With the evolution of Unmanned Aircraft Systems (UAS), its applications can be observed in the fields of monitoring for fire detection, sustainable computing, emergencies, and law enforcement. They can be useful for monitoring or screening applications, as well as the deployment of smart cities, security monitoring, and communication establishments at rare locations or unapproachable locations. Thus, the wireless ad-hoc networks of Unmanned Aerial Vehicles (UAVs) and infrastructure-based UAVs can be utilized in this proposal. Unmanned aircraft systems (UAS) extend human potential and allow us to execute dangerous or difficult tasks safely and efficiently, saving time, money, and, most importantly, lives. UAS can help police, fire, and other public workers save lives in emergencies like natural disasters, locate missing animals and children, or help fight fighters.
Unmanned Aircraft Systems contains novel contributions and emerging trends in the area of Unmanned Aerial Vehicles (UAV), drones, and aircraft without a human pilot aboard. It has three segments incorporating technological advancements and future trends in UAS, the policies and security aspects of UAVs, and their applications as an intelligent system. Along with these state-of-the-art techniques, this book also incorporates advances in AI and machine learning, deep learning, IoT technology, cybersecurity and Blockchain, UAV regulation policies in the United States and Europe, SOTA in ITS, and many more technological advancements, which makes this book the pioneer and benchmarking reference in these areas.
Table of Contents
Preface xix
1 Unmanned Aircraft Systems (UASs): Technology, Applications, and Challenges 1
Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary and Shahanawaj Ahamad
1.1 Introduction 2
1.1.1 Overview of Unmanned Aircraft Systems (UAS) 3
1.1.2 Historical Development and Evolution of UAS 6
1.1.3 Importance and Impact of UAS Technology 8
1.2 UAS Fundamentals 11
1.2.1 UAS Components and Architecture 11
1.2.2 UAS Control and Navigation Systems 14
1.3 Literature Review 16
1.4 UAS Applications 20
1.4.1 Military and Defense Applications 20
1.4.2 Civil and Commercial Applications 21
1.4.3 Scientific and Research Applications 22
1.5 UAS Regulations and Challenges 24
1.5.1 Regulatory Framework for UAS Operations 24
1.5.1.1 National and International Regulations 24
1.5.1.2 Licensing and Certification Requirements 26
1.5.1.3 Airspace Integration and Traffic Management 27
1.5.2 Safety and Security Considerations 29
1.5.2.1 Collision Avoidance and Risk Mitigation 30
1.5.2.2 Cybersecurity and Data Protection 30
1.5.2.3 Emergency Procedures and Contingency Planning 30
1.5.3 Ethical and Legal Challenges 31
1.5.3.1 Privacy and Surveillance Concerns 31
1.5.3.2 Liability and Accountability Issues 32
1.5.3.3 Public Perception and Acceptance 32
1.5.3.4 UAS Performance Metrics 32
1.6 Technological Advancements and Future Trends 34
1.6.1 Emerging Technologies in UAS 34
1.6.1.1 AI and ml 34
1.6.1.2 Swarming and Cooperative Systems 36
1.6.1.3 Extended Flight Endurance and Range 37
1.6.2 Integration of UAS with Other Technologies 38
1.6.2.1 IoT and Sensor Networks 38
1.6.2.2 5G and Communication Infrastructure 40
1.6.2.3 Augmented Reality (AR) and Virtual Reality (vr) 43
1.6.3 Future Applications and Impacts of UAS 45
1.6.3.1 Urban Air Mobility and Air Taxi Services 45
1.6.3.2 Medical Delivery and Emergency Response 47
1.6.3.3 Space Exploration and Planetary Science 48
1.7 Conclusion 50
1.7.1 Summary of UAS Technology and Applications 51
1.7.2 Key Challenges and Opportunities in the UAS Industry 52
1.7.3 Prospects for Future Development and Adoption of UAS 54
1.8 Future Scope 55
References 56
2 Enhancing the Effectiveness of Drones to Monitor Mars Surface Exploration: A Study 65
Harneet Kour, Sachin Kumar Gupta, Shachi Mall, Radha Raman Chandan, Mohd Najim and Pankaj Jain
2.1 Introduction 66
2.2 UAVs’ Exploration on Earth’s Surface 68
2.2.1 Surveillance 68
2.2.2 Mapping and Cartography 70
2.2.3 Environmental Monitoring 71
2.2.4 Infrastructure Inspection 71
2.2.5 Agriculture and Crop Monitoring 72
2.3 UAVs’ Exploration on Mars’ Surface 73
2.4 In-Depth Analysis of UAVs for Mission Planning and Safety: A Martian Body 76
2.4.1 Mars Environment and Challenges 78
2.4.2 Design Considerations for Martian UAVs 81
2.4.3 Development 83
2.5 Modeling and Simulation of Martian UAVs 85
2.5.1 Path Planning and Navigation 87
2.5.2 Image Processing and Data Analysis 88
2.5.3 Communication and Data Transmission 89
2.6 Conclusion and Future Scope 89
References 90
3 IoT-Enabled UAV: A Comprehensive Review of Technological Change in Indian Farming 93
Rahul Joshi and Krishna Pandey
3.1 Introduction 94
3.1.1 Indian Perspective on Drone Technology 95
3.2 Utilization of Drones in Agricultural Practices 97
3.3 Types of Drones and Sensors 101
3.3.1 Drones Based on Design 101
3.3.2 Drones Based on Weight 103
3.3.3 Drones Based on Sensors 105
3.4 Agricultural Drone Industry in India 107
3.4.1 An Overview of India’s Farming Drone Business 108
3.4.2 Major Organizations in India’s Agricultural Drone Industry 109
3.5 Competitive Analysis of the Drone Market in the Agriculture Sector in India 113
3.5.1 Prominent International Stakeholders 113
3.5.2 Strategic Approach Used by Market Players 114
3.5.3 Newest Trends in the Indian Market 116
3.5.4 Barriers to Entry in the Indian Market 118
3.6 Revenue and Growth of the Indian Drone Market 120
3.6.1 Past Revenue Patterns and Future Growth Forecasts for the Drone Industry in the Farming Sector 121
3.6.2 Revenue-Growing Components 121
3.7 Successful Case Studies of Agriculture Drone in India 123
3.8 Regulatory Frameworks Impacting the Use of Drones in Agriculture 126
3.8.1 Directorate General of Civil Aviation Guidelines for Farming Drones 126
3.8.2 Restricted Zone for Drone Flying in India 128
3.9 Conclusion and Future Directions 130
References 131
4 Applications of AI in UAVs Using In-Flight Parameters 137
Yogesh Beeharry and Raviduth Ramful
4.1 Introduction 138
4.1.1 UAV Technology 139
4.1.2 UAV Navigation Technology 141
4.1.2.1 Autonomous Navigation Systems 142
4.1.3 Artificial Intelligence for UAV Navigation 145
4.1.4 Regression-Based Predictive Models 146
4.1.4.1 Linear Regression 146
4.1.4.2 Regression Decision Tree 146
4.1.4.3 Ensemble of Regression Learners 148
4.1.4.4 Gaussian Process Regression 148
4.1.4.5 Kernel Regression 148
4.1.4.6 Regression Neural Network 149
4.1.4.7 Regression Support Vector Machine 150
4.2 Methodology 151
4.2.1 Existing Datasets for UAV Navigation 151
4.2.1.1 UAV Delivery Dataset 151
4.2.1.2 Hull Drone Indoor Navigation (HDIN) Dataset 151
4.2.1.3 UAVVAste Dataset 151
4.2.2 Selected Dataset 151
4.2.3 System Model 153
4.3 Results for Instantaneous Power versus Wind Speed 154
4.3.1 Linear Regression Model 154
4.3.2 Regression Decision Tree Model 155
4.3.3 Ensemble of Regression Learners Model 157
4.3.4 Gaussian Process Regression Model 158
4.3.5 Kernel Regression Model 159
4.3.6 Regression Neural Network Model 161
4.3.7 Regression Support Vector Machine 162
4.4 Results for Instantaneous Power versus Wind Speed and Wind Angle 163
4.4.1 Linear Regression Model 163
4.4.2 Regression Decision Tree Model 165
4.4.3 Ensemble of Regression Learners Model 166
4.4.4 Gaussian Process Regression Model 168
4.4.5 Kernel Regression Model 169
4.4.6 Regression Neural Network Model 170
4.4.7 Regression Support Vector Machine Model 171
4.5 Comparative Analysis of Results 174
4.6 Conclusion and Future Scope 174
References 175
5 AVFD: Autonomous Vision-Based Fleet Management for Drone Delivery Optimization in E-Commerce 181
Vu Duy Trung, Phuong Anh Nguyen, Toh Yan Chi, Phung Thao Vi, Satyam Mishra and Le Anh Ngoc
5.1 Introduction 182
5.2 Literature Review 185
5.2.1 Overview of Drone Technology in E-Commerce 185
5.2.2 Current Challenges in Drone Fleet Management for Last-Mile Delivery 186
5.2.3 State-of-the-Art Machine Learning Algorithms for Drone Optimization 187
5.2.4 Previous Studies on Face-Tracking and Line-Follower Drones 189
5.3 Methodology 192
5.3.1 Research Design and Approach 192
5.3.2 Data Collection and Sources 193
5.3.3 Programming Process 197
5.3.4 Experimental Setup for Face-Tracking Drone Development 199
5.3.5 Experimental Setup for Line-Follower Drone Development 204
5.4 Results and Discussion 208
5.4.1 Performance Analysis of Face-Tracker Drone 208
5.4.2 Performance Analysis of Line-Follower Drone 211
5.4.3 Comparison with Existing Solutions 213
5.4.4 Interpretation of Findings 214
5.5 Conclusion and Future Scope 215
References 218
6 STEDSDR: Simulated Testing and Evaluation of Drone Surveillance for Disaster Response 225
Yan Chi Toh, Phuong Anh Nguyen, Satyam Mishra, Vu Duy Trung, Phung Thao Vi and Le Anh Ngoc
6.1 Introduction 226
6.2 Literature Review 229
6.3 Research Methodology 231
6.3.1 Research Design 231
6.3.2 Test Case Development 231
6.3.3 Drone Platform and Equipment 232
6.3.4 Surveillance and Mapping Software 234
6.3.5 Test Execution 234
6.3.6 Data Analysis 236
6.3.7 Ethical Considerations 237
6.3.8 Drone Surveillance 237
6.3.9 Drone Mapping 239
6.4 Data Collection and Analysis 241
6.4.1 Data Collection 241
6.4.2 Quantitative Analysis 247
6.4.3 Key Results 251
6.5 Results and Discussion 252
6.6 Conclusion, Recommendations, and Future Scope 255
References 258
7 Review on Assessment of Land Degradation in Watershed Using Geospatial Technique Based on Unmanned Aircraft Systems 263
Soumya Pandey, Neeta Kumari and Lovely Mallick
7.1 Introduction 264
7.1.1 Global Initiatives Towards Land Degradation 267
7.2 Processes of Land Degradation 269
7.2.1 Soil Loss 269
7.2.2 Land Use Land Cover 271
7.2.3 Climate Change 273
7.2.4 Hydrological Cycles 274
7.2.5 Salinization 275
7.2.6 Heavy Metal Pollution 275
7.2.7 Plastic Pollution 276
7.3 Geospatial Application in Addressing the Land Degradation 277
7.4 Components of Unmanned Aircraft Systems (UASs) 281
7.5 Data Collection and Processing for UAVs 283
7.5.1 Pre-Flight Planning 283
7.5.2 Sensors 284
7.5.2.1 Optical Sensors 285
7.5.2.2 Fluorescence Sensors 285
7.5.2.3 Thermal Infrared Sensors 286
7.5.2.4 LiDAR Sensors 286
7.5.2.5 Gas Sensors 287
7.5.2.6 Photogrammetric Sensors 288
7.5.3 Platforms - Advantages and Disadvantages 289
7.5.3.1 Fixed-Wing UAS 289
7.5.3.2 Multirotor UAS 290
7.5.3.3 Hybrid UAS 292
7.5.3.4 Tethered UAS 294
7.6 Advantages of UAS Integrated with GIS for Land Degradation Monitoring 295
7.6.1 Selection of UAS 296
7.7 Application of UAV in Land Degradation Monitoring and Assessment 297
7.8 Conclusion and Future Scope 298
References 299
8 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 313
Bhupinder Singh
8.1 Introduction 314
8.1.1 Significance of Unmanned Aircraft Systems (UASs): Exponential Growth Across Industries 315
8.1.2 Unmanned Aircraft Systems (UASs): High Sky Exploring the Strange New World 317
8.1.3 Scope of the Chapter 319
8.2 Evolution of Unmanned Aircraft Systems: Origin and Widespread Applications in Commercial and Civilian Sectors 322
8.2.1 Motivations for UAS Assimilation 325
8.3 Surveillance Applications and Ethical Considerations: Advantages and Challenges Associated with Surveillance Operations 326
8.4 Risk Management and Safety Aspects within the UAS Ecosystem 328
8.5 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 331
8.6 Legal and Regulatory Framework: Airspace Integration and Challenges of Creating Adaptable Frameworks to Accommodate Evolving UAS Technologies 334
8.7 Benefits of UAS Adoption: Economic, Environmental, and Societal Advantages to Enhance Efficiency and Reduce Costs via Contributing Toward Agriculture, Logistics, and Disaster Management 337
8.8 Challenges and Mitigation Strategies: UAS Integration and Offer Strategies to Mitigate Issues of Privacy Concerns, Regulatory Hurdles, Technological Limitations, and Public Perception 341
8.8.1 International Collaboration and Standardization 344
8.8.2 Ethical Considerations and Societal Implications 345
8.9 Conclusion and Future Scope 346
References 348
9 Navigating the Future: Unmanned Aerial Systems in IoT Paradigms 355
Chandrakant Mahobiya, Sailesh Iyer, Mahendra Verma, Prabhat Ranjan Mishra and Shailendra Kumar Bohidar
9.1 Introduction 356
9.1.1 Setting the Stage 356
9.1.2 Importance of the Convergence 357
9.2 The Anatomy of UAS and IoT 358
9.2.1 Understanding UAS 359
9.2.2 Capabilities 363
9.2.3 Classifications 364
9.2.4 Exploring IoT 364
9.2.5 Architecture 365
9.2.5.1 Device Layer 365
9.2.5.2 Communication Layer 365
9.2.5.3 Data Processing Layer 366
9.2.5.4 Application Layer 366
9.2.6 Type of Devices 367
9.2.7 UAS as IoT Nodes 367
9.2.8 History of UAS and IoT 368
9.2.8.1 Unmanned Aerial Systems (UASs) 368
9.2.8.2 Internet of Things (IoT) 369
9.3 Technical Infrastructure 370
9.3.1 Communication Protocols 370
9.3.1.1 LoRaWAN 370
9.3.1.2 25G 371
9.3.1.3 ZigBee 371
9.3.2 Data Management and Analytics 371
9.3.2.1 Edge Computing 372
9.3.2.2 Cloud Computing 373
9.3.2.3 Data Analytics 373
9.3.3 Security Measures 373
9.3.4 Types of Drones and Its Applications 374
9.4 Application and Use Cases 375
9.4.1 Agriculture 376
9.4.2 Public Safety 376
9.4.3 Industrial Inspection 377
9.4.4 Environmental Monitoring 377
9.4.5 Media and Entertainment 377
9.4.6 Delivery Services 377
9.4.7 Surveying and Mapping 378
9.4.8 Research and Development 378
9.5 Ethical and Legal Dimensions 378
9.5.1 Privacy Concerns 378
9.5.2 Regulatory Aspects 379
9.6 Challenges and Opportunities 379
9.6.1 Technological Obstacles 380
9.6.1.1 Battery Life 380
9.6.1.2 Range 381
9.6.1.3 Data Security 381
9.7 Conclusion and Future Scope 382
References 383
10 Dynamic Modeling and Designing Robust MIMO Controller for Rudderless Flying-Wing UAVs 387
Sevda Rezazadeh Movahhed and Mohammad Ali Hamed
10.1 Introduction 388
10.2 Literature Review 391
10.3 Materials and Methods 399
10.3.1 Physical Model of Rudderless Flying-Wing UAV 399
10.3.2 Coordinate System 400
10.3.3 Equations of Motion 401
10.3.4 Forces and Moments 402
10.3.5 Linearized Equations of Motion 403
10.3.5.1 Small-Disturbance Theory 403
10.3.5.2 Longitudinal and Lateral Motions 404
10.3.5.3 State-Space Form 404
10.3.6 LQG/LTR Method 406
10.4 Proposed Methodology: LQG/LTR Method 406
10.4.1 Optimal State Estimator: Kalman Filter 407
10.4.2 Optimal State Feedback Controller: LQR Method 407
10.4.3 Output Feedback Closed-Loop System 408
10.4.4 Loop Transfer Recovery 408
10.4.4.1 Kalman Filter-Based Adjustment Approach 409
10.4.4.2 LQR Controller-Based Adjustment Approach 410
10.5 Results and Discussion 411
10.5.1 Case Study 411
10.5.2 Longitudinal System Setup 413
10.5.3 Lateral System Setup 417
10.5.4 Tracking Behavior and Control Signals 418
10.5.4.1 Longitudinal Motion 419
10.5.4.2 Lateral Motion 420
10.5.5 Input Disturbance Rejection 421
10.6 Conclusion and Future Scope 423
References 424
11 Enhancing Security for Unmanned Aircraft Systems in IoT Environments: Defense Mechanisms and Mitigation Strategies 429
C.V. Suresh Babu and Abhinaba Pal
11.1 Introduction 430
11.1.1 Background 430
11.1.2 Objective of Chapter 431
11.1.3 Scope of the Chapter 433
11.2 Security Challenges in IoT-Enabled UAS 434
11.2.1 Complexity and Heterogeneity of IoT Systems 434
11.2.2 Distributed Nature and Access Control Issues 436
11.2.3 Authentication and Confidentiality Concerns 436
11.2.4 Data Protection and Firmware Security 437
11.3 Case Study: SkySoftware Incident 441
11.3.1 Exploiting an Unprotected Communications Link 441
11.3.2 Intercepting Live Video Feeds from U.S. Predator Drones 441
11.3.3 Implications of the Security Breach 443
11.4 GPS Spoofing Attacks on UAS 443
11.4.1 Equipment Used and Basic Functioning 444
11.4.2 Comprehending GPS Spoofing and Its Corresponding Techniques 447
11.4.3 Effects on UAS Navigation and Control 454
11.4.4 Limitations of GPS Spoofing and Mitigation Tactics 455
11.5 Sensor Based Attacks on UAS 457
11.5.1 Laser Attacks 457
11.5.2 Mitigation Strategies 461
11.6 Trust Architectures for UAS Security 462
11.6.1 Application Layer Defensive Security Mechanisms (e.g., MQTT, CoAP) 462
11.6.2 Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) Techniques for Secure Drone-to-Drone Communication 465
11.7 Subsequent Trends in UAS Security 469
11.7.1 A Machine Learning Approach Promoting UAS Edge-Security and Performance 469
11.8 Conclusion and Future Scope 470
References 472
12 Foldable Quadcopters: Design, Analysis, and Additive Manufacturing for Enhanced Aerial Mobility 477
Yash H. Thummar and Mohammad Irfan Alam
12.1 Background and Introduction 478
12.2 Design Methodology 483
12.2.1 Selection of Frame 484
12.2.2 Understanding the Flight Dynamics 486
12.2.3 Creating the Base 487
12.2.4 CAD Modeling 488
12.2.5 Quadcopter Foldable Arm Design 489
12.2.6 Thrust and Total Flight Time Calculation 491
12.3 Analysis of Design 492
12.3.1 Material Selection 493
12.3.2 Loads and Constraints Estimation 493
12.3.3 Static Stress Analysis 494
12.4 Fabrication Using 3D Printing 494
12.4.1 3D Printing Filament 496
12.4.2 CAD Part Slicing 497
12.4.3 Printing the Quadcopter Parts 500
12.5 Components and Assembly 500
12.6 Testing and Verification 506
12.7 Making to the First Flight 510
12.8 Discussions and Applications 512
12.9 Conclusions and Future Scope 513
References 514
13 A Perspective Analysis of UAV Flight Control Architecture Incorporating Ground Control Stations and Near-Actual Techniques 519
Imran Mir, Muhammad Amir Tahir and Suleman Mir
13.1 Introduction 520
13.2 UAV Dynamics and Control Algorithms 523
13.2.1 Flight Control Techniques 527
13.2.2 Stability and Robustness 529
13.3 Near-Actual Simulation Techniques 532
13.3.1 Model-in-Loop Simulation 533
13.3.2 Software-in-Loop Simulation 534
13.3.3 Processor-in-Loop Simulation 536
13.3.4 Hardware-in-Loop Simulation 537
13.4 Visualization Software 541
13.4.1 X-Plane 542
13.4.2 FlightGear 542
13.4.3 jMAVSim 544
13.4.4 Gazebo 544
13.5 Ground Control Station 545
13.5.1 QGroundControl 547
13.5.2 Mission Planner 547
13.5.3 Universal Ground Control Software 549
13.5.4 MAVProxy 549
13.6 Existing Challenges 550
13.7 Conclusion 552
13.7.1 Future Directions 552
References 554
14 Optimal Transportation System Based on Adaptive Federated Learning Techniques for Healthcare IoV (HIoV) 563
Pallati Narsimhulu, Rashmi Sahay and Premkumar Chithaluru
14.1 Introduction 564
14.2 Impacts of AI/ML/FL Techniques in HIoV 579
14.3 Research Challenges in IoV Transportation 592
14.4 Comparative Study 598
14.5 Conclusions and Future Scope 605
References 606
Index 609
