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AI-Driven Spatial Computing

Concepts, Challenges, and Use Cases

Edited by Arun Kumar, Aziz Nanthaamornphong, Nishant Gaur, and S. Balamurugan
Series: Leading-Edge Breakthroughs in Artificial Intelligence
Copyright: 2026   |   Expected Pub Date:2026/11/30
ISBN: 9781394422524  |  Hardcover  |  
424 pages

One Line Description
Discover how the fusion of real-time adaptive AI and spatial computing is redefining Industry 5.0, giving you the exact technical frameworks and actionable strategies needed to lead the next era of human-machine innovation.

Audience
Engineers, research scholars and students, IT professionals, network administrators, artificial intelligence and deep learning experts, and government research agencies looking to the future AI applications.

Description
Industries are increasingly leveraging automation and cutting-edge innovations to streamline operations and meet evolving demands. Today, Industry 5.0 emphasizes collaboration between humans and machines, enabling greater creativity, customization, and innovation. This volume explores a significant advancement in artificial intelligence: adaptive, AI-driven systems capable of learning and evolving in real time. Unlike traditional AI, which relies on static data and predefined models, adaptive AI responds dynamically to changing conditions, making it especially valuable in complex, unpredictable environments. This book explores the transformative fusion of artificial intelligence with spatial computing technologies, providing a comprehensive understanding of how intelligent systems interpret, interact with, and simulate physical environments. It presents a cohesive framework that demonstrates how AI augments spatial awareness and decision-making in complex, dynamic settings, delving into critical technical and ethical issues that arise from integrating AI into spatial systems. These include concerns about data privacy and surveillance, algorithmic bias in location-based services, the computational complexity of real-time 3D perception, and the lack of standardized
platforms for interoperability across spatial devices. The text also examines infrastructure constraints such as bandwidth limitations, latency in edge processing, and the security vulnerabilities of AI-driven spatial networks. Each case study illustrates the tangible benefits of spatial intelligence, providing readers with actionable insights into emerging opportunities and strategic implementations in this rapidly evolving field.
Readers will find the volume:
• Serves as a pioneering resource at the intersection of artificial intelligence and spatial technologies;
• Provides a timely synthesis of how AI techniques can enhance spatial awareness, decision-making, and interactivity in physical and virtual environments;
• Contributes to the maturation of spatial computing as an AI-augmented field, bringing together geoinformatics, robotics, and data science;
• Emphasizes technological integration and methodological and ethical frameworks needed for sustainable innovation.

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Author / Editor Details
Arun Kumar, PhD is an Assistant Professor of Electronics and Communication Engineering, Sikkim Manipal,
Institute of Technology, Sikkim Manipal University, Rangpo, Sikkim, India. He has published more than 200 research articles in national and international journals of repute. His research includes advanced waveforms for 5G mobile communication systems, 5G-based smart hospitals, and spectrum sensing techniques.

Aziz Nanthaamornphong, PhD is the Dean of the College of Computing at the Prince of Songkla University, Phuket, Thailand. He has published more than 150 research articles in international journals and is known for structured thinking, strategic execution, and the ability to translate complex ideas into practical outcomes. His expertise spans artificial intelligence, data science, and advanced communication systems, with applied work in smart tourism, healthcare, and digital transformation.

Nishant Gaur, PhD is an Associate Professor in the Department of Physics at JECRC University, Jaipur, India, with more than 25 years of experience. He has published more than 80 publications in international journals and conferences of repute. His research focuses on advanced 5G and beyond-5G technologies, exploring areas like hybrid machine learning algorithms for enhancing signal detection, reducing peak-to-average power ratios, and optimizing power efficiency.

S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.

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Table of Contents
Series Preface
Preface
Acknowledgement
Part I: AI-Driven Spatial Computing: Concepts
1. Sustainability and Environmental Impact of Digital Twins
and the Metaverse
Amit Kumar Jain and Garima Mathur
1.1 Introduction
1.1.1 Objectives of the Chapter
1.1.2 Novelty of This Book Chapter
1.1.2.1 Integrated Analysis of Digital Twins and Metaverse in Sustainability
1.1.2.2 Focus on Multisectoral and Multidimensional Applications
1.1.2.3 Comprehensive Assessment of Environmental Impacts
1.1.2.4 Case Studies as Practical Examples
1.1.2.5 Future Scope and Strategic Recommendations
1.1.2.6 Ethical and Equity Considerations
1.1.2.7 Bridging Technology with Climate Literacy
1.1.2.8 Systematic and Multidisciplinary Approach
1.2 Foundational Concepts
1.2.1 Understanding Digital Twins
1.2.1.1 Key Features of Digital Twins
1.2.1.2 Types of Digital Twins
1.2.1.3 Applications of Digital Twins
1.2.2 Defining the Metaverse
1.2.2.1 The Technologies Supporting the Metaverse
1.2.2.2 Applications of the Metaverse
1.2.3 The Intersection of Digital Twins and the Metaverse
1.2.3.1 Examples of Convergence
1.2.3.2 Benefits of Convergence
1.2.4 The Link between These Concepts and Sustainability
1.2.4.1 Key Questions for Sustainability
1.3 Environmental Impacts of Digital Twins and Metaverse Infrastructure
1.3.1 Energy Consumption
1.3.2 Resource Extraction and Hardware Production
1.3.2.1 Primary Materials Used
1.3.2.2 Environmental Consequences of Resource Extraction
1.3.3 Electronic Waste and Obsolescence
1.3.3.1 Sources if E-Waste in the Metaverse/Digital Twin Context
1.3.3.2 Challenges with E-Waste
1.3.4 Data Storage and Its Environmental Footprint
1.3.4.1 Environmental Costs of Data Centers
1.4 Advantages and Sustainability Applications
1.4.1 Advantages of Digital Twins and Metaverse Technologies
1.4.2 Sustainability Applications
1.4.2.1 Urban Planning and Smart Cities
1.4.2.2 Renewable Energy Optimization
1.4.2.3 Climate Change Modeling and Adaptation
1.4.2.4 Circular Economy Applications
1.4.2.5 Sustainable Supply Chain Management
1.4.2.6 AI and Water Resource Management
1.5 Case Studies: Sustainable Use of Digital Twins and Metaverse in Real-World Applications
1.5.1 Smart City Development: Singapore’s Virtual Singapore
1.5.2 Urban Water Management: Amsterdam’s Digital Twin for Climate Adaptation
1.5.3 Supply Chain Sustainability: Walmart’s Supply Chain
Digital Twin
1.5.4 Sustainable Construction: The Building Information
Modeling–Digital Twin Integration in Smart Building Design
1.5.5 Climate Change Modeling: NASA’s Earth Science Simulations with Digital Twins
1.5.6 Metaverse for Environmental Education and Climate Change Visualization
1.6 Future Scope
1.6.1 Advanced Climate Change Mitigation and Adaptation
1.6.2 Circular Economy Optimization
1.6.3 Integration with AI and Quantum Computing
1.6.4 Urban Resilience with Smart Cities
1.6.5 Enhanced Sustainable Agriculture and Food Security
1.6.6 Improved Supply Chain Networks
1.6.7 Metaverse as a Collaborative Sustainability Platform
1.6.8 Blockchain for Transparent Climate Action
1.6.9 Environmental Monitoring at Scale
1.7 Recommendations
1.7.1 Prioritize Renewable Energy for Digital Infrastructure
1.7.2 Establish Clear Policy Frameworks and Regulations
1.7.3 Promote Circular Economy Principles in Digital Twin
Development
1.7.4 Encourage Cross-Sectoral Collaboration
1.7.5 Increase Public Engagement and Climate Literacy via the Metaverse
1.7.6 Develop Technology Standards to Measure Sustainability Outcomes
1.8 Applications of Digital Twins and the Metaverse in Sustainability
1.8.1 Smart Urban Infrastructure and Resilient Cities
1.8.2 Climate Science and Environmental Modeling
1.8.3 Sustainable Energy Systems
1.8.4 Precision Agriculture and Water Resource Management
1.8.5 Circular Economy and Waste Minimization
1.8.6 Sustainable Supply Chain and Logistics
1.8.7 Immersive Environmental Education and Engagement
1.8.8 Disaster Management and Resilience Planning
1.9 Limitations and Future Scope
1.9.1 Limitations
1.9.2 Future Scope
1.10 Conclusions
1.10.1 Digital Twins and Metaverse Technologies: Balancing
Challenges with Opportunities
1.10.2 Digital Twins: Advancing Resource Optimization and Climate Resilience
1.10.3 The Metaverse: A Platform for Collaboration and Climate Action
1.10.4 Strategic Recommendations for Sustainable Implementation
References
2. AI for Climate Modeling and Disaster Management
Amit Kumar Jain and Garima Mathur
2.1 Introduction
2.1.1 Objectives of the Chapter
2.2 The Role of AI in Climate Science
2.2.1 Enhancing Climate Modeling and Prediction
2.2.2 Pattern Recognition and Anomaly Detection
2.2.3 Climate Data Assimilation and Fusion
2.2.4 Accelerating Climate Sensitivity Studies
2.2.5 Improving Seasonal and Extreme Weather Forecasts
2.2.6 Greenhouse Gas Emissions Tracking
2.2.7 Climate Impact Assessment and Vulnerability Mapping
2.2.8 Speeding Up Scientific Discovery
2.3 AI Techniques in Climate Modeling
2.3.1 Machine Learning
2.3.2 Deep Learning
2.3.3 Hybrid AI–Physics Models
2.3.4 Transfer Learning
2.3.5 XAI in Climate Science
2.4 AI-Based Climate Prediction Models
2.4.1 Major AI-Based Climate Prediction Techniques
2.4.2 Advantages of AI in Climate Prediction
2.4.3 Challenges in AI-Based Climate Prediction
2.5 AI in Disaster Risk Reduction
2.5.1 Key Roles of AI in Disaster Risk Reduction
2.5.2 Benefits of Using AI in DRR
2.5.3 Challenges and Limitations
2.6 Data Sources and Processing Techniques
2.6.1 Major Data Sources
2.6.2 Data Processing Techniques
2.6.3 Data Infrastructure and Platforms
2.7 Case Studies and Real-World Applications
2.7.1 Google’s AI-Enabled Flood Forecasting (Global)
2.7.2 AI-Powered Bushfire Detection in Australia
2.7.3 Advanced Wildfire Prediction (Europe)
2.7.4 Drone-Based Wildfire Monitoring (Global)
2.8 Challenges and Ethical Considerations
2.8.1 Technical and Data Challenges
2.8.2 Operational and Logistical Challenges
2.8.3 Ethical Considerations
2.8.4 Environmental Impact of AI
2.8.5 Political and Geopolitical Challenges
2.9 Future Trends and Research Directions
2.9.1 Integration of Multimodal and Multisource Data
2.9.2 Self-Supervised and Few-Shot Learning
2.9.3 Explainable and Interpretable AI (XAI)
2.9.4 AI-Enhanced Climate Modeling (Hybrid AI–Physics Models)
2.9.5 Real-Time Edge AI for On-Site Decision-Making
2.9.6 Human-Centered and Participatory AI Systems
2.9.7 Climate-Adaptive Urban and Infrastructure Planning
2.9.8 Ethical AI and Global Governance
2.9.9 AI for Climate Finance and Risk Assessment
2.9.10 Integration with Quantum Computing and HPC
2.9.11 Global AI Platforms for Shared Learning and Early Warning
2.9.12 AI for Anticipatory Governance and Scenario Planning
2.10 Conclusion
Bibliography
3. AI-Powered Language Learning Tools: A Study of Efficacy
in ESL Classrooms
Anubhav Tripathee, Vishnu Kumar Sharma and Nidhi Bhatnagar
3.1 Introduction
3.2 Literature Review
3.2.1 AI in Education
3.2.2 Chatbots and Conversational Agents in ESL
3.2.3 Adaptive Feedback Systems
3.2.4 Learner Engagement and Autonomy
3.2.5 Challenges and Critical Perspectives
3.2.6 Synthesis
3.3 Research Methodology
3.3.1 Research Design
3.3.2 Participants and Sampling
3.3.2.1 Sample Size and Selection
3.3.2.2 Teacher Participants
3.3.3 Research Instruments
3.3.3.1 Quantitative Instruments
3.3.3.2 Qualitative Instruments
3.3.4 AI-Powered Language Learning Tools
3.3.5 Data Collection Procedure
3.3.5.1 Preintervention Phase (Week 1)
3.3.5.2 Intervention Phase (Weeks 2–10)
3.3.5.3 Postintervention Phase (Week 11)
3.3.6 Data Analysis
3.3.6.1 Quantitative Data Analysis
3.3.6.2 Qualitative Data Analysis
3.3.7 Ethical Considerations
3.4 Contributions of This Paper
3.5 Applications of AI in ESL Classrooms
3.5.1 Personalized Learning Platforms
3.5.2 AWE Tools
3.5.3 Content Curation and Recommendation Systems
3.6 Limitations of This Paper
3.7 Future Work
3.8 Conclusion
References
4. Evolution of 5G Networks: Key Technologies, Spectrum
Utilization, and Future Prospects
Arun Kumar, Nishant Gaur and Aziz Nanthaamornphong
4.1 Introduction
4.2 Recent Updates in the 5G Field
4.3 Key Points for Arrangement of 5G Network
4.3.1 Data Analysis
4.3.2 Ultradense Networks
4.3.3 High Data Rate at Constant Mobility
4.3.4 Vertical Handover
4.3.5 Interference and Spectrum Management in 5G Communication
4.4 Key Technologies in 5G Cellular Communication
4.4.1 Femtocell
4.4.2 Quadrature Amplitude Modulation (256-QAM)
4.4.3 Smart Antenna
4.4.4 Faster-than-Nyquist Signaling
4.4.5 Cognitive Radio and Associated Technologies
4.4.6 Device-to-Device Communication
4.5 Channel Estimation in 5G Cellular Communication
4.6 Application of 5G
4.7 Conclusion
References
5. Design and Implementation of a Neuro-Controlled Robot
Using Neural Communication Interface
G. Pius Agbulu, S. Gunasekar and H. Ndwabe
5.1 Introduction
5.2 System Architecture
5.3 Signal Processing and Feature Extraction
5.4 Actor–Critic Decision-Making
5.5 Command Mapping and Communication
5.6 Results and Discussion
5.7 Conclusion
References
Part II: Driven Spatial Computing: Challenges
6. Smart Water Cities in the Brahmaputra Basin: AI Applications for Climate‑Resilient Water Governance in Guwahati
Jyotsna Choudhury and Jesmine Ahmed
6.1 Introduction
6.2 Literature Review
6.2.1 Reliable Communication and Data Exchange for Smart Water Governance
6.2.2 AI and Machine Learning for Efficiency, Adaptability, and Optimization
6.2.3 Reinforcement Learning for Adaptive and Real-Time Decision-Making
6.2.4 Interference Management, Scalability, and System Security
6.2.5 AI, Water Governance, and Climate Resilience in the Global South
6.2.6 Applying Lessons into Smart Water Cities in Guwahati
6.3 Methodology and Limitation
6.4 Guwahati’s Hydro-Urban Profile
6.5 Climate Risk and Disaster Vulnerabilities
6.6 AI for Climate Modeling and Disaster Management in Guwahati
6.7 AI for Smart Water Infrastructure in Guwahati
6.8 Governance, Policy, and Institutional Integration
6.9 Findings and Discussion
6.10 Conclusion and Future Research Direction
Bibliography
7. The Impact of Artificial Intelligence on Human Identity:
A Humanist–Posthumanist Perspective
Nidhi Bhatnagar, Parveen Bala and Rashi Mehta
7.1 Introduction
7.1.1 Background of the Study
7.2 Objectives of the Study
7.3 Literature Review
7.3.1 Humanism in English Studies
7.3.2 Posthumanism and Haraway’s Cyborg Manifesto
7.3.3 AI, Language, and Cultural Discourse
7.3.4 Governance and Policy Perspectives
7.4 Research Gap
7.5 Methodology
7.5.1 Critical Textual Analysis
7.5.2 Thematic Coding
7.5.3 Application of Cyborg Theory
7.5.4 Interdisciplinary Synthesis
7.6 Purpose of the Study
7.7 Findings and Discussion
7.7.1 Cyborg Author and Hybrid Authorship
7.7.2 Cyborg Language: Human + AI Discourse
7.7.3 Surveillance, Commodification, and Cyborg Citizens
7.7.4 Toward Humanistic–Posthumanism
7.8 Conclusion
Bibliography
8. Secure Data Handling in AI-Driven Spatial Computing
Vinay Kishor and Rashi Bhargava
8.1 Introduction (The Intersection of Artificial Intelligence,
Spatial Computing, and Data Security)
8.1.1 The Critical Challenge of Data Security in Spatial Computing
8.1.2 Spatial Computing
8.2 The Role of AI in Spatial Data Processing
8.2.1 Data Collection and Transmission
8.2.2 Foundational Cryptography for Spatial Data
8.2.3 Blockchain for Decentralized Data Provenance
8.2.4 Advanced Privacy-Preserving Techniques
8.3 Challenges and Future Research
8.4 Limitations of the Paper
8.5 Future Work
8.6 Applications for the Project
8.7 Conclusion
Acknowledgements
References
9. Stochastic Planning Approaches and Ecological Footprint
for Sustainable EV Charging Infrastructure
Shah Faisal, Govind Rai Goyal and Bhanu Pratap Soni
9.1 Introduction
9.1.1 Novelty
9.1.2 Contribution
9.1.3 Objective
9.1.4 Limitation
9.2 Overview of EV Charging Infrastructure
9.2.1 Charging Process
9.3 EV Charging Station
9.3.1 Domestic Level 1 Charging Station
9.3.2 Commercial Level 2 Charging Station
9.3.3 Overview of Level 3 DC Fast Charging
9.3.4 Wireless Charging
9.3.4.1 Static Wireless Charging
9.3.4.2 Dynamic Charging via Wireless Power Transfer
9.4 EV Charging Strategy
9.4.1 Unbalanced Energy Demand
9.4.2 Integration of Renewable Energy Sources
9.4.3 Dynamic Pricing Methodology
9.4.4 EV Charger Installation Cost
9.4.5 Stochastic Modeling of Charging Demand
9.5 Charging Station Infrastructure Strategy
9.5.1 Incorporating Renewable Energy into EV-Charging Station Design
9.5.2 Placement of Parking-Based and On-Road EV-Charging Infrastructure
9.5.3 Regular and Irregular Charging Needs Prediction
9.5.4 Interlinked Internet of Vehicles Infrastructure
9.5.5 Dynamic Pricing in EV-Charging Stations
9.5.6 Uncertainty-Based Optimization
9.5.7 Scenario-Based Renewable Energy Integration
9.6 Field Study on the Environmental Impact of EV-Charging
Infrastructure in India
9.6.1 Ecological Footprint Analysis
9.7 Uncertainty in Policy and Market Framework
9.8 Conclusion
9.8.1 Application
9.8.2 Future Scope
References
10. Synergistic Roles of Edge AI and Cloud AI in Advancing
Spatial Intelligence: Architectures, Applications, and Challenges
Sneh Lata
10.1 Introduction
10.1.1 Defining Spatial Computing in the Era of AI
10.1.2 Scope of the Chapter
10.2 Foundations of Spatial Computing
10.2.1 Historical Evolution of Spatial Computing
10.2.2 Core Principles of Spatial Computing
10.2.3 The Role of AI in Enhancing Spatial Systems
10.3 Enabling Technologies
10.3.1 AI/ML Algorithms
10.3.2 Extended Reality (AR, VR, MR)
10.3.3 IoT and Edge Computing
10.3.4 5G/6G and Cloud Infrastructure
10.3.5 Digital Twins and Simulation Platforms
10.3.6 Synthesis of Enabling Technologies
10.3.7 Edge AI and Cloud AI Synergy in Spatial Computing
10.4 Conceptual Framework for AI-Driven Spatial Computing
10.4.1 Data Acquisition and Sensor Integration
10.4.2 AI Models for Spatial Understanding
10.4.3 Interaction Paradigms
10.4.4 Decision-Making and Automation
10.4.5 Human-Centered Design in Spatial AI Systems
10.4.6 Integration of Layers Into a Unified Framework
10.4.7 Hybrid Edge–Cloud Architectures for Spatial Intelligence
10.5 Applications and Use Cases of AI-Driven Spatial Computing
10.5.1 Healthcare and Biomedical Systems
10.5.2 Industrial and Manufacturing Environments
10.5.3 Smart Cities and Urban Infrastructure
10.5.4 Defense and Security
10.5.5 Education and Training
10.5.6 Environmental and Agricultural Systems
10.5.7 Synthesis Across Domains
10.6 Challenges and Future Directions in AI-Driven Spatial
Computing
10.6.1 Technical Challenges
10.6.1.1 Data Quality and Heterogeneity
10.6.1.2 Computational Complexity and Latency
10.6.1.3 Edge–Cloud Orchestration for Spatial Intelligence
10.6.2 Ethical and Societal Challenges
10.6.2.1 Bias and Fairness in Spatial AI
10.6.2.2 Privacy and Surveillance
10.6.2.3 Trust and Human–AI Interaction
10.6.3 Infrastructure and Scalability
10.6.3.1 Edge–Cloud Integration
10.6.3.2 Energy Consumption and Sustainability
10.6.4 Future Research Directions
10.7 Conclusion
Bibliography
11. Balancing Privacy and Efficiency in Indoor Localization: A SafeLoc-Inspired Model Compression Approach
S. Sowjanya Chintalapati, Sriram Parabrahmachari, S. Sathvika, V. Madhavi and M. Vedik Reddy
11.1 Introduction
11.2 Literature Survey
11.2.1 Federated Learning for Privacy-Preserving Indoor Localization
11.2.2 Security and Robustness in Federated Indoor Positioning
11.2.3 Differential Privacy and Device-Free Localization Approaches
11.2.4 Lightweight Model Optimization: Pruning and Quantization
11.3 Identified Gaps in the Literature
11.4 Methodology
11.4.1 System Overview
11.5 Pruning and Quantization
11.5.1 Pruning
11.5.2 Quantization
11.5.3 Combined Impact
11.6 Framework of the Proposed Model
11.7 Mathematical Formulation of SafeLoc
11.7.1 Federated Averaging (Baseline SafeLoc)
11.7.2 Pruning
11.7.3 Quantization
11.8 Expected Benefits
11.9 Experimental Setup
11.9.1 Simulation Environment
11.9.2 System Configuration
11.9.3 Evaluation Metrics
11.10 Results and Discussion
11.10.1 Localization Accuracy
11.10.2 Communication Overhead
11.10.3 Computational Load and Energy Efficiency
11.10.4 Trade-Offs and Implications
11.11 Privacy and Data Protection
11.11.1 Practical Implications
11.11.2 Consent and Transparency
11.11.3 Bystander and Environmental Considerations
11.11.4 Fairness and Nondiscrimination
11.12 Conclusion and Future Scope
References
Part III: AI-Driven Spatial Computing: Use Cases
12. Federated and Edge-Enabled Spatial AI for Privacy-Preserving Climate Resilience and Disaster Management
S. Sowjanya Chintalapati, Sriram Parabrahmachari, S. Sathvika, V. Madhavi and M. Vedik Reddy
12.1 Introduction
12.2 Literature Survey
12.2.1 Federated Learning for Disaster Management
12.2.2 Edge Computing and Unmanned Aerial Vehicles in Disaster Response
12.2.3 Spatial AI and Geospatial Intelligence
12.2.4 Privacy-Preserving Geospatial Analytics
12.2.5 Synthesis
12.3 Methodology
12.4 Workflow of Proposed Model
12.4.1 Potential Benefits of Proposed Model
12.4.2 Sample Use-Case Diagram
12.5 Experimental Setup
12.5.1 Simulation Environment
12.5.2 System Configuration
12.5.3 Evaluation Metrics
12.5.4 Procedure
12.5.5 Conceptual Outcomes
12.6 Results and Discussion
12.7 Ethical Considerations
12.8 Conclusion and Future Scope
12.8.1 Conclusion
12.8.2 Future Scope
References
13. Spatial AI in Literature Teaching: Redefining Pedagogical Space through Immersive Technologies
Vishnu Kumar Sharma, Parveen Bala and Abhishek Singh
13.1 Introduction
13.2 Hypothesis
13.3 Methodology
13.3.1 Conceptual Framework
13.3.2 Selection of Texts
13.3.3 Empirical Pilot Studies
13.3.4 Data Collection and Analysis
13.3.5 Ethical Considerations
13.4 Literature Review
13.4.1 Immersive Learning and Embodied Pedagogy
13.4.2 Spatial Humanities and Literary Mapping
13.4.3 Pedagogical Inclusivity through Spatial AI
13.5 Spatial AI Applications and Cognition-Cum-Creativity
13.5.1 Application: Reimagining The Waste Land through Spatial AI
13.5.2 Application: Reimagining The White Tiger through Spatial AI
13.5.3 Application: Reimagining The God of Small Things through Spatial AI
13.6 Findings
13.7 Conclusion
13.8 Limitations
13.9 Future Work
References
14. Artificial Intelligence in Smart Cities: Toward Sustainable and Intelligent Urban Ecosystems
Sowmya Bhat, Shashikala R. and Jayashree M.
14.1 Introduction
14.1.1 Sensing and Communications
14.1.2 Edge and Cloud Intelligence
14.1.3 Decision Systems and Services
14.2 Scope, Objectives, and Chapter Organization
14.2.1 Scope
14.2.2 Objectives
14.2.3 Chapter Organization
14.3 Background and Key Concepts
14.3.1 Defining Smart Cities
14.3.2 AI Methods Most Relevant to Smart Cities
14.3.3 Urban Data Modalities
14.3.4 Infrastructure and Communication Requirements
14.4 Literature Review
14.4.1 Communications and Wireless Foundations for Urban Sensing
14.4.2 Algorithms and Urban Application Research
14.4.3 Cross-Cutting Issues and Standards
14.4.4 Gaps and Opportunities
14.5 Reference Architecture for AI–Enabled Smart Cities
14.6 Representative Use Cases and Case Studies
14.6.1 Case Study 1: Smart Traffic Management
14.6.2 Case Study 2: Energy and Building Management
14.6.3 Case Study 3: Environmental Monitoring and Air Quality
14.6.4 Case Study 4: Public Safety and Resource Allocation
14.7 Evaluation Metrics, Benchmarking, and Sustainability Considerations
14.7.1 Technical Metrics
14.7.2 Societal and Ethical Metrics
14.7.3 Environmental Impact
14.8 Contributions
14.9 Limitations
14.10 Future Work and Applications
14.10.1 Near Term
14.10.2 Medium Term
14.10.3 Long Term
14.11 Conclusions
References
15. Artificial Intelligence in Agriculture: Current Applications, Challenges, and Future Prospects
Saurabh Dave, Shalini Sujit Kumar, Poonam Hariyani, Vinay Kumar and Hardik Pathak
15.1 Introduction
15.1.1 Background
15.1.2 Sensor Networks and Environmental Monitoring
15.1.3 Control Systems and Automation
15.1.4 Livestock Monitoring and Animal Welfare
15.1.5 Integration with AI and Predictive Analytics
15.1.6 Objective and Problem Statement
15.1.6.1 Global Food Security Challenge
15.1.6.2 Why Traditional Methods Fail
15.1.6.3 AI as a Solution
15.1.7 Significance and Originality
15.1.8 Methodology
15.1.9 Scope and Conceptual Framework
15.2 Classification and Analysis of Applications of IoT in
Agriculture
15.3 Discussion: Toward Scalable and Fair AI Adoption in
Agriculture
15.3.1 Technical Innovations and Systemic Innovation
15.3.2 Low- and Middle-Income Setting Challenges to Adoption
15.3.3 Regional Strategies and Policy Frameworks
15.3.4 Cross-Sector Collaboration and Inclusive Design
15.4 Agricultural Practices and Artificial Intelligence Technologies
15.5 Sustainability and Efficiency Impacts
15.6 Applications of AI in Agriculture
15.7 Limitations and Research Gaps
15.8 Future Work
15.9 Conclusion
References
16. Benefits and Efficiency Combination of Blockchain and 6G Wireless Communication Network Technology
Arun Kumar, Nishant Gaur and Aziz Nanthaamornphong
16.1 Introduction
16.2 Related Research
16.3 Approaches, Difficulties: 6G Networks
16.3.1 THz Frequency Band
16.3.2 Massive MIMO and Beamforming
16.3.3 Integration of Terrestrial and Nonterrestrial Networks
16.3.4 Artificial Intelligence and Machine Learning
16.4 Blockchain Empowering 6G Networks
16.5 The Security Structure of Blockchain-Enabled 6G Networks
16.5.1 Security Threats and Attack Types
16.5.2 Security Structure Design Requirements
16.6 Effectual Execution of Blockchain–6G Network
16.6.1 Transformation of Blockchain Protocols to 6G Networks
16.6.2 Optimization Strategies for Blockchain Storage and Communication in 6G Networks
16.7 Future Research Scope
16.7.1 Integration of Intelligent Blockchain and 6G Networks
16.7.2 Flexible and Efficient Blockchain Consensus Protocols
16.7.3 Postquantum Era Security Safeguards
16.8 Conclusion
References
Index

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