation 925.8.3 Cost reduction and operational efficiency 935.9 Challenges and risks in implementing AI-driven digital twins 935.9.1 Computational and resource constraints 935.9.2 Data integrity and reliability issues 945.9.3 Regulatory and compliance barriers 955.10 Future directions and innovations 955.10.1 Emerging trends in digital twin security for 6G 955.10.2 AI-enhanced autonomous network defense mechanisms 965.10.3 Policy recommendations and global collaboration 975.11 References 98Chapter 6 Harnessing Artificial Intelligence and Digital Twin Technologies for Sustainable Agripreneurship: A Path Toward Smart Agriculture 107A. IYAPPAN, G. ILANKUMARAN and Tripuraneni JAGGAIAH6.1 Introduction 1076.1.1 The call for sustainable agripreneurship in India 1096.2 Role of AI and Digital Twin in sustainable agripreneurship 1106.2.1 Precision farming and resource management 1106.2.2 Enhanced crop monitoring and predictive analytics 1116.2.3 Smart irrigation and water management 1126.2.4 Supply chain optimization 1136.2.5 Risk management and climate adaptation 1156.3 Challenges in implementing AI and digital twins for sustainability 1166.3.1 Data availability and quality 1166.3.2 Scalability 1166.3.3 Cybersecurity risks 1176.3.4 High implementation costs 1176.3.5 Skill gaps 1176.3.6 Regulatory and ethical concerns 1176.3.7 Environmental impact of technology 1186.4 Overcoming the challenges 1186.5 Practical applications of harnessing artificial intelligence and digital twin technologies for sustainable agripreneurship 1196.5.1 A path toward smart agriculture 1196.6 Conclusion 1206.7 References 120Chapter 7 Role of AI and Digital Twin for Smart Transportation 123Ritesh Gangasingh BAIS, Vipan KUMAR, Manjushri JOSHI, Jainender SHARMA,Pradnya BORKAR and Mohammad Alamgir HOSSAIN7.1 Introduction 1237.1.1 Background and current challenges in urban transportation 1247.1.2 Overview of AI and DT technologies 1257.2 Related work 1257.3 Foundational concepts 1277.3.1 Defining AI in the context of smart transportation 1277.3.2 Introduction to DTs: concept and components 1277.3.3 Integration of AI and DT in transportation systems 1287.4 AI-driven technologies in transportation 1297.4.1 ML models for traffic prediction and management 1297.4.2 AI in vehicle autonomy and route optimization 1297.4.3 Real-time data processing and decision support systems 1307.5 DT implementation in transportation 1317.5.1 Architectures and models of DTs for urban mobility 1317.5.2 Challenges and solutions in digital twinning of transport infrastructure 1327.6 Data analysis and results 1337.6.1 Data collection methods and sources 1347.6.2 Analysis techniques: from descriptive to predictive 1357.6.3 Presentation of results: case studies and model outputs 1377.7 Integrating AI and DT for enhanced mobility 1387.7.1 Synergistic effects of AI and DT on transportation efficiency 1387.7.2 Future directions: AI and DT in smart city frameworks 1397.8 Conclusion 1407.9 References 140Chapter 8 6G-enabled Digital Twin for Smart Transportation 143Prashant WAKHARE, Pritesh PATIL, Anuradha Amar BAKARE, Rajshri NIKAM, Vipan KUMAR, Yamini SOOD and Utku KOSE8.1 Introduction 1448.1.1 Overview of 6G technology 1448.1.2 The concept of digital twins 1458.2 Literature review 1468.2.1 5G in smart transportation 1468.2.2 Digital twin technology and its advancements 1478.2.3 Digital twins in urban infrastructure 1478.3 6G technology overview 1498.3.1 Key features of 6G networks 1498.3.2 Network architecture and communication protocols 1508.3.3 Enhanced capabilities: speed, latency and reliability 1518.3.4 Role of AI, ML and IoT in 6G for transportation 1528.4 Digital twin concept for smart transportation 1548.4.1 Definition and components of a digital twin 1548.4.2 Application of digital twins in transportation systems 1558.4.3 Real-time monitoring and simulation in transportation networks 1558.5 Result and discussion 1568.6 Conclusion 1608.7 References 161Chapter 9 Digital Twins Supercharges Efficiency-Unlocking the Power of Industry 5.0 163T. Shirley DEVAKIRUBAI9.1 Introduction 1639.2 Review of literature 1659.3 Research questions 1669.4 VR and AR prototyping and innovation in Industry 5.0 1669.5 VR and AR technologies in product customization and personalization 1689.6 VR and AR technologies changing training and development 1709.7 Conclusion 1719.8 References 172Chapter 10 Role of AI and Digital Twin in Industry 5.0 179Harashleen KOUR, Shubham GUPTA and Osamah Ibrahim KHALAF10.1 Introduction 17910.2 Importance of AI and DT in Industry 5.0 18110.3 Fundamentals of AI and DT in Industry 5.0 18110.3.1 AI in Industry 5.0 18110.3.2 DT technology in Industry 5.0 18210.3.3 Integration of AI with DT 18510.4 Applications of AI and DT in Industry 5.0 18710.4.1 Smart manufacturing 18710.4.2 Personalized and adaptive production 18810.4.3 Energy optimization and sustainability 18910.4.4 Worker safety and human–machine collaboration 18910.5 Enabling technologies for AI and DT in Industry 5.0 19010.5.1 IoT for data acquisition 19010.5.2 Edge computing and cloud computing 19110.5.3 Advanced communication technologies (5G/6G) 19110.5.4 Cybersecurity for AI-DT systems 19210.6 Challenges and limitations 19310.6.1 Technical challenges 19310.6.2 Data privacy and security concerns 19410.6.3 Cost and implementation barriers 19410.6.4 Ethical considerations 19410.7 Future trends and research directions 19510.7.1 AI and DT for hyper-personalization 19510.7.2 Autonomous and decentralized manufacturing ecosystems 19610.7.3 Integration with emerging technologies 19610.7.4 Evolving standards and frameworks 19610.8 Case studies and real-world implementations 19710.8.1 Automotive manufacturing 19710.8.2 Smart factories 19810.8.3 Sustainable industries 19910.9 Conclusion 19910.10 References 200List of Authors 203Index 207