Sachin Kumar Gupta, PhD, is an associate professor in the Department of Electronics and Communication Engineering, Central University of Jamu, India. He has published over 120 research articles in reputed national and international journals and prestigious conference proceedings and is the author of many book chapters, as well as editing numerous books. Manoj Kumar, PhD, is an associate professor at the Faculty of Engineering and Information Sciences, University of Wollongong, Dubai with over 11 years of experience in academics and corporations. He has published over 100 articles, ten patents, and over ten books with reputed publishers. Additionally, he works in various inter-disciplinary areas including data security, forensics, computer networks, image processing, computer vision, machine learning, and Internet of Things. Anand Nayyar, PhD, is a professor and scientist, as well as the Vice Chairman of Research at the School of Computer Science, Duy Tan University, Da Nang, Vietnam, as well as the Director of IoT at Intelligent Systems Lab. He is a certified professional with over 150 professional certificates and has published over 200 research papers, over 100 papers in international conferences and over 70 book chapters. Additionally, he has 18 Australian patents, 15 German patents, four Japanese patents, 13 UK patents, 41 Indian patents, one US patent, three Indian copyrights, and two Canadian copyrights to his credit. Shubham Mahajan, PhD, is an assistant professor at Amity University, India. He has eight Indian, one Australian, and one German patent to his credit in the area of artificial intelligence and image processing. He has authored and co-authored more than 50 publications including peer-reviewed journals and conferences. His main research interests include image processing, video compression, image segmentation, fuzzy entropy, and nature-inspired computing methods with applications in optimization, data mining, machine learning, robotics, and optical communication.

• Preface xix1 Unmanned Aircraft Systems (UASs): Technology, Applications, and Challenges 1Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary and Shahanawaj Ahamad1.1 Introduction 21.1.1 Overview of Unmanned Aircraft Systems (UAS) 31.1.2 Historical Development and Evolution of UAS 61.1.3 Importance and Impact of UAS Technology 81.2 UAS Fundamentals 111.2.1 UAS Components and Architecture 111.2.2 UAS Control and Navigation Systems 141.3 Literature Review 161.4 UAS Applications 201.4.1 Military and Defense Applications 201.4.2 Civil and Commercial Applications 211.4.3 Scientific and Research Applications 221.5 UAS Regulations and Challenges 241.5.1 Regulatory Framework for UAS Operations 241.5.1.1 National and International Regulations 241.5.1.2 Licensing and Certification Requirements 261.5.1.3 Airspace Integration and Traffic Management 271.5.2 Safety and Security Considerations 291.5.2.1 Collision Avoidance and Risk Mitigation 301.5.2.2 Cybersecurity and Data Protection 301.5.2.3 Emergency Procedures and Contingency Planning 301.5.3 Ethical and Legal Challenges 311.5.3.1 Privacy and Surveillance Concerns 311.5.3.2 Liability and Accountability Issues 321.5.3.3 Public Perception and Acceptance 321.5.3.4 UAS Performance Metrics 321.6 Technological Advancements and Future Trends 341.6.1 Emerging Technologies in UAS 341.6.1.1 AI and ml 341.6.1.2 Swarming and Cooperative Systems 361.6.1.3 Extended Flight Endurance and Range 371.6.2 Integration of UAS with Other Technologies 381.6.2.1 IoT and Sensor Networks 381.6.2.2 5G and Communication Infrastructure 401.6.2.3 Augmented Reality (AR) and Virtual Reality (vr) 431.6.3 Future Applications and Impacts of UAS 451.6.3.1 Urban Air Mobility and Air Taxi Services 451.6.3.2 Medical Delivery and Emergency Response 471.6.3.3 Space Exploration and Planetary Science 481.7 Conclusion 501.7.1 Summary of UAS Technology and Applications 511.7.2 Key Challenges and Opportunities in the UAS Industry 521.7.3 Prospects for Future Development and Adoption of UAS 541.8 Future Scope 55References 562 Enhancing the Effectiveness of Drones to Monitor Mars Surface Exploration: A Study 65Harneet Kour, Sachin Kumar Gupta, Shachi Mall, Radha Raman Chandan, Mohd Najim and Pankaj Jain2.1 Introduction 662.2 UAVs’ Exploration on Earth’s Surface 682.2.1 Surveillance 682.2.2 Mapping and Cartography 702.2.3 Environmental Monitoring 712.2.4 Infrastructure Inspection 712.2.5 Agriculture and Crop Monitoring 722.3 UAVs’ Exploration on Mars’ Surface 732.4 In-Depth Analysis of UAVs for Mission Planning and Safety: A Martian Body 762.4.1 Mars Environment and Challenges 782.4.2 Design Considerations for Martian UAVs 812.4.3 Development 832.5 Modeling and Simulation of Martian UAVs 852.5.1 Path Planning and Navigation 872.5.2 Image Processing and Data Analysis 882.5.3 Communication and Data Transmission 892.6 Conclusion and Future Scope 89References 903 IoT-Enabled UAV: A Comprehensive Review of Technological Change in Indian Farming 93Rahul Joshi and Krishna Pandey3.1 Introduction 943.1.1 Indian Perspective on Drone Technology 953.2 Utilization of Drones in Agricultural Practices 973.3 Types of Drones and Sensors 1013.3.1 Drones Based on Design 1013.3.2 Drones Based on Weight 1033.3.3 Drones Based on Sensors 1053.4 Agricultural Drone Industry in India 1073.4.1 An Overview of India’s Farming Drone Business 1083.4.2 Major Organizations in India’s Agricultural Drone Industry 1093.5 Competitive Analysis of the Drone Market in the Agriculture Sector in India 1133.5.1 Prominent International Stakeholders 1133.5.2 Strategic Approach Used by Market Players 1143.5.3 Newest Trends in the Indian Market 1163.5.4 Barriers to Entry in the Indian Market 1183.6 Revenue and Growth of the Indian Drone Market 1203.6.1 Past Revenue Patterns and Future Growth Forecasts for the Drone Industry in the Farming Sector 1213.6.2 Revenue-Growing Components 1213.7 Successful Case Studies of Agriculture Drone in India 1233.8 Regulatory Frameworks Impacting the Use of Drones in Agriculture 1263.8.1 Directorate General of Civil Aviation Guidelines for Farming Drones 1263.8.2 Restricted Zone for Drone Flying in India 1283.9 Conclusion and Future Directions 130References 1314 Applications of AI in UAVs Using In-Flight Parameters 137Yogesh Beeharry and Raviduth Ramful4.1 Introduction 1384.1.1 UAV Technology 1394.1.2 UAV Navigation Technology 1414.1.2.1 Autonomous Navigation Systems 1424.1.3 Artificial Intelligence for UAV Navigation 1454.1.4 Regression-Based Predictive Models 1464.1.4.1 Linear Regression 1464.1.4.2 Regression Decision Tree 1464.1.4.3 Ensemble of Regression Learners 1484.1.4.4 Gaussian Process Regression 1484.1.4.5 Kernel Regression 1484.1.4.6 Regression Neural Network 1494.1.4.7 Regression Support Vector Machine 1504.2 Methodology 1514.2.1 Existing Datasets for UAV Navigation 1514.2.1.1 UAV Delivery Dataset 1514.2.1.2 Hull Drone Indoor Navigation (HDIN) Dataset 1514.2.1.3 UAVVAste Dataset 1514.2.2 Selected Dataset 1514.2.3 System Model 1534.3 Results for Instantaneous Power versus Wind Speed 1544.3.1 Linear Regression Model 1544.3.2 Regression Decision Tree Model 1554.3.3 Ensemble of Regression Learners Model 1574.3.4 Gaussian Process Regression Model 1584.3.5 Kernel Regression Model 1594.3.6 Regression Neural Network Model 1614.3.7 Regression Support Vector Machine 1624.4 Results for Instantaneous Power versus Wind Speed and Wind Angle 1634.4.1 Linear Regression Model 1634.4.2 Regression Decision Tree Model 1654.4.3 Ensemble of Regression Learners Model 1664.4.4 Gaussian Process Regression Model 1684.4.5 Kernel Regression Model 1694.4.6 Regression Neural Network Model 1704.4.7 Regression Support Vector Machine Model 1714.5 Comparative Analysis of Results 1744.6 Conclusion and Future Scope 174References 1755 AVFD: Autonomous Vision-Based Fleet Management for Drone Delivery Optimization in E-Commerce 181Vu Duy Trung, Phuong Anh Nguyen, Toh Yan Chi, Phung Thao Vi, Satyam Mishra and Le Anh Ngoc5.1 Introduction 1825.2 Literature Review 1855.2.1 Overview of Drone Technology in E-Commerce 1855.2.2 Current Challenges in Drone Fleet Management for Last-Mile Delivery 1865.2.3 State-of-the-Art Machine Learning Algorithms for Drone Optimization 1875.2.4 Previous Studies on Face-Tracking and Line-Follower Drones 1895.3 Methodology 1925.3.1 Research Design and Approach 1925.3.2 Data Collection and Sources 1935.3.3 Programming Process 1975.3.4 Experimental Setup for Face-Tracking Drone Development 1995.3.5 Experimental Setup for Line-Follower Drone Development 2045.4 Results and Discussion 2085.4.1 Performance Analysis of Face-Tracker Drone 2085.4.2 Performance Analysis of Line-Follower Drone 2115.4.3 Comparison with Existing Solutions 2135.4.4 Interpretation of Findings 2145.5 Conclusion and Future Scope 215References 2186 STEDSDR: Simulated Testing and Evaluation of Drone Surveillance for Disaster Response 225Yan Chi Toh, Phuong Anh Nguyen, Satyam Mishra, Vu Duy Trung, Phung Thao Vi and Le Anh Ngoc6.1 Introduction 2266.2 Literature Review 2296.3 Research Methodology 2316.3.1 Research Design 2316.3.2 Test Case Development 2316.3.3 Drone Platform and Equipment 2326.3.4 Surveillance and Mapping Software 2346.3.5 Test Execution 2346.3.6 Data Analysis 2366.3.7 Ethical Considerations 2376.3.8 Drone Surveillance 2376.3.9 Drone Mapping 2396.4 Data Collection and Analysis 2416.4.1 Data Collection 2416.4.2 Quantitative Analysis 2476.4.3 Key Results 2516.5 Results and Discussion 2526.6 Conclusion, Recommendations, and Future Scope 255References 2587 Review on Assessment of Land Degradation in Watershed Using Geospatial Technique Based on Unmanned Aircraft Systems 263Soumya Pandey, Neeta Kumari and Lovely Mallick7.1 Introduction 2647.1.1 Global Initiatives Towards Land Degradation 2677.2 Processes of Land Degradation 2697.2.1 Soil Loss 2697.2.2 Land Use Land Cover 2717.2.3 Climate Change 2737.2.4 Hydrological Cycles 2747.2.5 Salinization 2757.2.6 Heavy Metal Pollution 2757.2.7 Plastic Pollution 2767.3 Geospatial Application in Addressing the Land Degradation 2777.4 Components of Unmanned Aircraft Systems (UASs) 2817.5 Data Collection and Processing for UAVs 2837.5.1 Pre-Flight Planning 2837.5.2 Sensors 2847.5.2.1 Optical Sensors 2857.5.2.2 Fluorescence Sensors 2857.5.2.3 Thermal Infrared Sensors 2867.5.2.4 LiDAR Sensors 2867.5.2.5 Gas Sensors 2877.5.2.6 Photogrammetric Sensors 2887.5.3 Platforms—Advantages and Disadvantages 2897.5.3.1 Fixed-Wing UAS 2897.5.3.2 Multirotor UAS 2907.5.3.3 Hybrid UAS 2927.5.3.4 Tethered UAS 2947.6 Advantages of UAS Integrated with GIS for Land Degradation Monitoring 2957.6.1 Selection of UAS 2967.7 Application of UAV in Land Degradation Monitoring and Assessment 2977.8 Conclusion and Future Scope 298References 2998 Unmanned Aircraft Systems (UAS), Surveillance, Risk Management to Cybersecurity and Legal Regulation Landscape: Unraveling the Future Analysis, Challenges, Demand, and Benefits in the High Sky Exploring the Strange New World 313Bhupinder Singh8.1 Introduction 3148.1.1 Significance of Unmanned Aircraft Systems (UASs): Exponential Growth Across Industries 3158.1.2 Unmanned Aircraft Systems (UASs): High Sky Exploring the Strange New World 3178.1.3 Scope of the Chapter 3198.2 Evolution of Unmanned Aircraft Systems: Origin and Widespread Applications in Commercial and Civilian Sectors 3228.2.1 Motivations for UAS Assimilation 3258.3 Surveillance Applications and Ethical Considerations: Advantages and Challenges Associated with Surveillance Operations 3268.4 Risk Management and Safety Aspects within the UAS Ecosystem 3288.5 Cybersecurity Risks and Challenges in UAS: Highlighting Vulnerabilities, Potential Threats, and Need for Robust Cybersecurity Measures to Protect UAS Systems from Hacking, Data Breaches, and Malicious Activities 3318.6 Legal and Regulatory Framework: Airspace Integration and Challenges of Creating Adaptable Frameworks to Accommodate Evolving UAS Technologies 3348.7 Benefits of UAS Adoption: Economic, Environmental, and Societal Advantages to Enhance Efficiency and Reduce Costs via Contributing Toward Agriculture, Logistics, and Disaster Management 3378.8 Challenges and Mitigation Strategies: UAS Integration and Offer Strategies to Mitigate Issues of Privacy Concerns, Regulatory Hurdles, Technological Limitations, and Public Perception 3418.8.1 International Collaboration and Standardization 3448.8.2 Ethical Considerations and Societal Implications 3458.9 Conclusion and Future Scope 346References 3489 Navigating the Future: Unmanned Aerial Systems in IoT Paradigms 355Chandrakant Mahobiya, Sailesh Iyer, Mahendra Verma, Prabhat Ranjan Mishra and Shailendra Kumar Bohidar9.1 Introduction 3569.1.1 Setting the Stage 3569.1.2 Importance of the Convergence 3579.2 The Anatomy of UAS and IoT 3589.2.1 Understanding UAS 3599.2.2 Capabilities 3639.2.3 Classifications 3649.2.4 Exploring IoT 3649.2.5 Architecture 3659.2.5.1 Device Layer 3659.2.5.2 Communication Layer 3659.2.5.3 Data Processing Layer 3669.2.5.4 Application Layer 3669.2.6 Type of Devices 3679.2.7 UAS as IoT Nodes 3679.2.8 History of UAS and IoT 3689.2.8.1 Unmanned Aerial Systems (UASs) 3689.2.8.2 Internet of Things (IoT) 3699.3 Technical Infrastructure 3709.3.1 Communication Protocols 3709.3.1.1 LoRaWAN 3709.3.1.2 25G 3719.3.1.3 ZigBee 3719.3.2 Data Management and Analytics 3719.3.2.1 Edge Computing 3729.3.2.2 Cloud Computing 3739.3.2.3 Data Analytics 3739.3.3 Security Measures 3739.3.4 Types of Drones and Its Applications 3749.4 Application and Use Cases 3759.4.1 Agriculture 3769.4.2 Public Safety 3769.4.3 Industrial Inspection 3779.4.4 Environmental Monitoring 3779.4.5 Media and Entertainment 3779.4.6 Delivery Services 3779.4.7 Surveying and Mapping 3789.4.8 Research and Development 3789.5 Ethical and Legal Dimensions 3789.5.1 Privacy Concerns 3789.5.2 Regulatory Aspects 3799.6 Challenges and Opportunities 3799.6.1 Technological Obstacles 3809.6.1.1 Battery Life 3809.6.1.2 Range 3819.6.1.3 Data Security 3819.7 Conclusion and Future Scope 382References 38310 Dynamic Modeling and Designing Robust MIMO Controller for Rudderless Flying-Wing UAVs 387Sevda Rezazadeh Movahhed and Mohammad Ali Hamed10.1 Introduction 38810.2 Literature Review 39110.3 Materials and Methods 39910.3.1 Physical Model of Rudderless Flying-Wing UAV 39910.3.2 Coordinate System 40010.3.3 Equations of Motion 40110.3.4 Forces and Moments 40210.3.5 Linearized Equations of Motion 40310.3.5.1 Small-Disturbance Theory 40310.3.5.2 Longitudinal and Lateral Motions 40410.3.5.3 State-Space Form 40410.3.6 LQG/LTR Method 40610.4 Proposed Methodology: LQG/LTR Method 40610.4.1 Optimal State Estimator: Kalman Filter 40710.4.2 Optimal State Feedback Controller: LQR Method 40710.4.3 Output Feedback Closed-Loop System 40810.4.4 Loop Transfer Recovery 40810.4.4.1 Kalman Filter-Based Adjustment Approach 40910.4.4.2 LQR Controller-Based Adjustment Approach 41010.5 Results and Discussion 41110.5.1 Case Study 41110.5.2 Longitudinal System Setup 41310.5.3 Lateral System Setup 41710.5.4 Tracking Behavior and Control Signals 41810.5.4.1 Longitudinal Motion 41910.5.4.2 Lateral Motion 42010.5.5 Input Disturbance Rejection 42110.6 Conclusion and Future Scope 423References 42411 Enhancing Security for Unmanned Aircraft Systems in IoT Environments: Defense Mechanisms and Mitigation Strategies 429C.V. Suresh Babu and Abhinaba Pal11.1 Introduction 43011.1.1 Background 43011.1.2 Objective of Chapter 43111.1.3 Scope of the Chapter 43311.2 Security Challenges in IoT-Enabled UAS 43411.2.1 Complexity and Heterogeneity of IoT Systems 43411.2.2 Distributed Nature and Access Control Issues 43611.2.3 Authentication and Confidentiality Concerns 43611.2.4 Data Protection and Firmware Security 43711.3 Case Study: SkySoftware Incident 44111.3.1 Exploiting an Unprotected Communications Link 44111.3.2 Intercepting Live Video Feeds from U.S. Predator Drones 44111.3.3 Implications of the Security Breach 44311.4 GPS Spoofing Attacks on UAS 44311.4.1 Equipment Used and Basic Functioning 44411.4.2 Comprehending GPS Spoofing and Its Corresponding Techniques 44711.4.3 Effects on UAS Navigation and Control 45411.4.4 Limitations of GPS Spoofing and Mitigation Tactics 45511.5 Sensor Based Attacks on UAS 45711.5.1 Laser Attacks 45711.5.2 Mitigation Strategies 46111.6 Trust Architectures for UAS Security 46211.6.1 Application Layer Defensive Security Mechanisms (e.g., MQTT, CoAP) 46211.6.2 Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) Techniques for Secure Drone-to-Drone Communication 46511.7 Subsequent Trends in UAS Security 46911.7.1 A Machine Learning Approach Promoting UAS Edge-Security and Performance 46911.8 Conclusion and Future Scope 470References 47212 Foldable Quadcopters: Design, Analysis, and Additive Manufacturing for Enhanced Aerial Mobility 477Yash H. Thummar and Mohammad Irfan Alam12.1 Background and Introduction 47812.2 Design Methodology 48312.2.1 Selection of Frame 48412.2.2 Understanding the Flight Dynamics 48612.2.3 Creating the Base 48712.2.4 CAD Modeling 48812.2.5 Quadcopter Foldable Arm Design 48912.2.6 Thrust and Total Flight Time Calculation 49112.3 Analysis of Design 49212.3.1 Material Selection 49312.3.2 Loads and Constraints Estimation 49312.3.3 Static Stress Analysis 49412.4 Fabrication Using 3D Printing 49412.4.1 3D Printing Filament 49612.4.2 CAD Part Slicing 49712.4.3 Printing the Quadcopter Parts 50012.5 Components and Assembly 50012.6 Testing and Verification 50612.7 Making to the First Flight 51012.8 Discussions and Applications 51212.9 Conclusions and Future Scope 513References 51413 A Perspective Analysis of UAV Flight Control Architecture Incorporating Ground Control Stations and Near-Actual Techniques 519Imran Mir, Muhammad Amir Tahir and Suleman Mir13.1 Introduction 52013.2 UAV Dynamics and Control Algorithms 52313.2.1 Flight Control Techniques 52713.2.2 Stability and Robustness 52913.3 Near-Actual Simulation Techniques 53213.3.1 Model-in-Loop Simulation 53313.3.2 Software-in-Loop Simulation 53413.3.3 Processor-in-Loop Simulation 53613.3.4 Hardware-in-Loop Simulation 53713.4 Visualization Software 54113.4.1 X-Plane 54213.4.2 FlightGear 54213.4.3 jMAVSim 54413.4.4 Gazebo 54413.5 Ground Control Station 54513.5.1 QGroundControl 54713.5.2 Mission Planner 54713.5.3 Universal Ground Control Software 54913.5.4 MAVProxy 54913.6 Existing Challenges 55013.7 Conclusion 55213.7.1 Future Directions 552References 55414 Optimal Transportation System Based on Adaptive Federated Learning Techniques for Healthcare IoV (HIoV) 563Pallati Narsimhulu, Rashmi Sahay and Premkumar Chithaluru14.1 Introduction 56414.2 Impacts of AI/ML/FL Techniques in HIoV 57914.3 Research Challenges in IoV Transportation 59214.4 Comparative Study 59814.5 Conclusions and Future Scope 605References 606Index 609

Sachin Kumar Gupta, PhD, is an associate professor in the Department of Electronics and Communication Engineering, Central University of Jamu, India. He has published over 120 research articles in reputed national and international journals and prestigious conference proceedings and is the author of many book chapters, as well as editing numerous books. Manoj Kumar, PhD, is an associate professor at the Faculty of Engineering and Information Sciences, University of Wollongong, Dubai with over 11 years of experience in academics and corporations. He has published over 100 articles, ten patents, and over ten books with reputed publishers. Additionally, he works in various inter-disciplinary areas including data security, forensics, computer networks, image processing, computer vision, machine learning, and Internet of Things. Anand Nayyar, PhD, is a professor and scientist, as well as the Vice Chairman of Research at the School of Computer Science, Duy Tan University, Da Nang, Vietnam, as well as the Director of IoT at Intelligent Systems Lab. He is a certified professional with over 150 professional certificates and has published over 200 research papers, over 100 papers in international conferences and over 70 book chapters. Additionally, he has 18 Australian patents, 15 German patents, four Japanese patents, 13 UK patents, 41 Indian patents, one US patent, three Indian copyrights, and two Canadian copyrights to his credit. Shubham Mahajan, PhD, is an assistant professor at Amity University, India. He has eight Indian, one Australian, and one German patent to his credit in the area of artificial intelligence and image processing. He has authored and co-authored more than 50 publications including peer-reviewed journals and conferences. His main research interests include image processing, video compression, image segmentation, fuzzy entropy, and nature-inspired computing methods with applications in optimization, data mining, machine learning, robotics, and optical communication.

• Preface xix1 Unmanned Aircraft Systems (UASs): Technology, Applications, and Challenges 1Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary and Shahanawaj Ahamad1.1 Introduction 21.1.1 Overview of Unmanned Aircraft Systems (UAS) 31.1.2 Historical Development and Evolution of UAS 61.1.3 Importance and Impact of UAS Technology 81.2 UAS Fundamentals 111.2.1 UAS Components and Architecture 111.2.2 UAS Control and Navigation Systems 141.3 Literature Review 161.4 UAS Applications 201.4.1 Military and Defense Applications 201.4.2 Civil and Commercial Applications 211.4.3 Scientific and Research Applications 221.5 UAS Regulations and Challenges 241.5.1 Regulatory Framework for UAS Operations 241.5.1.1 National and International Regulations 241.5.1.2 Licensing and Certification Requirements 261.5.1.3 Airspace Integration and Traffic Management 271.5.2 Safety and Security Considerations 291.5.2.1 Collision Avoidance and Risk Mitigation 301.5.2.2 Cybersecurity and Data Protection 301.5.2.3 Emergency Procedures and Contingency Planning 301.5.3 Ethical and Legal Challenges 311.5.3.1 Privacy and Surveillance Concerns 311.5.3.2 Liability and Accountability Issues 321.5.3.3 Public Perception and Acceptance 321.5.3.4 UAS Performance Metrics 321.6 Technological Advancements and Future Trends 341.6.1 Emerging Technologies in UAS 341.6.1.1 AI and ml 341.6.1.2 Swarming and Cooperative Systems 361.6.1.3 Extended Flight Endurance and Range 371.6.2 Integration of UAS with Other Technologies 381.6.2.1 IoT and Sensor Networks 381.6.2.2 5G and Communication Infrastructure 401.6.2.3 Augmented Reality (AR) and Virtual Reality (vr) 431.6.3 Future Applications and Impacts of UAS 451.6.3.1 Urban Air Mobility and Air Taxi Services 451.6.3.2 Medical Delivery and Emergency Response 471.6.3.3 Space Exploration and Planetary Science 481.7 Conclusion 501.7.1 Summary of UAS Technology and Applications 511.7.2 Key Challenges and Opportunities in the UAS Industry 521.7.3 Prospects for Future Development and Adoption of UAS 541.8 Future Scope 55References 562 Enhancing the Effectiveness of Drones to Monitor Mars Surface Exploration: A Study 65Harneet Kour, Sachin Kumar Gupta, Shachi Mall, Radha Raman Chandan, Mohd Najim and Pankaj Jain2.1 Introduction 662.2 UAVs’ Exploration on Earth’s Surface 682.2.1 Surveillance 682.2.2 Mapping and Cartography 702.2.3 Environmental Monitoring 712.2.4 Infrastructure Inspection 712.2.5 Agriculture and Crop Monitoring 722.3 UAVs’ Exploration on Mars’ Surface 732.4 In-Depth Analysis of UAVs for Mission Planning and Safety: A Martian Body 762.4.1 Mars Environment and Challenges 782.4.2 Design Considerations for Martian UAVs 812.4.3 Development 832.5 Modeling and Simulation of Martian UAVs 852.5.1 Path Planning and Navigation 872.5.2 Image Processing and Data Analysis 882.5.3 Communication and Data Transmission 892.6 Conclusion and Future Scope 89References 903 IoT-Enabled UAV: A Comprehensive Review of Technological Change in Indian Farming 93Rahul Joshi and Krishna Pandey3.1 Introduction 943.1.1 Indian Perspective on Drone Technology 953.2 Utilization of Drones in Agricultural Practices 973.3 Types of Drones and Sensors 1013.3.1 Drones Based on Design 1013.3.2 Drones Based on Weight 1033.3.3 Drones Based on Sensors 1053.4 Agricultural Drone Industry in India 1073.4.1 An Overview of India’s Farming Drone Business 1083.4.2 Major Organizations in India’s Agricultural Drone Industry 1093.5 Competitive Analysis of the Drone Market in the Agriculture Sector in India 1133.5.1 Prominent International Stakeholders 1133.5.2 Strategic Approach Used by Market Players 1143.5.3 Newest Trends in the Indian Market 1163.5.4 Barriers to Entry in the Indian Market 1183.6 Revenue and Growth of the Indian Drone Market 1203.6.1 Past Revenue Patterns and Future Growth Forecasts for the Drone Industry in the Farming Sector 1213.6.2 Revenue-Growing Components 1213.7 Successful Case Studies of Agriculture Drone in India 1233.8 Regulatory Frameworks Impacting the Use of Drones in Agriculture 1263.8.1 Directorate General of Civil Aviation Guidelines for Farming Drones 1263.8.2 Restricted Zone for Drone Flying in India 1283.9 Conclusion and Future Directions 130References 1314 Applications of AI in UAVs Using In-Flight Parameters 137Yogesh Beeharry and Raviduth Ramful4.1 Introduction 1384.1.1 UAV Technology 1394.1.2 UAV Navigation Technology 1414.1.2.1 Autonomous Navigation Systems 1424.1.3 Artificial Intelligence for UAV Navigation 1454.1.4 Regression-Based Predictive Models 1464.1.4.1 Linear Regression 1464.1.4.2 Regression Decision Tree 1464.1.4.3 Ensemble of Regression Learners 1484.1.4.4 Gaussian Process Regression 1484.1.4.5 Kernel Regression 1484.1.4.6 Regression Neural Network 1494.1.4.7 Regression Support Vector Machine 1504.2 Methodology 1514.2.1 Existing Datasets for UAV Navigation 1514.2.1.1 UAV Delivery Dataset 1514.2.1.2 Hull Drone Indoor Navigation (HDIN) Dataset 1514.2.1.3 UAVVAste Dataset 1514.2.2 Selected Dataset 1514.2.3 System Model 1534.3 Results for Instantaneous Power versus Wind Speed 1544.3.1 Linear Regression Model 1544.3.2 Regression Decision Tree Model 1554.3.3 Ensemble of Regression Learners Model 1574.3.4 Gaussian Process Regression Model 1584.3.5 Kernel Regression Model 1594.3.6 Regression Neural Network Model 1614.3.7 Regression Support Vector Machine 1624.4 Results for Instantaneous Power versus Wind Speed and Wind Angle 1634.4.1 Linear Regression Model 1634.4.2 Regression Decision Tree Model 1654.4.3 Ensemble of Regression Learners Model 1664.4.4 Gaussian Process Regression Model 1684.4.5 Kernel Regression Model 1694.4.6 Regression Neural Network Model 1704.4.7 Regression Support Vector Machine Model 1714.5 Comparative Analysis of Results 1744.6 Conclusion and Future Scope 174References 1755 AVFD: Autonomous Vision-Based Fleet Management for Drone Delivery Optimization in E-Commerce 181Vu Duy Trung, Phuong Anh Nguyen, Toh Yan Chi, Phung Thao Vi, Satyam Mishra and Le Anh Ngoc5.1 Introduction 1825.2 Literature Review 1855.2.1 Overview of Drone Technology in E-Commerce 1855.2.2 Current Challenges in Drone Fleet Management for Last-Mile Delivery 1865.2.3 State-of-the-Art Machine Learning Algorithms for Drone Optimization 1875.2.4 Previous Studies on Face-Tracking and Line-Follower Drones 1895.3 Methodology 1925.3.1 Research Design and Approach 1925.3.2 Data Collection and Sources 1935.3.3 Programming Process 1975.3.4 Experimental Setup for Face-Tracking Drone Development 1995.3.5 Experimental Setup for Line-Follower Drone Development 2045.4 Results and Discussion 2085.4.1 Performance Analysis of Face-Tracker Drone 2085.4.2 Performance Analysis of Line-Follower Drone 2115.4.3 Comparison with Existing Solutions 2135.4.4 Interpretation of Findings 2145.5 Conclusion and Future Scope 215References 2186 STEDSDR: Simulated Testing and Evaluation of Drone Surveillance for Disaster Response 225Yan Chi Toh, Phuong Anh Nguyen, Satyam Mishra, Vu Duy Trung, Phung Thao Vi and Le Anh Ngoc6.1 Introduction 2266.2 Literature Review 2296.3 Research Methodology 2316.3.1 Research Design 2316.3.2 Test Case Development 2316.3.3 Drone Platform and Equipment 2326.3.4 Surveillance and Mapping Software 2346.3.5 Test Execution 2346.3.6 Data Analysis 2366.3.7 Ethical Considerations 2376.3.8 Drone Surveillance 2376.3.9 Drone Mapping 2396.4 Data Collection and Analysis 2416.4.1 Data Collection 2416.4.2 Quantitative Analysis 2476.4.3 Key Results 2516.5 Results and Discussion 2526.6 Conclusion, Recommendations, and Future Scope 255References 2587 Review on Assessment of Land Degradation in Watershed Using Geospatial Technique Based on Unmanned Aircraft Systems 263Soumya Pandey, Neeta Kumari and Lovely Mallick7.1 Introduction 2647.1.1 Global Initiatives Towards Land Degradation 2677.2 Processes of Land Degradation 2697.2.1 Soil Loss 2697.2.2 Land Use Land Cover 2717.2.3 Climate Change 2737.2.4 Hydrological Cycles 2747.2.5 Salinization 2757.2.6 Heavy Metal Pollution 2757.2.7 Plastic Pollution 2767.3 Geospatial Application in Addressing the Land Degradation 2777.4 Components of Unmanned Aircraft Systems (UASs) 2817.5 Data Collection and Processing for UAVs 2837.5.1 Pre-Flight Planning 2837.5.2 Sensors 2847.5.2.1 Optical Sensors 2857.5.2.2 Fluorescence Sensors 2857.5.2.3 Thermal Infrared Sensors 2867.5.2.4 LiDAR Sensors 2867.5.2.5 Gas Sensors 2877.5.2.6 Photogrammetric Sensors 2887.5.3 Platforms—Advantages and Disadvantages 2897.5.3.1 Fixed-Wing UAS 2897.5.3.2 Multirotor UAS 2907.5.3.3 Hybrid UAS 2927.5.3.4 Tethered UAS 2947.6 Advantages of UAS Integrated with GIS for Land Degradation Monitoring 2957.6.1 Selection of UAS 2967.7 Application of UAV in Land Degradation Monitoring and Assessment 2977.8 Conclusion and Future Scope 298References 2998 Unmanned Aircraft Systems (UAS), Surveillance, Risk Management to Cybersecurity and Legal Regulation Landscape: Unraveling the Future Analysis, Challenges, Demand, and Benefits in the High Sky Exploring the Strange New World 313Bhupinder Singh8.1 Introduction 3148.1.1 Significance of Unmanned Aircraft Systems (UASs): Exponential Growth Across Industries 3158.1.2 Unmanned Aircraft Systems (UASs): High Sky Exploring the Strange New World 3178.1.3 Scope of the Chapter 3198.2 Evolution of Unmanned Aircraft Systems: Origin and Widespread Applications in Commercial and Civilian Sectors 3228.2.1 Motivations for UAS Assimilation 3258.3 Surveillance Applications and Ethical Considerations: Advantages and Challenges Associated with Surveillance Operations 3268.4 Risk Management and Safety Aspects within the UAS Ecosystem 3288.5 Cybersecurity Risks and Challenges in UAS: Highlighting Vulnerabilities, Potential Threats, and Need for Robust Cybersecurity Measures to Protect UAS Systems from Hacking, Data Breaches, and Malicious Activities 3318.6 Legal and Regulatory Framework: Airspace Integration and Challenges of Creating Adaptable Frameworks to Accommodate Evolving UAS Technologies 3348.7 Benefits of UAS Adoption: Economic, Environmental, and Societal Advantages to Enhance Efficiency and Reduce Costs via Contributing Toward Agriculture, Logistics, and Disaster Management 3378.8 Challenges and Mitigation Strategies: UAS Integration and Offer Strategies to Mitigate Issues of Privacy Concerns, Regulatory Hurdles, Technological Limitations, and Public Perception 3418.8.1 International Collaboration and Standardization 3448.8.2 Ethical Considerations and Societal Implications 3458.9 Conclusion and Future Scope 346References 3489 Navigating the Future: Unmanned Aerial Systems in IoT Paradigms 355Chandrakant Mahobiya, Sailesh Iyer, Mahendra Verma, Prabhat Ranjan Mishra and Shailendra Kumar Bohidar9.1 Introduction 3569.1.1 Setting the Stage 3569.1.2 Importance of the Convergence 3579.2 The Anatomy of UAS and IoT 3589.2.1 Understanding UAS 3599.2.2 Capabilities 3639.2.3 Classifications 3649.2.4 Exploring IoT 3649.2.5 Architecture 3659.2.5.1 Device Layer 3659.2.5.2 Communication Layer 3659.2.5.3 Data Processing Layer 3669.2.5.4 Application Layer 3669.2.6 Type of Devices 3679.2.7 UAS as IoT Nodes 3679.2.8 History of UAS and IoT 3689.2.8.1 Unmanned Aerial Systems (UASs) 3689.2.8.2 Internet of Things (IoT) 3699.3 Technical Infrastructure 3709.3.1 Communication Protocols 3709.3.1.1 LoRaWAN 3709.3.1.2 25G 3719.3.1.3 ZigBee 3719.3.2 Data Management and Analytics 3719.3.2.1 Edge Computing 3729.3.2.2 Cloud Computing 3739.3.2.3 Data Analytics 3739.3.3 Security Measures 3739.3.4 Types of Drones and Its Applications 3749.4 Application and Use Cases 3759.4.1 Agriculture 3769.4.2 Public Safety 3769.4.3 Industrial Inspection 3779.4.4 Environmental Monitoring 3779.4.5 Media and Entertainment 3779.4.6 Delivery Services 3779.4.7 Surveying and Mapping 3789.4.8 Research and Development 3789.5 Ethical and Legal Dimensions 3789.5.1 Privacy Concerns 3789.5.2 Regulatory Aspects 3799.6 Challenges and Opportunities 3799.6.1 Technological Obstacles 3809.6.1.1 Battery Life 3809.6.1.2 Range 3819.6.1.3 Data Security 3819.7 Conclusion and Future Scope 382References 38310 Dynamic Modeling and Designing Robust MIMO Controller for Rudderless Flying-Wing UAVs 387Sevda Rezazadeh Movahhed and Mohammad Ali Hamed10.1 Introduction 38810.2 Literature Review 39110.3 Materials and Methods 39910.3.1 Physical Model of Rudderless Flying-Wing UAV 39910.3.2 Coordinate System 40010.3.3 Equations of Motion 40110.3.4 Forces and Moments 40210.3.5 Linearized Equations of Motion 40310.3.5.1 Small-Disturbance Theory 40310.3.5.2 Longitudinal and Lateral Motions 40410.3.5.3 State-Space Form 40410.3.6 LQG/LTR Method 40610.4 Proposed Methodology: LQG/LTR Method 40610.4.1 Optimal State Estimator: Kalman Filter 40710.4.2 Optimal State Feedback Controller: LQR Method 40710.4.3 Output Feedback Closed-Loop System 40810.4.4 Loop Transfer Recovery 40810.4.4.1 Kalman Filter-Based Adjustment Approach 40910.4.4.2 LQR Controller-Based Adjustment Approach 41010.5 Results and Discussion 41110.5.1 Case Study 41110.5.2 Longitudinal System Setup 41310.5.3 Lateral System Setup 41710.5.4 Tracking Behavior and Control Signals 41810.5.4.1 Longitudinal Motion 41910.5.4.2 Lateral Motion 42010.5.5 Input Disturbance Rejection 42110.6 Conclusion and Future Scope 423References 42411 Enhancing Security for Unmanned Aircraft Systems in IoT Environments: Defense Mechanisms and Mitigation Strategies 429C.V. Suresh Babu and Abhinaba Pal11.1 Introduction 43011.1.1 Background 43011.1.2 Objective of Chapter 43111.1.3 Scope of the Chapter 43311.2 Security Challenges in IoT-Enabled UAS 43411.2.1 Complexity and Heterogeneity of IoT Systems 43411.2.2 Distributed Nature and Access Control Issues 43611.2.3 Authentication and Confidentiality Concerns 43611.2.4 Data Protection and Firmware Security 43711.3 Case Study: SkySoftware Incident 44111.3.1 Exploiting an Unprotected Communications Link 44111.3.2 Intercepting Live Video Feeds from U.S. Predator Drones 44111.3.3 Implications of the Security Breach 44311.4 GPS Spoofing Attacks on UAS 44311.4.1 Equipment Used and Basic Functioning 44411.4.2 Comprehending GPS Spoofing and Its Corresponding Techniques 44711.4.3 Effects on UAS Navigation and Control 45411.4.4 Limitations of GPS Spoofing and Mitigation Tactics 45511.5 Sensor Based Attacks on UAS 45711.5.1 Laser Attacks 45711.5.2 Mitigation Strategies 46111.6 Trust Architectures for UAS Security 46211.6.1 Application Layer Defensive Security Mechanisms (e.g., MQTT, CoAP) 46211.6.2 Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) Techniques for Secure Drone-to-Drone Communication 46511.7 Subsequent Trends in UAS Security 46911.7.1 A Machine Learning Approach Promoting UAS Edge-Security and Performance 46911.8 Conclusion and Future Scope 470References 47212 Foldable Quadcopters: Design, Analysis, and Additive Manufacturing for Enhanced Aerial Mobility 477Yash H. Thummar and Mohammad Irfan Alam12.1 Background and Introduction 47812.2 Design Methodology 48312.2.1 Selection of Frame 48412.2.2 Understanding the Flight Dynamics 48612.2.3 Creating the Base 48712.2.4 CAD Modeling 48812.2.5 Quadcopter Foldable Arm Design 48912.2.6 Thrust and Total Flight Time Calculation 49112.3 Analysis of Design 49212.3.1 Material Selection 49312.3.2 Loads and Constraints Estimation 49312.3.3 Static Stress Analysis 49412.4 Fabrication Using 3D Printing 49412.4.1 3D Printing Filament 49612.4.2 CAD Part Slicing 49712.4.3 Printing the Quadcopter Parts 50012.5 Components and Assembly 50012.6 Testing and Verification 50612.7 Making to the First Flight 51012.8 Discussions and Applications 51212.9 Conclusions and Future Scope 513References 51413 A Perspective Analysis of UAV Flight Control Architecture Incorporating Ground Control Stations and Near-Actual Techniques 519Imran Mir, Muhammad Amir Tahir and Suleman Mir13.1 Introduction 52013.2 UAV Dynamics and Control Algorithms 52313.2.1 Flight Control Techniques 52713.2.2 Stability and Robustness 52913.3 Near-Actual Simulation Techniques 53213.3.1 Model-in-Loop Simulation 53313.3.2 Software-in-Loop Simulation 53413.3.3 Processor-in-Loop Simulation 53613.3.4 Hardware-in-Loop Simulation 53713.4 Visualization Software 54113.4.1 X-Plane 54213.4.2 FlightGear 54213.4.3 jMAVSim 54413.4.4 Gazebo 54413.5 Ground Control Station 54513.5.1 QGroundControl 54713.5.2 Mission Planner 54713.5.3 Universal Ground Control Software 54913.5.4 MAVProxy 54913.6 Existing Challenges 55013.7 Conclusion 55213.7.1 Future Directions 552References 55414 Optimal Transportation System Based on Adaptive Federated Learning Techniques for Healthcare IoV (HIoV) 563Pallati Narsimhulu, Rashmi Sahay and Premkumar Chithaluru14.1 Introduction 56414.2 Impacts of AI/ML/FL Techniques in HIoV 57914.3 Research Challenges in IoV Transportation 59214.4 Comparative Study 59814.5 Conclusions and Future Scope 605References 606Index 609

Sachin Kumar Gupta, PhD, is an associate professor in the Department of Electronics and Communication Engineering, Central University of Jamu, India. He has published over 120 research articles in reputed national and international journals and prestigious conference proceedings and is the author of many book chapters, as well as editing numerous books. Manoj Kumar, PhD, is an associate professor at the Faculty of Engineering and Information Sciences, University of Wollongong, Dubai with over 11 years of experience in academics and corporations. He has published over 100 articles, ten patents, and over ten books with reputed publishers. Additionally, he works in various inter-disciplinary areas including data security, forensics, computer networks, image processing, computer vision, machine learning, and Internet of Things. Anand Nayyar, PhD, is a professor and scientist, as well as the Vice Chairman of Research at the School of Computer Science, Duy Tan University, Da Nang, Vietnam, as well as the Director of IoT at Intelligent Systems Lab. He is a certified professional with over 150 professional certificates and has published over 200 research papers, over 100 papers in international conferences and over 70 book chapters. Additionally, he has 18 Australian patents, 15 German patents, four Japanese patents, 13 UK patents, 41 Indian patents, one US patent, three Indian copyrights, and two Canadian copyrights to his credit. Shubham Mahajan, PhD, is an assistant professor at Amity University, India. He has eight Indian, one Australian, and one German patent to his credit in the area of artificial intelligence and image processing. He has authored and co-authored more than 50 publications including peer-reviewed journals and conferences. His main research interests include image processing, video compression, image segmentation, fuzzy entropy, and nature-inspired computing methods with applications in optimization, data mining, machine learning, robotics, and optical communication.

• Preface xix1 Unmanned Aircraft Systems (UASs): Technology, Applications, and Challenges 1Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary and Shahanawaj Ahamad1.1 Introduction 21.1.1 Overview of Unmanned Aircraft Systems (UAS) 31.1.2 Historical Development and Evolution of UAS 61.1.3 Importance and Impact of UAS Technology 81.2 UAS Fundamentals 111.2.1 UAS Components and Architecture 111.2.2 UAS Control and Navigation Systems 141.3 Literature Review 161.4 UAS Applications 201.4.1 Military and Defense Applications 201.4.2 Civil and Commercial Applications 211.4.3 Scientific and Research Applications 221.5 UAS Regulations and Challenges 241.5.1 Regulatory Framework for UAS Operations 241.5.1.1 National and International Regulations 241.5.1.2 Licensing and Certification Requirements 261.5.1.3 Airspace Integration and Traffic Management 271.5.2 Safety and Security Considerations 291.5.2.1 Collision Avoidance and Risk Mitigation 301.5.2.2 Cybersecurity and Data Protection 301.5.2.3 Emergency Procedures and Contingency Planning 301.5.3 Ethical and Legal Challenges 311.5.3.1 Privacy and Surveillance Concerns 311.5.3.2 Liability and Accountability Issues 321.5.3.3 Public Perception and Acceptance 321.5.3.4 UAS Performance Metrics 321.6 Technological Advancements and Future Trends 341.6.1 Emerging Technologies in UAS 341.6.1.1 AI and ml 341.6.1.2 Swarming and Cooperative Systems 361.6.1.3 Extended Flight Endurance and Range 371.6.2 Integration of UAS with Other Technologies 381.6.2.1 IoT and Sensor Networks 381.6.2.2 5G and Communication Infrastructure 401.6.2.3 Augmented Reality (AR) and Virtual Reality (vr) 431.6.3 Future Applications and Impacts of UAS 451.6.3.1 Urban Air Mobility and Air Taxi Services 451.6.3.2 Medical Delivery and Emergency Response 471.6.3.3 Space Exploration and Planetary Science 481.7 Conclusion 501.7.1 Summary of UAS Technology and Applications 511.7.2 Key Challenges and Opportunities in the UAS Industry 521.7.3 Prospects for Future Development and Adoption of UAS 541.8 Future Scope 55References 562 Enhancing the Effectiveness of Drones to Monitor Mars Surface Exploration: A Study 65Harneet Kour, Sachin Kumar Gupta, Shachi Mall, Radha Raman Chandan, Mohd Najim and Pankaj Jain2.1 Introduction 662.2 UAVs’ Exploration on Earth’s Surface 682.2.1 Surveillance 682.2.2 Mapping and Cartography 702.2.3 Environmental Monitoring 712.2.4 Infrastructure Inspection 712.2.5 Agriculture and Crop Monitoring 722.3 UAVs’ Exploration on Mars’ Surface 732.4 In-Depth Analysis of UAVs for Mission Planning and Safety: A Martian Body 762.4.1 Mars Environment and Challenges 782.4.2 Design Considerations for Martian UAVs 812.4.3 Development 832.5 Modeling and Simulation of Martian UAVs 852.5.1 Path Planning and Navigation 872.5.2 Image Processing and Data Analysis 882.5.3 Communication and Data Transmission 892.6 Conclusion and Future Scope 89References 903 IoT-Enabled UAV: A Comprehensive Review of Technological Change in Indian Farming 93Rahul Joshi and Krishna Pandey3.1 Introduction 943.1.1 Indian Perspective on Drone Technology 953.2 Utilization of Drones in Agricultural Practices 973.3 Types of Drones and Sensors 1013.3.1 Drones Based on Design 1013.3.2 Drones Based on Weight 1033.3.3 Drones Based on Sensors 1053.4 Agricultural Drone Industry in India 1073.4.1 An Overview of India’s Farming Drone Business 1083.4.2 Major Organizations in India’s Agricultural Drone Industry 1093.5 Competitive Analysis of the Drone Market in the Agriculture Sector in India 1133.5.1 Prominent International Stakeholders 1133.5.2 Strategic Approach Used by Market Players 1143.5.3 Newest Trends in the Indian Market 1163.5.4 Barriers to Entry in the Indian Market 1183.6 Revenue and Growth of the Indian Drone Market 1203.6.1 Past Revenue Patterns and Future Growth Forecasts for the Drone Industry in the Farming Sector 1213.6.2 Revenue-Growing Components 1213.7 Successful Case Studies of Agriculture Drone in India 1233.8 Regulatory Frameworks Impacting the Use of Drones in Agriculture 1263.8.1 Directorate General of Civil Aviation Guidelines for Farming Drones 1263.8.2 Restricted Zone for Drone Flying in India 1283.9 Conclusion and Future Directions 130References 1314 Applications of AI in UAVs Using In-Flight Parameters 137Yogesh Beeharry and Raviduth Ramful4.1 Introduction 1384.1.1 UAV Technology 1394.1.2 UAV Navigation Technology 1414.1.2.1 Autonomous Navigation Systems 1424.1.3 Artificial Intelligence for UAV Navigation 1454.1.4 Regression-Based Predictive Models 1464.1.4.1 Linear Regression 1464.1.4.2 Regression Decision Tree 1464.1.4.3 Ensemble of Regression Learners 1484.1.4.4 Gaussian Process Regression 1484.1.4.5 Kernel Regression 1484.1.4.6 Regression Neural Network 1494.1.4.7 Regression Support Vector Machine 1504.2 Methodology 1514.2.1 Existing Datasets for UAV Navigation 1514.2.1.1 UAV Delivery Dataset 1514.2.1.2 Hull Drone Indoor Navigation (HDIN) Dataset 1514.2.1.3 UAVVAste Dataset 1514.2.2 Selected Dataset 1514.2.3 System Model 1534.3 Results for Instantaneous Power versus Wind Speed 1544.3.1 Linear Regression Model 1544.3.2 Regression Decision Tree Model 1554.3.3 Ensemble of Regression Learners Model 1574.3.4 Gaussian Process Regression Model 1584.3.5 Kernel Regression Model 1594.3.6 Regression Neural Network Model 1614.3.7 Regression Support Vector Machine 1624.4 Results for Instantaneous Power versus Wind Speed and Wind Angle 1634.4.1 Linear Regression Model 1634.4.2 Regression Decision Tree Model 1654.4.3 Ensemble of Regression Learners Model 1664.4.4 Gaussian Process Regression Model 1684.4.5 Kernel Regression Model 1694.4.6 Regression Neural Network Model 1704.4.7 Regression Support Vector Machine Model 1714.5 Comparative Analysis of Results 1744.6 Conclusion and Future Scope 174References 1755 AVFD: Autonomous Vision-Based Fleet Management for Drone Delivery Optimization in E-Commerce 181Vu Duy Trung, Phuong Anh Nguyen, Toh Yan Chi, Phung Thao Vi, Satyam Mishra and Le Anh Ngoc5.1 Introduction 1825.2 Literature Review 1855.2.1 Overview of Drone Technology in E-Commerce 1855.2.2 Current Challenges in Drone Fleet Management for Last-Mile Delivery 1865.2.3 State-of-the-Art Machine Learning Algorithms for Drone Optimization 1875.2.4 Previous Studies on Face-Tracking and Line-Follower Drones 1895.3 Methodology 1925.3.1 Research Design and Approach 1925.3.2 Data Collection and Sources 1935.3.3 Programming Process 1975.3.4 Experimental Setup for Face-Tracking Drone Development 1995.3.5 Experimental Setup for Line-Follower Drone Development 2045.4 Results and Discussion 2085.4.1 Performance Analysis of Face-Tracker Drone 2085.4.2 Performance Analysis of Line-Follower Drone 2115.4.3 Comparison with Existing Solutions 2135.4.4 Interpretation of Findings 2145.5 Conclusion and Future Scope 215References 2186 STEDSDR: Simulated Testing and Evaluation of Drone Surveillance for Disaster Response 225Yan Chi Toh, Phuong Anh Nguyen, Satyam Mishra, Vu Duy Trung, Phung Thao Vi and Le Anh Ngoc6.1 Introduction 2266.2 Literature Review 2296.3 Research Methodology 2316.3.1 Research Design 2316.3.2 Test Case Development 2316.3.3 Drone Platform and Equipment 2326.3.4 Surveillance and Mapping Software 2346.3.5 Test Execution 2346.3.6 Data Analysis 2366.3.7 Ethical Considerations 2376.3.8 Drone Surveillance 2376.3.9 Drone Mapping 2396.4 Data Collection and Analysis 2416.4.1 Data Collection 2416.4.2 Quantitative Analysis 2476.4.3 Key Results 2516.5 Results and Discussion 2526.6 Conclusion, Recommendations, and Future Scope 255References 2587 Review on Assessment of Land Degradation in Watershed Using Geospatial Technique Based on Unmanned Aircraft Systems 263Soumya Pandey, Neeta Kumari and Lovely Mallick7.1 Introduction 2647.1.1 Global Initiatives Towards Land Degradation 2677.2 Processes of Land Degradation 2697.2.1 Soil Loss 2697.2.2 Land Use Land Cover 2717.2.3 Climate Change 2737.2.4 Hydrological Cycles 2747.2.5 Salinization 2757.2.6 Heavy Metal Pollution 2757.2.7 Plastic Pollution 2767.3 Geospatial Application in Addressing the Land Degradation 2777.4 Components of Unmanned Aircraft Systems (UASs) 2817.5 Data Collection and Processing for UAVs 2837.5.1 Pre-Flight Planning 2837.5.2 Sensors 2847.5.2.1 Optical Sensors 2857.5.2.2 Fluorescence Sensors 2857.5.2.3 Thermal Infrared Sensors 2867.5.2.4 LiDAR Sensors 2867.5.2.5 Gas Sensors 2877.5.2.6 Photogrammetric Sensors 2887.5.3 Platforms—Advantages and Disadvantages 2897.5.3.1 Fixed-Wing UAS 2897.5.3.2 Multirotor UAS 2907.5.3.3 Hybrid UAS 2927.5.3.4 Tethered UAS 2947.6 Advantages of UAS Integrated with GIS for Land Degradation Monitoring 2957.6.1 Selection of UAS 2967.7 Application of UAV in Land Degradation Monitoring and Assessment 2977.8 Conclusion and Future Scope 298References 2998 Unmanned Aircraft Systems (UAS), Surveillance, Risk Management to Cybersecurity and Legal Regulation Landscape: Unraveling the Future Analysis, Challenges, Demand, and Benefits in the High Sky Exploring the Strange New World 313Bhupinder Singh8.1 Introduction 3148.1.1 Significance of Unmanned Aircraft Systems (UASs): Exponential Growth Across Industries 3158.1.2 Unmanned Aircraft Systems (UASs): High Sky Exploring the Strange New World 3178.1.3 Scope of the Chapter 3198.2 Evolution of Unmanned Aircraft Systems: Origin and Widespread Applications in Commercial and Civilian Sectors 3228.2.1 Motivations for UAS Assimilation 3258.3 Surveillance Applications and Ethical Considerations: Advantages and Challenges Associated with Surveillance Operations 3268.4 Risk Management and Safety Aspects within the UAS Ecosystem 3288.5 Cybersecurity Risks and Challenges in UAS: Highlighting Vulnerabilities, Potential Threats, and Need for Robust Cybersecurity Measures to Protect UAS Systems from Hacking, Data Breaches, and Malicious Activities 3318.6 Legal and Regulatory Framework: Airspace Integration and Challenges of Creating Adaptable Frameworks to Accommodate Evolving UAS Technologies 3348.7 Benefits of UAS Adoption: Economic, Environmental, and Societal Advantages to Enhance Efficiency and Reduce Costs via Contributing Toward Agriculture, Logistics, and Disaster Management 3378.8 Challenges and Mitigation Strategies: UAS Integration and Offer Strategies to Mitigate Issues of Privacy Concerns, Regulatory Hurdles, Technological Limitations, and Public Perception 3418.8.1 International Collaboration and Standardization 3448.8.2 Ethical Considerations and Societal Implications 3458.9 Conclusion and Future Scope 346References 3489 Navigating the Future: Unmanned Aerial Systems in IoT Paradigms 355Chandrakant Mahobiya, Sailesh Iyer, Mahendra Verma, Prabhat Ranjan Mishra and Shailendra Kumar Bohidar9.1 Introduction 3569.1.1 Setting the Stage 3569.1.2 Importance of the Convergence 3579.2 The Anatomy of UAS and IoT 3589.2.1 Understanding UAS 3599.2.2 Capabilities 3639.2.3 Classifications 3649.2.4 Exploring IoT 3649.2.5 Architecture 3659.2.5.1 Device Layer 3659.2.5.2 Communication Layer 3659.2.5.3 Data Processing Layer 3669.2.5.4 Application Layer 3669.2.6 Type of Devices 3679.2.7 UAS as IoT Nodes 3679.2.8 History of UAS and IoT 3689.2.8.1 Unmanned Aerial Systems (UASs) 3689.2.8.2 Internet of Things (IoT) 3699.3 Technical Infrastructure 3709.3.1 Communication Protocols 3709.3.1.1 LoRaWAN 3709.3.1.2 25G 3719.3.1.3 ZigBee 3719.3.2 Data Management and Analytics 3719.3.2.1 Edge Computing 3729.3.2.2 Cloud Computing 3739.3.2.3 Data Analytics 3739.3.3 Security Measures 3739.3.4 Types of Drones and Its Applications 3749.4 Application and Use Cases 3759.4.1 Agriculture 3769.4.2 Public Safety 3769.4.3 Industrial Inspection 3779.4.4 Environmental Monitoring 3779.4.5 Media and Entertainment 3779.4.6 Delivery Services 3779.4.7 Surveying and Mapping 3789.4.8 Research and Development 3789.5 Ethical and Legal Dimensions 3789.5.1 Privacy Concerns 3789.5.2 Regulatory Aspects 3799.6 Challenges and Opportunities 3799.6.1 Technological Obstacles 3809.6.1.1 Battery Life 3809.6.1.2 Range 3819.6.1.3 Data Security 3819.7 Conclusion and Future Scope 382References 38310 Dynamic Modeling and Designing Robust MIMO Controller for Rudderless Flying-Wing UAVs 387Sevda Rezazadeh Movahhed and Mohammad Ali Hamed10.1 Introduction 38810.2 Literature Review 39110.3 Materials and Methods 39910.3.1 Physical Model of Rudderless Flying-Wing UAV 39910.3.2 Coordinate System 40010.3.3 Equations of Motion 40110.3.4 Forces and Moments 40210.3.5 Linearized Equations of Motion 40310.3.5.1 Small-Disturbance Theory 40310.3.5.2 Longitudinal and Lateral Motions 40410.3.5.3 State-Space Form 40410.3.6 LQG/LTR Method 40610.4 Proposed Methodology: LQG/LTR Method 40610.4.1 Optimal State Estimator: Kalman Filter 40710.4.2 Optimal State Feedback Controller: LQR Method 40710.4.3 Output Feedback Closed-Loop System 40810.4.4 Loop Transfer Recovery 40810.4.4.1 Kalman Filter-Based Adjustment Approach 40910.4.4.2 LQR Controller-Based Adjustment Approach 41010.5 Results and Discussion 41110.5.1 Case Study 41110.5.2 Longitudinal System Setup 41310.5.3 Lateral System Setup 41710.5.4 Tracking Behavior and Control Signals 41810.5.4.1 Longitudinal Motion 41910.5.4.2 Lateral Motion 42010.5.5 Input Disturbance Rejection 42110.6 Conclusion and Future Scope 423References 42411 Enhancing Security for Unmanned Aircraft Systems in IoT Environments: Defense Mechanisms and Mitigation Strategies 429C.V. Suresh Babu and Abhinaba Pal11.1 Introduction 43011.1.1 Background 43011.1.2 Objective of Chapter 43111.1.3 Scope of the Chapter 43311.2 Security Challenges in IoT-Enabled UAS 43411.2.1 Complexity and Heterogeneity of IoT Systems 43411.2.2 Distributed Nature and Access Control Issues 43611.2.3 Authentication and Confidentiality Concerns 43611.2.4 Data Protection and Firmware Security 43711.3 Case Study: SkySoftware Incident 44111.3.1 Exploiting an Unprotected Communications Link 44111.3.2 Intercepting Live Video Feeds from U.S. Predator Drones 44111.3.3 Implications of the Security Breach 44311.4 GPS Spoofing Attacks on UAS 44311.4.1 Equipment Used and Basic Functioning 44411.4.2 Comprehending GPS Spoofing and Its Corresponding Techniques 44711.4.3 Effects on UAS Navigation and Control 45411.4.4 Limitations of GPS Spoofing and Mitigation Tactics 45511.5 Sensor Based Attacks on UAS 45711.5.1 Laser Attacks 45711.5.2 Mitigation Strategies 46111.6 Trust Architectures for UAS Security 46211.6.1 Application Layer Defensive Security Mechanisms (e.g., MQTT, CoAP) 46211.6.2 Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) Techniques for Secure Drone-to-Drone Communication 46511.7 Subsequent Trends in UAS Security 46911.7.1 A Machine Learning Approach Promoting UAS Edge-Security and Performance 46911.8 Conclusion and Future Scope 470References 47212 Foldable Quadcopters: Design, Analysis, and Additive Manufacturing for Enhanced Aerial Mobility 477Yash H. Thummar and Mohammad Irfan Alam12.1 Background and Introduction 47812.2 Design Methodology 48312.2.1 Selection of Frame 48412.2.2 Understanding the Flight Dynamics 48612.2.3 Creating the Base 48712.2.4 CAD Modeling 48812.2.5 Quadcopter Foldable Arm Design 48912.2.6 Thrust and Total Flight Time Calculation 49112.3 Analysis of Design 49212.3.1 Material Selection 49312.3.2 Loads and Constraints Estimation 49312.3.3 Static Stress Analysis 49412.4 Fabrication Using 3D Printing 49412.4.1 3D Printing Filament 49612.4.2 CAD Part Slicing 49712.4.3 Printing the Quadcopter Parts 50012.5 Components and Assembly 50012.6 Testing and Verification 50612.7 Making to the First Flight 51012.8 Discussions and Applications 51212.9 Conclusions and Future Scope 513References 51413 A Perspective Analysis of UAV Flight Control Architecture Incorporating Ground Control Stations and Near-Actual Techniques 519Imran Mir, Muhammad Amir Tahir and Suleman Mir13.1 Introduction 52013.2 UAV Dynamics and Control Algorithms 52313.2.1 Flight Control Techniques 52713.2.2 Stability and Robustness 52913.3 Near-Actual Simulation Techniques 53213.3.1 Model-in-Loop Simulation 53313.3.2 Software-in-Loop Simulation 53413.3.3 Processor-in-Loop Simulation 53613.3.4 Hardware-in-Loop Simulation 53713.4 Visualization Software 54113.4.1 X-Plane 54213.4.2 FlightGear 54213.4.3 jMAVSim 54413.4.4 Gazebo 54413.5 Ground Control Station 54513.5.1 QGroundControl 54713.5.2 Mission Planner 54713.5.3 Universal Ground Control Software 54913.5.4 MAVProxy 54913.6 Existing Challenges 55013.7 Conclusion 55213.7.1 Future Directions 552References 55414 Optimal Transportation System Based on Adaptive Federated Learning Techniques for Healthcare IoV (HIoV) 563Pallati Narsimhulu, Rashmi Sahay and Premkumar Chithaluru14.1 Introduction 56414.2 Impacts of AI/ML/FL Techniques in HIoV 57914.3 Research Challenges in IoV Transportation 59214.4 Comparative Study 59814.5 Conclusions and Future Scope 605References 606Index 609