Vandana Mohindru PhD is an assistant professor in the Department of Computer Science and Engineering, Chandigarh Group of Colleges, Mohali, Punjab, India. Her research interests are in the areas of Internet of Things, wireless sensor networks, security, blockchain and cryptography, unmanned aerial vehicles. She has published more than 20 technical research papers in leading journals and conferences. Yashwant Singh PhD is an associate professor & Head in the Department of Computer Science & Information Technology at the Central University of Jammu. His research interests lie in the area of Internet of Things, wireless sensor networks, unmanned aerial vehicles, cybersecurity. He has published more than 70 research articles in the international journals and conferences. Ravindara Bhatt PhD is an assistant professor at Jaypee University of Information Technology, Solan, H.P., India. He has over 20 years of experience in academics and industry in India. He has published more than 30 research papers in leading journals and conferences. His areas of research include sensor networks, deployment modeling, communication, and energy-efficient algorithms, security and unmanned aerial vehicles. Anuj Kumar Gupta PhD is professor & Head in CSE at Chandigarh Group of Colleges, Mohali, Punjab, India. He has published 100+ research papers in reputed journals.

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• Preface xvii1 Unmanned Aerial Vehicle (UAV): A Comprehensive Survey 1Rohit Chaurasia and Vandana Mohindru1.1 Introduction 21.2 Related Work 21.3 UAV Technology 31.3.1 UAV Platforms 31.3.1.1 Fixed-Wing Drones 31.3.1.2 Multi-Rotor Drones 41.3.1.3 Single-Rotor Drones 51.3.1.4 Fixed-Wing Hybrid VTOL 61.3.2 Categories of the Military Drones 61.3.3 How Drones Work 81.3.3.1 Firmware—Platform Construction and Design 91.3.4 Comparison of Various Technologies 101.3.4.1 Drone Types & Sizes 101.3.4.2 Radar Positioning and Return to Home 101.3.4.3 GNSS on Ground Control Station 111.3.4.4 Collision Avoidance Technology and Obstacle Detection 111.3.4.5 Gyroscopic Stabilization, Flight Controllers and IMU 121.3.4.6 UAV Drone Propulsion System 121.3.4.7 Flight Parameters Through Telemetry 131.3.4.8 Drone Security & Hacking 131.3.4.9 3D Maps and Models With Drone Sensors 131.3.5 UAV Communication Network 151.3.5.1 Classification on the Basis of Spectrum Perspective 151.3.5.2 Various Types of Radio communication Links 161.3.5.3 VLOS (Visual Line-of-Sight) and BLOS (Beyond Line-of-Sight) Communication in Unmanned Aircraft System 181.3.5.4 Frequency Bands for the Operation of UAS 191.3.5.5 Cellular Technology for UAS Operation 191.4 Application of UAV 211.4.1 In Military 211.4.2 In Geomorphological Mapping and Other Similar Sectors 221.4.3 In Agriculture 221.5 UAV Challenges 231.6 Conclusion and Future Scope 24References 242 Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope 29Jolly Parikh and Anuradha Basu2.1 Introduction 302.2 Technical Challenges 302.2.1 Variations in Channel Characteristics 322.2.2 UAV-Assisted Cellular Network Planning and Provisioning 332.2.3 Millimeter Wave Cellular Connected UAVs 342.2.4 Deployment of UAV 352.2.5 Trajectory Optimization 362.2.6 On-Board Energy 372.3 Conclusion 37References 373 Battery and Energy Management in UAV-Based Networks 43Santosh Kumar, Amol Vasudeva and Manu Sood3.1 Introduction 433.2 The Need for Energy Management in UAV-Based Communication Networks 453.2.1 Unpredictable Trajectories of UAVs in Cellular UAV Networks 463.2.2 Non-Homogeneous Power Consumption 473.2.3 High Bandwidth Requirement/Low Spectrum Availability/Spectrum Scarcity 473.2.4 Short-Range Line-of-Sight Communication 483.2.5 Time Constraint (Time-Limited Spectrum Access) 483.2.6 Energy Constraint 493.2.7 The Joint Design for the Sensor Nodes’ Wake-Up Schedule and the UAV’s Trajectory (Data Collection) 493.3 Efficient Battery and Energy Management Proposed Techniques in Literature 503.3.1 Cognitive Radio (CR)-Based UAV Communication to Solve Spectrum Congestion 513.3.2 Compressed Sensing 523.3.3 Power Allocation and Position Optimization 533.3.4 Non-Orthogonal Multiple Access (NOMA) 533.3.5 Wireless Charging/Power Transfer (WPT) 543.3.6 UAV Trajectory Design Using a Reinforcement Learning Framework in a Decentralized Manner 553.3.7 Efficient Deployment and Movement of UAVs 553.3.8 3D Position Optimization Mixed With Resource Allocation to Overcome Spectrum Scarcity and Limited Energy Constraint 563.3.9 UAV-Enabled WSN: Energy-Efficient Data Collection 573.3.10 Trust Management 573.3.11 Self-Organization-Based Clustering 583.3.12 Bandwidth/Spectrum-Sharing Between UAVs 593.3.13 Using Millimeter Wave With SWIPT 593.3.14 Energy Harvesting 603.4 Conclusion 61References 674 Energy Efficient Communication Methods for Unmanned Ariel Vehicles (UAVs): Last Five Years’ Study 73Nagesh Kumar4.1 Introduction 734.1.1 Introduction to UAV 744.1.2 Communication in UAV 754.2 Literature Survey Process 774.2.1 Research Questions 774.2.2 Information Source 774.3 Routing in UAV 784.3.1 Communication Methods in UAV 784.3.1.1 Single-Hop Communication 794.3.1.2 Multi-Hop Communication 804.4 Challenges and Issues 824.4.1 Energy Consumption 824.4.2 Mobility of Devices 824.4.3 Density of UAVs 824.4.4 Changes in Topology 854.4.5 Propagation Models 854.4.6 Security in Routing 854.5 Conclusion 85References 865 A Review on Challenges and Threats to Unmanned Aerial Vehicles (UAVs) 89Shaik Johny Basha and Jagan Mohan Reddy Danda5.1 Introduction 895.2 Applications of UAVs and Their Market Opportunity 905.2.1 Applications 905.2.2 Market Opportunity 925.3 Attacks and Solutions to Unmanned Aerial Vehicles (UAVs) 925.3.1 Confidentiality Attacks 935.3.2 Integrity Attacks 955.3.3 Availability Attacks 965.3.4 Authenticity Attacks 975.4 Research Challenges 995.4.1 Security Concerns 995.4.2 Safety Concerns 995.4.3 Privacy Concerns 1005.4.4 Scalability Issues 1005.4.5 Limited Resources 1005.5 Conclusion 101References 1016 Internet of Things and UAV: An Interoperability Perspective 105Bharti Rana and Yashwant Singh6.1 Introduction 1066.2 Background 1086.2.1 Issues, Controversies, and Problems 1096.3 Internet of Things (IoT) and UAV 1106.4 Applications of UAV-Enabled IoT 1136.5 Research Issues in UAV-Enabled IoT 1146.6 High-Level UAV-Based IoT Architecture 1176.6.1 UAV Overview 1176.6.2 Enabling IoT Scalability 1196.6.3 Enabling IoT Intelligence 1206.6.4 Enabling Diverse IoT Applications 1216.7 Interoperability Issues in UAV-Based IoT 1216.8 Conclusion 123References 1247 Practices of Unmanned Aerial Vehicle (UAV) for Security Intelligence 129Swarnjeet Kaur, Kulwant Singh and Amanpreet Singh7.1 Introduction 1307.2 Military 1327.3 Attack 1337.4 Journalism 1347.5 Search and Rescue 1367.6 Disaster Relief 1387.7 Conclusion 139References 1398 Blockchain-Based Solutions for Various Security Issues in UAV-Enabled IoT 143Madhuri S. Wakode and Rajesh B. Ingle8.1 Introduction 1448.1.1 Organization of the Work 1458.2 Introduction to UAV and IoT 1458.2.1 UAV 1458.2.2 IoT 1468.2.3 UAV-Enabled IoT 1478.2.4 Blockchain 1508.3 Security and Privacy Issues in UAV-Enabled IoT 1518.4 Blockchain-Based Solutions to Various Security Issues 1538.5 Research Directions 1548.6 Conclusion 1548.7 Future Work 155References 1559 Efficient Energy Management Systems in UAV-Based IoT Networks 159V. Mounika Reddy, Neelima K. and G. Naresh9.1 Introduction 1609.2 Energy Harvesting Methods 1619.2.1 Basic Energy Harvesting Mechanisms 1629.2.2 Markov Decision Process-Based Energy Harvesting Mechanisms 1639.2.3 mm Wave Energy Harvesting Mechanism 1649.2.4 Full Duplex Wireless Energy Harvesting Mechanism 1659.3 Energy Recharge Methods 1659.4 Efficient Energy Utilization Methods 1669.4.1 GLRM Method 1669.4.2 DRL Mechanism 1679.4.3 Onboard Double Q-Learning Mechanism 1689.4.4 Collision-Free Scheduling Mechanism 1689.5 Conclusion 170References 17010 A Survey on IoE-Enabled Unmanned Aerial Vehicles 173K. Siddharthraju, R. Dhivyadevi, M. Supriya, B. Jaishankar and Shanmugaraja T.10.1 Introduction 17410.2 Overview of Internet of Everything 17610.2.1 Emergence of IoE 17610.2.2 Expectation of IoE 17710.2.2.1 Scalability 17710.2.2.2 Intelligence 17810.2.2.3 Diversity 17810.2.3 Possible Technologies 17910.2.3.1 Enabling Scalability 17910.2.3.2 Enabling Intelligence 18010.2.3.3 Enabling Diversity 18010.2.4 Challenges of IoE 18110.2.4.1 Coverage Constraint 18110.2.4.2 Battery Constraint 18110.2.4.3 Computing Constraint 18110.2.4.4 Security Constraint 18210.3 Overview of Unmanned Aerial Vehicle (UAV) 18210.3.1 Unmanned Aircraft System (UAS) 18310.3.2 UAV Communication Networks 18310.3.2.1 Ad Hoc Multi-UAV Networks 18310.3.2.2 UAV-Aided Communication Networks 18410.4 UAV and IoE Integration 18410.4.1 Possibilities to Carry UAVs 18410.4.1.1 Widespread Connectivity 18510.4.1.2 Environmentally Aware 18510.4.1.3 Peer-Maintenance of Communications 18510.4.1.4 Detector Control and Reusing 18510.4.2 UAV-Enabled IoE 18610.4.3 Vehicle Detection Enabled IoE Optimization 18610.4.3.1 Weak-Connected Locations 18610.4.3.2 Regions with Low Network Support 18610.5 Open Research Issues 18710.6 Discussion 18710.6.1 Resource Allocation 18710.6.2 Universal Standard Design 18810.6.3 Security Mechanism 18810.7 Conclusion 189References 18911 Role of AI and Big Data Analytics in UAV-Enabled IoT Applications for Smart Cities 193Madhuri S. Wakode11.1 Introduction 19411.1.1 Related Work 19511.1.2 Contributions 19511.1.3 Organization of the Work 19511.2 Overview of UAV-Enabled IoT Systems 19611.2.1 UAV-Enabled IoT Systems for Smart Cities 19711.3 Overview of Big Data Analytics 19711.4 Big Data Analytics Requirements in UAV-Enabled IoT Systems 19811.4.1 Big Data Analytics in UAV-Enabled IoT Applications 19911.4.2 Big Data Analytics for Governance of UAV-Enabled IoT Systems 20111.5 Challenges 20211.6 Conclusion 20211.7 Future Work 203References 20312 Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module 207Lakshit Kohli, Manglesh Saurabh, Ishaan Bhatia, Nidhi Sindhwani and Manjula Vijh12.1 Introduction 20812.2 Existing System 20812.3 Proposed System 21012.4 IoT Sensors and Architecture 21212.4.1 Sensors and Theory 21212.4.2 Architectures Available 21312.4.2.1 3-Layer IoT Architecture 21312.4.2.2 5-Layer IoT Architecture 21412.4.2.3 Architecture & Supporting Modules 21512.4.2.4 Integration Approach 21512.4.2.5 System of Modules 21612.5 Advantages of the Proposed System 21712.6 Design 21812.6.1 System Design 21912.6.2 Auto-Leveling 21912.6.3 Amphibious Landing Module 22112.6.4 Processing Module 22312.6.5 Surround Sense Module 22312.7 Results 22412.8 Conclusion 22712.9 Future Scope 228References 22813 Mind Controlled Unmanned Aerial Vehicle (UAV) Using Brain–Computer Interface (BCI) 231Prasath M.S., Naveen R. and Sivaraj G.13.1 Introduction 23213.1.1 Classification of UAVs 23213.1.2 Drone Controlling 23213.2 Mind-Controlled UAV With BCI Technology 23313.3 Layout and Architecture of BCI Technology 23413.4 Hardware Components 23513.4.1 Neurosky Mindwave Headset 23513.4.2 Microcontroller Board—Arduino 23613.4.3 A Computer 23713.4.4 Drone for Quadcopter 23813.5 Software Components 23913.5.1 Processing P3 Software 23913.5.2 Arduino IDE Software 24013.5.3 ThinkGear Connector 24013.6 Hardware and Software Integration 24113.7 Conclusion 243References 24414 Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0 247Dhirendra Siddharth, Dilip Kumar Saini and Ajay Kumar14.1 Introduction 24714.2 Drone Technology as an Instrument for Increasing Farm Productivity 24814.3 Mapping and Tracking of Rice Farm Areas With Information and Communication Technology (ICT) and Remote Sensing Technology 24914.3.1 Methodology and Development of ICT 25014.4 Strong Intelligence From UAV to the Agricultural Sector 25214.4.1 Latest Agricultural Drone History 25214.4.2 The Challenges 25414.4.3 SAP’s Next Wave of Drone Technologies 25414.4.4 SAP Connected Agriculture 25614.4.5 Cases of Real-World Use 25714.4.5.1 Crop Surveying 25714.4.5.2 Capture the Plantation 25814.4.5.3 Image Processing 25814.4.5.4 Working to Create GeoTiles and an Image Pyramid 25914.5 Drones-Based Sensor Platforms 26014.5.1 Context and Challenges 26014.5.2 Stakeholder and End Consumer Benefits 26114.5.3 The Technology 26214.5.3.1 Provisions of the Unmanned Aerial Vehicles 26214.6 Jobs of Space Technology in Crop Insurance 26314.7 The Institutionalization of Drone Imaging Technologies in Agriculture for Disaster Managing Risk 26714.7.1 A Modern Working 26714.7.2 Discovering Drone Mapping Technology 26814.7.3 From Lowland to Uplands, Drone Mapping Technology 26914.7.4 Institutionalization of Drone Monitoring Systems and Farming Capability 26914.8 Usage of Internet of Things in Agriculture and Use of Unmanned Aerial Vehicles 27014.8.1 System and Application Based on UAV-WSN 27014.8.2 Using a Complex Comprehensive System 27114.8.3 Benefits Assessment of Conventional System and the UAV-Based System 27114.8.3.1 Merit 27214.8.3.2 Saving Expenses 27214.8.3.3 Traditional Agriculture 27314.8.3.4 UAV-WSN System-Based Agriculture 27314.9 Conclusion 273References 27315 IoT-Based UAV Platform Revolutionized in Smart Healthcare 277Umesh Kumar Gera, Dilip Kumar Saini, Preeti Singh and Dhirendra Siddharth15.1 Introduction 27815.2 IoT-Based UAV Platform for Emergency Services 27915.3 Healthcare Internet of Things: Technologies, Advantages 28115.3.1 Advantage 28115.3.1.1 Concurrent Surveillance and Tracking 28115.3.1.2 From End-To-End Networking and Availability 28215.3.1.3 Information and Review Assortment 28215.3.1.4 Warnings and Recording 28215.3.1.5 Wellbeing Remote Assistance 28315.3.1.6 Research 28315.3.2 Complications 28315.3.2.1 Privacy and Data Security 28315.3.2.2 Integration: Various Protocols and Services 28415.3.2.3 Overload and Accuracy of Data 28415.3.2.4 Expenditure 28415.4 Healthcare’s IoT Applications: Surgical and Medical Applications of Drones 28515.4.1 Hearables 28515.4.2 Ingestible Sensors 28515.4.3 Moodables 28515.4.4 Technology of Computer Vision 28615.4.5 Charting for Healthcare 28615.5 Drones That Will Revolutionize Healthcare 28615.5.1 Integrated Enhancement in Efficiency 28615.5.2 Offering Personalized Healthcare 28715.5.3 The Big Data Manipulation 28715.5.4 Safety and Privacy Optimization 28715.5.5 Enabling M2M Communication 28815.6 Healthcare Revolutionizing Drones 28815.6.1 Google Drones 28815.6.2 Healthcare Integrated Rescue Operations (HiRO) 28915.6.3 EHang 28915.6.4 TU Delft 28915.6.5 Project Wing 28915.6.6 Flirtey 28915.6.7 Seattle’s VillageReach 29015.6.8 ZipLine 29015.7 Conclusion 290References 290Index 295

Vandana Mohindru PhD is an assistant professor in the Department of Computer Science and Engineering, Chandigarh Group of Colleges, Mohali, Punjab, India. Her research interests are in the areas of Internet of Things, wireless sensor networks, security, blockchain and cryptography, unmanned aerial vehicles. She has published more than 20 technical research papers in leading journals and conferences. Yashwant Singh PhD is an associate professor & Head in the Department of Computer Science & Information Technology at the Central University of Jammu. His research interests lie in the area of Internet of Things, wireless sensor networks, unmanned aerial vehicles, cybersecurity. He has published more than 70 research articles in the international journals and conferences. Ravindara Bhatt PhD is an assistant professor at Jaypee University of Information Technology, Solan, H.P., India. He has over 20 years of experience in academics and industry in India. He has published more than 30 research papers in leading journals and conferences. His areas of research include sensor networks, deployment modeling, communication, and energy-efficient algorithms, security and unmanned aerial vehicles. Anuj Kumar Gupta PhD is professor & Head in CSE at Chandigarh Group of Colleges, Mohali, Punjab, India. He has published 100+ research papers in reputed journals.

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• Preface xvii1 Unmanned Aerial Vehicle (UAV): A Comprehensive Survey 1Rohit Chaurasia and Vandana Mohindru1.1 Introduction 21.2 Related Work 21.3 UAV Technology 31.3.1 UAV Platforms 31.3.1.1 Fixed-Wing Drones 31.3.1.2 Multi-Rotor Drones 41.3.1.3 Single-Rotor Drones 51.3.1.4 Fixed-Wing Hybrid VTOL 61.3.2 Categories of the Military Drones 61.3.3 How Drones Work 81.3.3.1 Firmware—Platform Construction and Design 91.3.4 Comparison of Various Technologies 101.3.4.1 Drone Types & Sizes 101.3.4.2 Radar Positioning and Return to Home 101.3.4.3 GNSS on Ground Control Station 111.3.4.4 Collision Avoidance Technology and Obstacle Detection 111.3.4.5 Gyroscopic Stabilization, Flight Controllers and IMU 121.3.4.6 UAV Drone Propulsion System 121.3.4.7 Flight Parameters Through Telemetry 131.3.4.8 Drone Security & Hacking 131.3.4.9 3D Maps and Models With Drone Sensors 131.3.5 UAV Communication Network 151.3.5.1 Classification on the Basis of Spectrum Perspective 151.3.5.2 Various Types of Radio communication Links 161.3.5.3 VLOS (Visual Line-of-Sight) and BLOS (Beyond Line-of-Sight) Communication in Unmanned Aircraft System 181.3.5.4 Frequency Bands for the Operation of UAS 191.3.5.5 Cellular Technology for UAS Operation 191.4 Application of UAV 211.4.1 In Military 211.4.2 In Geomorphological Mapping and Other Similar Sectors 221.4.3 In Agriculture 221.5 UAV Challenges 231.6 Conclusion and Future Scope 24References 242 Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope 29Jolly Parikh and Anuradha Basu2.1 Introduction 302.2 Technical Challenges 302.2.1 Variations in Channel Characteristics 322.2.2 UAV-Assisted Cellular Network Planning and Provisioning 332.2.3 Millimeter Wave Cellular Connected UAVs 342.2.4 Deployment of UAV 352.2.5 Trajectory Optimization 362.2.6 On-Board Energy 372.3 Conclusion 37References 373 Battery and Energy Management in UAV-Based Networks 43Santosh Kumar, Amol Vasudeva and Manu Sood3.1 Introduction 433.2 The Need for Energy Management in UAV-Based Communication Networks 453.2.1 Unpredictable Trajectories of UAVs in Cellular UAV Networks 463.2.2 Non-Homogeneous Power Consumption 473.2.3 High Bandwidth Requirement/Low Spectrum Availability/Spectrum Scarcity 473.2.4 Short-Range Line-of-Sight Communication 483.2.5 Time Constraint (Time-Limited Spectrum Access) 483.2.6 Energy Constraint 493.2.7 The Joint Design for the Sensor Nodes’ Wake-Up Schedule and the UAV’s Trajectory (Data Collection) 493.3 Efficient Battery and Energy Management Proposed Techniques in Literature 503.3.1 Cognitive Radio (CR)-Based UAV Communication to Solve Spectrum Congestion 513.3.2 Compressed Sensing 523.3.3 Power Allocation and Position Optimization 533.3.4 Non-Orthogonal Multiple Access (NOMA) 533.3.5 Wireless Charging/Power Transfer (WPT) 543.3.6 UAV Trajectory Design Using a Reinforcement Learning Framework in a Decentralized Manner 553.3.7 Efficient Deployment and Movement of UAVs 553.3.8 3D Position Optimization Mixed With Resource Allocation to Overcome Spectrum Scarcity and Limited Energy Constraint 563.3.9 UAV-Enabled WSN: Energy-Efficient Data Collection 573.3.10 Trust Management 573.3.11 Self-Organization-Based Clustering 583.3.12 Bandwidth/Spectrum-Sharing Between UAVs 593.3.13 Using Millimeter Wave With SWIPT 593.3.14 Energy Harvesting 603.4 Conclusion 61References 674 Energy Efficient Communication Methods for Unmanned Ariel Vehicles (UAVs): Last Five Years’ Study 73Nagesh Kumar4.1 Introduction 734.1.1 Introduction to UAV 744.1.2 Communication in UAV 754.2 Literature Survey Process 774.2.1 Research Questions 774.2.2 Information Source 774.3 Routing in UAV 784.3.1 Communication Methods in UAV 784.3.1.1 Single-Hop Communication 794.3.1.2 Multi-Hop Communication 804.4 Challenges and Issues 824.4.1 Energy Consumption 824.4.2 Mobility of Devices 824.4.3 Density of UAVs 824.4.4 Changes in Topology 854.4.5 Propagation Models 854.4.6 Security in Routing 854.5 Conclusion 85References 865 A Review on Challenges and Threats to Unmanned Aerial Vehicles (UAVs) 89Shaik Johny Basha and Jagan Mohan Reddy Danda5.1 Introduction 895.2 Applications of UAVs and Their Market Opportunity 905.2.1 Applications 905.2.2 Market Opportunity 925.3 Attacks and Solutions to Unmanned Aerial Vehicles (UAVs) 925.3.1 Confidentiality Attacks 935.3.2 Integrity Attacks 955.3.3 Availability Attacks 965.3.4 Authenticity Attacks 975.4 Research Challenges 995.4.1 Security Concerns 995.4.2 Safety Concerns 995.4.3 Privacy Concerns 1005.4.4 Scalability Issues 1005.4.5 Limited Resources 1005.5 Conclusion 101References 1016 Internet of Things and UAV: An Interoperability Perspective 105Bharti Rana and Yashwant Singh6.1 Introduction 1066.2 Background 1086.2.1 Issues, Controversies, and Problems 1096.3 Internet of Things (IoT) and UAV 1106.4 Applications of UAV-Enabled IoT 1136.5 Research Issues in UAV-Enabled IoT 1146.6 High-Level UAV-Based IoT Architecture 1176.6.1 UAV Overview 1176.6.2 Enabling IoT Scalability 1196.6.3 Enabling IoT Intelligence 1206.6.4 Enabling Diverse IoT Applications 1216.7 Interoperability Issues in UAV-Based IoT 1216.8 Conclusion 123References 1247 Practices of Unmanned Aerial Vehicle (UAV) for Security Intelligence 129Swarnjeet Kaur, Kulwant Singh and Amanpreet Singh7.1 Introduction 1307.2 Military 1327.3 Attack 1337.4 Journalism 1347.5 Search and Rescue 1367.6 Disaster Relief 1387.7 Conclusion 139References 1398 Blockchain-Based Solutions for Various Security Issues in UAV-Enabled IoT 143Madhuri S. Wakode and Rajesh B. Ingle8.1 Introduction 1448.1.1 Organization of the Work 1458.2 Introduction to UAV and IoT 1458.2.1 UAV 1458.2.2 IoT 1468.2.3 UAV-Enabled IoT 1478.2.4 Blockchain 1508.3 Security and Privacy Issues in UAV-Enabled IoT 1518.4 Blockchain-Based Solutions to Various Security Issues 1538.5 Research Directions 1548.6 Conclusion 1548.7 Future Work 155References 1559 Efficient Energy Management Systems in UAV-Based IoT Networks 159V. Mounika Reddy, Neelima K. and G. Naresh9.1 Introduction 1609.2 Energy Harvesting Methods 1619.2.1 Basic Energy Harvesting Mechanisms 1629.2.2 Markov Decision Process-Based Energy Harvesting Mechanisms 1639.2.3 mm Wave Energy Harvesting Mechanism 1649.2.4 Full Duplex Wireless Energy Harvesting Mechanism 1659.3 Energy Recharge Methods 1659.4 Efficient Energy Utilization Methods 1669.4.1 GLRM Method 1669.4.2 DRL Mechanism 1679.4.3 Onboard Double Q-Learning Mechanism 1689.4.4 Collision-Free Scheduling Mechanism 1689.5 Conclusion 170References 17010 A Survey on IoE-Enabled Unmanned Aerial Vehicles 173K. Siddharthraju, R. Dhivyadevi, M. Supriya, B. Jaishankar and Shanmugaraja T.10.1 Introduction 17410.2 Overview of Internet of Everything 17610.2.1 Emergence of IoE 17610.2.2 Expectation of IoE 17710.2.2.1 Scalability 17710.2.2.2 Intelligence 17810.2.2.3 Diversity 17810.2.3 Possible Technologies 17910.2.3.1 Enabling Scalability 17910.2.3.2 Enabling Intelligence 18010.2.3.3 Enabling Diversity 18010.2.4 Challenges of IoE 18110.2.4.1 Coverage Constraint 18110.2.4.2 Battery Constraint 18110.2.4.3 Computing Constraint 18110.2.4.4 Security Constraint 18210.3 Overview of Unmanned Aerial Vehicle (UAV) 18210.3.1 Unmanned Aircraft System (UAS) 18310.3.2 UAV Communication Networks 18310.3.2.1 Ad Hoc Multi-UAV Networks 18310.3.2.2 UAV-Aided Communication Networks 18410.4 UAV and IoE Integration 18410.4.1 Possibilities to Carry UAVs 18410.4.1.1 Widespread Connectivity 18510.4.1.2 Environmentally Aware 18510.4.1.3 Peer-Maintenance of Communications 18510.4.1.4 Detector Control and Reusing 18510.4.2 UAV-Enabled IoE 18610.4.3 Vehicle Detection Enabled IoE Optimization 18610.4.3.1 Weak-Connected Locations 18610.4.3.2 Regions with Low Network Support 18610.5 Open Research Issues 18710.6 Discussion 18710.6.1 Resource Allocation 18710.6.2 Universal Standard Design 18810.6.3 Security Mechanism 18810.7 Conclusion 189References 18911 Role of AI and Big Data Analytics in UAV-Enabled IoT Applications for Smart Cities 193Madhuri S. Wakode11.1 Introduction 19411.1.1 Related Work 19511.1.2 Contributions 19511.1.3 Organization of the Work 19511.2 Overview of UAV-Enabled IoT Systems 19611.2.1 UAV-Enabled IoT Systems for Smart Cities 19711.3 Overview of Big Data Analytics 19711.4 Big Data Analytics Requirements in UAV-Enabled IoT Systems 19811.4.1 Big Data Analytics in UAV-Enabled IoT Applications 19911.4.2 Big Data Analytics for Governance of UAV-Enabled IoT Systems 20111.5 Challenges 20211.6 Conclusion 20211.7 Future Work 203References 20312 Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module 207Lakshit Kohli, Manglesh Saurabh, Ishaan Bhatia, Nidhi Sindhwani and Manjula Vijh12.1 Introduction 20812.2 Existing System 20812.3 Proposed System 21012.4 IoT Sensors and Architecture 21212.4.1 Sensors and Theory 21212.4.2 Architectures Available 21312.4.2.1 3-Layer IoT Architecture 21312.4.2.2 5-Layer IoT Architecture 21412.4.2.3 Architecture & Supporting Modules 21512.4.2.4 Integration Approach 21512.4.2.5 System of Modules 21612.5 Advantages of the Proposed System 21712.6 Design 21812.6.1 System Design 21912.6.2 Auto-Leveling 21912.6.3 Amphibious Landing Module 22112.6.4 Processing Module 22312.6.5 Surround Sense Module 22312.7 Results 22412.8 Conclusion 22712.9 Future Scope 228References 22813 Mind Controlled Unmanned Aerial Vehicle (UAV) Using Brain–Computer Interface (BCI) 231Prasath M.S., Naveen R. and Sivaraj G.13.1 Introduction 23213.1.1 Classification of UAVs 23213.1.2 Drone Controlling 23213.2 Mind-Controlled UAV With BCI Technology 23313.3 Layout and Architecture of BCI Technology 23413.4 Hardware Components 23513.4.1 Neurosky Mindwave Headset 23513.4.2 Microcontroller Board—Arduino 23613.4.3 A Computer 23713.4.4 Drone for Quadcopter 23813.5 Software Components 23913.5.1 Processing P3 Software 23913.5.2 Arduino IDE Software 24013.5.3 ThinkGear Connector 24013.6 Hardware and Software Integration 24113.7 Conclusion 243References 24414 Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0 247Dhirendra Siddharth, Dilip Kumar Saini and Ajay Kumar14.1 Introduction 24714.2 Drone Technology as an Instrument for Increasing Farm Productivity 24814.3 Mapping and Tracking of Rice Farm Areas With Information and Communication Technology (ICT) and Remote Sensing Technology 24914.3.1 Methodology and Development of ICT 25014.4 Strong Intelligence From UAV to the Agricultural Sector 25214.4.1 Latest Agricultural Drone History 25214.4.2 The Challenges 25414.4.3 SAP’s Next Wave of Drone Technologies 25414.4.4 SAP Connected Agriculture 25614.4.5 Cases of Real-World Use 25714.4.5.1 Crop Surveying 25714.4.5.2 Capture the Plantation 25814.4.5.3 Image Processing 25814.4.5.4 Working to Create GeoTiles and an Image Pyramid 25914.5 Drones-Based Sensor Platforms 26014.5.1 Context and Challenges 26014.5.2 Stakeholder and End Consumer Benefits 26114.5.3 The Technology 26214.5.3.1 Provisions of the Unmanned Aerial Vehicles 26214.6 Jobs of Space Technology in Crop Insurance 26314.7 The Institutionalization of Drone Imaging Technologies in Agriculture for Disaster Managing Risk 26714.7.1 A Modern Working 26714.7.2 Discovering Drone Mapping Technology 26814.7.3 From Lowland to Uplands, Drone Mapping Technology 26914.7.4 Institutionalization of Drone Monitoring Systems and Farming Capability 26914.8 Usage of Internet of Things in Agriculture and Use of Unmanned Aerial Vehicles 27014.8.1 System and Application Based on UAV-WSN 27014.8.2 Using a Complex Comprehensive System 27114.8.3 Benefits Assessment of Conventional System and the UAV-Based System 27114.8.3.1 Merit 27214.8.3.2 Saving Expenses 27214.8.3.3 Traditional Agriculture 27314.8.3.4 UAV-WSN System-Based Agriculture 27314.9 Conclusion 273References 27315 IoT-Based UAV Platform Revolutionized in Smart Healthcare 277Umesh Kumar Gera, Dilip Kumar Saini, Preeti Singh and Dhirendra Siddharth15.1 Introduction 27815.2 IoT-Based UAV Platform for Emergency Services 27915.3 Healthcare Internet of Things: Technologies, Advantages 28115.3.1 Advantage 28115.3.1.1 Concurrent Surveillance and Tracking 28115.3.1.2 From End-To-End Networking and Availability 28215.3.1.3 Information and Review Assortment 28215.3.1.4 Warnings and Recording 28215.3.1.5 Wellbeing Remote Assistance 28315.3.1.6 Research 28315.3.2 Complications 28315.3.2.1 Privacy and Data Security 28315.3.2.2 Integration: Various Protocols and Services 28415.3.2.3 Overload and Accuracy of Data 28415.3.2.4 Expenditure 28415.4 Healthcare’s IoT Applications: Surgical and Medical Applications of Drones 28515.4.1 Hearables 28515.4.2 Ingestible Sensors 28515.4.3 Moodables 28515.4.4 Technology of Computer Vision 28615.4.5 Charting for Healthcare 28615.5 Drones That Will Revolutionize Healthcare 28615.5.1 Integrated Enhancement in Efficiency 28615.5.2 Offering Personalized Healthcare 28715.5.3 The Big Data Manipulation 28715.5.4 Safety and Privacy Optimization 28715.5.5 Enabling M2M Communication 28815.6 Healthcare Revolutionizing Drones 28815.6.1 Google Drones 28815.6.2 Healthcare Integrated Rescue Operations (HiRO) 28915.6.3 EHang 28915.6.4 TU Delft 28915.6.5 Project Wing 28915.6.6 Flirtey 28915.6.7 Seattle’s VillageReach 29015.6.8 ZipLine 29015.7 Conclusion 290References 290Index 295

Vandana Mohindru PhD is an assistant professor in the Department of Computer Science and Engineering, Chandigarh Group of Colleges, Mohali, Punjab, India. Her research interests are in the areas of Internet of Things, wireless sensor networks, security, blockchain and cryptography, unmanned aerial vehicles. She has published more than 20 technical research papers in leading journals and conferences. Yashwant Singh PhD is an associate professor & Head in the Department of Computer Science & Information Technology at the Central University of Jammu. His research interests lie in the area of Internet of Things, wireless sensor networks, unmanned aerial vehicles, cybersecurity. He has published more than 70 research articles in the international journals and conferences. Ravindara Bhatt PhD is an assistant professor at Jaypee University of Information Technology, Solan, H.P., India. He has over 20 years of experience in academics and industry in India. He has published more than 30 research papers in leading journals and conferences. His areas of research include sensor networks, deployment modeling, communication, and energy-efficient algorithms, security and unmanned aerial vehicles. Anuj Kumar Gupta PhD is professor & Head in CSE at Chandigarh Group of Colleges, Mohali, Punjab, India. He has published 100+ research papers in reputed journals.

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• Preface xvii1 Unmanned Aerial Vehicle (UAV): A Comprehensive Survey 1Rohit Chaurasia and Vandana Mohindru1.1 Introduction 21.2 Related Work 21.3 UAV Technology 31.3.1 UAV Platforms 31.3.1.1 Fixed-Wing Drones 31.3.1.2 Multi-Rotor Drones 41.3.1.3 Single-Rotor Drones 51.3.1.4 Fixed-Wing Hybrid VTOL 61.3.2 Categories of the Military Drones 61.3.3 How Drones Work 81.3.3.1 Firmware—Platform Construction and Design 91.3.4 Comparison of Various Technologies 101.3.4.1 Drone Types & Sizes 101.3.4.2 Radar Positioning and Return to Home 101.3.4.3 GNSS on Ground Control Station 111.3.4.4 Collision Avoidance Technology and Obstacle Detection 111.3.4.5 Gyroscopic Stabilization, Flight Controllers and IMU 121.3.4.6 UAV Drone Propulsion System 121.3.4.7 Flight Parameters Through Telemetry 131.3.4.8 Drone Security & Hacking 131.3.4.9 3D Maps and Models With Drone Sensors 131.3.5 UAV Communication Network 151.3.5.1 Classification on the Basis of Spectrum Perspective 151.3.5.2 Various Types of Radio communication Links 161.3.5.3 VLOS (Visual Line-of-Sight) and BLOS (Beyond Line-of-Sight) Communication in Unmanned Aircraft System 181.3.5.4 Frequency Bands for the Operation of UAS 191.3.5.5 Cellular Technology for UAS Operation 191.4 Application of UAV 211.4.1 In Military 211.4.2 In Geomorphological Mapping and Other Similar Sectors 221.4.3 In Agriculture 221.5 UAV Challenges 231.6 Conclusion and Future Scope 24References 242 Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope 29Jolly Parikh and Anuradha Basu2.1 Introduction 302.2 Technical Challenges 302.2.1 Variations in Channel Characteristics 322.2.2 UAV-Assisted Cellular Network Planning and Provisioning 332.2.3 Millimeter Wave Cellular Connected UAVs 342.2.4 Deployment of UAV 352.2.5 Trajectory Optimization 362.2.6 On-Board Energy 372.3 Conclusion 37References 373 Battery and Energy Management in UAV-Based Networks 43Santosh Kumar, Amol Vasudeva and Manu Sood3.1 Introduction 433.2 The Need for Energy Management in UAV-Based Communication Networks 453.2.1 Unpredictable Trajectories of UAVs in Cellular UAV Networks 463.2.2 Non-Homogeneous Power Consumption 473.2.3 High Bandwidth Requirement/Low Spectrum Availability/Spectrum Scarcity 473.2.4 Short-Range Line-of-Sight Communication 483.2.5 Time Constraint (Time-Limited Spectrum Access) 483.2.6 Energy Constraint 493.2.7 The Joint Design for the Sensor Nodes’ Wake-Up Schedule and the UAV’s Trajectory (Data Collection) 493.3 Efficient Battery and Energy Management Proposed Techniques in Literature 503.3.1 Cognitive Radio (CR)-Based UAV Communication to Solve Spectrum Congestion 513.3.2 Compressed Sensing 523.3.3 Power Allocation and Position Optimization 533.3.4 Non-Orthogonal Multiple Access (NOMA) 533.3.5 Wireless Charging/Power Transfer (WPT) 543.3.6 UAV Trajectory Design Using a Reinforcement Learning Framework in a Decentralized Manner 553.3.7 Efficient Deployment and Movement of UAVs 553.3.8 3D Position Optimization Mixed With Resource Allocation to Overcome Spectrum Scarcity and Limited Energy Constraint 563.3.9 UAV-Enabled WSN: Energy-Efficient Data Collection 573.3.10 Trust Management 573.3.11 Self-Organization-Based Clustering 583.3.12 Bandwidth/Spectrum-Sharing Between UAVs 593.3.13 Using Millimeter Wave With SWIPT 593.3.14 Energy Harvesting 603.4 Conclusion 61References 674 Energy Efficient Communication Methods for Unmanned Ariel Vehicles (UAVs): Last Five Years’ Study 73Nagesh Kumar4.1 Introduction 734.1.1 Introduction to UAV 744.1.2 Communication in UAV 754.2 Literature Survey Process 774.2.1 Research Questions 774.2.2 Information Source 774.3 Routing in UAV 784.3.1 Communication Methods in UAV 784.3.1.1 Single-Hop Communication 794.3.1.2 Multi-Hop Communication 804.4 Challenges and Issues 824.4.1 Energy Consumption 824.4.2 Mobility of Devices 824.4.3 Density of UAVs 824.4.4 Changes in Topology 854.4.5 Propagation Models 854.4.6 Security in Routing 854.5 Conclusion 85References 865 A Review on Challenges and Threats to Unmanned Aerial Vehicles (UAVs) 89Shaik Johny Basha and Jagan Mohan Reddy Danda5.1 Introduction 895.2 Applications of UAVs and Their Market Opportunity 905.2.1 Applications 905.2.2 Market Opportunity 925.3 Attacks and Solutions to Unmanned Aerial Vehicles (UAVs) 925.3.1 Confidentiality Attacks 935.3.2 Integrity Attacks 955.3.3 Availability Attacks 965.3.4 Authenticity Attacks 975.4 Research Challenges 995.4.1 Security Concerns 995.4.2 Safety Concerns 995.4.3 Privacy Concerns 1005.4.4 Scalability Issues 1005.4.5 Limited Resources 1005.5 Conclusion 101References 1016 Internet of Things and UAV: An Interoperability Perspective 105Bharti Rana and Yashwant Singh6.1 Introduction 1066.2 Background 1086.2.1 Issues, Controversies, and Problems 1096.3 Internet of Things (IoT) and UAV 1106.4 Applications of UAV-Enabled IoT 1136.5 Research Issues in UAV-Enabled IoT 1146.6 High-Level UAV-Based IoT Architecture 1176.6.1 UAV Overview 1176.6.2 Enabling IoT Scalability 1196.6.3 Enabling IoT Intelligence 1206.6.4 Enabling Diverse IoT Applications 1216.7 Interoperability Issues in UAV-Based IoT 1216.8 Conclusion 123References 1247 Practices of Unmanned Aerial Vehicle (UAV) for Security Intelligence 129Swarnjeet Kaur, Kulwant Singh and Amanpreet Singh7.1 Introduction 1307.2 Military 1327.3 Attack 1337.4 Journalism 1347.5 Search and Rescue 1367.6 Disaster Relief 1387.7 Conclusion 139References 1398 Blockchain-Based Solutions for Various Security Issues in UAV-Enabled IoT 143Madhuri S. Wakode and Rajesh B. Ingle8.1 Introduction 1448.1.1 Organization of the Work 1458.2 Introduction to UAV and IoT 1458.2.1 UAV 1458.2.2 IoT 1468.2.3 UAV-Enabled IoT 1478.2.4 Blockchain 1508.3 Security and Privacy Issues in UAV-Enabled IoT 1518.4 Blockchain-Based Solutions to Various Security Issues 1538.5 Research Directions 1548.6 Conclusion 1548.7 Future Work 155References 1559 Efficient Energy Management Systems in UAV-Based IoT Networks 159V. Mounika Reddy, Neelima K. and G. Naresh9.1 Introduction 1609.2 Energy Harvesting Methods 1619.2.1 Basic Energy Harvesting Mechanisms 1629.2.2 Markov Decision Process-Based Energy Harvesting Mechanisms 1639.2.3 mm Wave Energy Harvesting Mechanism 1649.2.4 Full Duplex Wireless Energy Harvesting Mechanism 1659.3 Energy Recharge Methods 1659.4 Efficient Energy Utilization Methods 1669.4.1 GLRM Method 1669.4.2 DRL Mechanism 1679.4.3 Onboard Double Q-Learning Mechanism 1689.4.4 Collision-Free Scheduling Mechanism 1689.5 Conclusion 170References 17010 A Survey on IoE-Enabled Unmanned Aerial Vehicles 173K. Siddharthraju, R. Dhivyadevi, M. Supriya, B. Jaishankar and Shanmugaraja T.10.1 Introduction 17410.2 Overview of Internet of Everything 17610.2.1 Emergence of IoE 17610.2.2 Expectation of IoE 17710.2.2.1 Scalability 17710.2.2.2 Intelligence 17810.2.2.3 Diversity 17810.2.3 Possible Technologies 17910.2.3.1 Enabling Scalability 17910.2.3.2 Enabling Intelligence 18010.2.3.3 Enabling Diversity 18010.2.4 Challenges of IoE 18110.2.4.1 Coverage Constraint 18110.2.4.2 Battery Constraint 18110.2.4.3 Computing Constraint 18110.2.4.4 Security Constraint 18210.3 Overview of Unmanned Aerial Vehicle (UAV) 18210.3.1 Unmanned Aircraft System (UAS) 18310.3.2 UAV Communication Networks 18310.3.2.1 Ad Hoc Multi-UAV Networks 18310.3.2.2 UAV-Aided Communication Networks 18410.4 UAV and IoE Integration 18410.4.1 Possibilities to Carry UAVs 18410.4.1.1 Widespread Connectivity 18510.4.1.2 Environmentally Aware 18510.4.1.3 Peer-Maintenance of Communications 18510.4.1.4 Detector Control and Reusing 18510.4.2 UAV-Enabled IoE 18610.4.3 Vehicle Detection Enabled IoE Optimization 18610.4.3.1 Weak-Connected Locations 18610.4.3.2 Regions with Low Network Support 18610.5 Open Research Issues 18710.6 Discussion 18710.6.1 Resource Allocation 18710.6.2 Universal Standard Design 18810.6.3 Security Mechanism 18810.7 Conclusion 189References 18911 Role of AI and Big Data Analytics in UAV-Enabled IoT Applications for Smart Cities 193Madhuri S. Wakode11.1 Introduction 19411.1.1 Related Work 19511.1.2 Contributions 19511.1.3 Organization of the Work 19511.2 Overview of UAV-Enabled IoT Systems 19611.2.1 UAV-Enabled IoT Systems for Smart Cities 19711.3 Overview of Big Data Analytics 19711.4 Big Data Analytics Requirements in UAV-Enabled IoT Systems 19811.4.1 Big Data Analytics in UAV-Enabled IoT Applications 19911.4.2 Big Data Analytics for Governance of UAV-Enabled IoT Systems 20111.5 Challenges 20211.6 Conclusion 20211.7 Future Work 203References 20312 Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module 207Lakshit Kohli, Manglesh Saurabh, Ishaan Bhatia, Nidhi Sindhwani and Manjula Vijh12.1 Introduction 20812.2 Existing System 20812.3 Proposed System 21012.4 IoT Sensors and Architecture 21212.4.1 Sensors and Theory 21212.4.2 Architectures Available 21312.4.2.1 3-Layer IoT Architecture 21312.4.2.2 5-Layer IoT Architecture 21412.4.2.3 Architecture & Supporting Modules 21512.4.2.4 Integration Approach 21512.4.2.5 System of Modules 21612.5 Advantages of the Proposed System 21712.6 Design 21812.6.1 System Design 21912.6.2 Auto-Leveling 21912.6.3 Amphibious Landing Module 22112.6.4 Processing Module 22312.6.5 Surround Sense Module 22312.7 Results 22412.8 Conclusion 22712.9 Future Scope 228References 22813 Mind Controlled Unmanned Aerial Vehicle (UAV) Using Brain–Computer Interface (BCI) 231Prasath M.S., Naveen R. and Sivaraj G.13.1 Introduction 23213.1.1 Classification of UAVs 23213.1.2 Drone Controlling 23213.2 Mind-Controlled UAV With BCI Technology 23313.3 Layout and Architecture of BCI Technology 23413.4 Hardware Components 23513.4.1 Neurosky Mindwave Headset 23513.4.2 Microcontroller Board—Arduino 23613.4.3 A Computer 23713.4.4 Drone for Quadcopter 23813.5 Software Components 23913.5.1 Processing P3 Software 23913.5.2 Arduino IDE Software 24013.5.3 ThinkGear Connector 24013.6 Hardware and Software Integration 24113.7 Conclusion 243References 24414 Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0 247Dhirendra Siddharth, Dilip Kumar Saini and Ajay Kumar14.1 Introduction 24714.2 Drone Technology as an Instrument for Increasing Farm Productivity 24814.3 Mapping and Tracking of Rice Farm Areas With Information and Communication Technology (ICT) and Remote Sensing Technology 24914.3.1 Methodology and Development of ICT 25014.4 Strong Intelligence From UAV to the Agricultural Sector 25214.4.1 Latest Agricultural Drone History 25214.4.2 The Challenges 25414.4.3 SAP’s Next Wave of Drone Technologies 25414.4.4 SAP Connected Agriculture 25614.4.5 Cases of Real-World Use 25714.4.5.1 Crop Surveying 25714.4.5.2 Capture the Plantation 25814.4.5.3 Image Processing 25814.4.5.4 Working to Create GeoTiles and an Image Pyramid 25914.5 Drones-Based Sensor Platforms 26014.5.1 Context and Challenges 26014.5.2 Stakeholder and End Consumer Benefits 26114.5.3 The Technology 26214.5.3.1 Provisions of the Unmanned Aerial Vehicles 26214.6 Jobs of Space Technology in Crop Insurance 26314.7 The Institutionalization of Drone Imaging Technologies in Agriculture for Disaster Managing Risk 26714.7.1 A Modern Working 26714.7.2 Discovering Drone Mapping Technology 26814.7.3 From Lowland to Uplands, Drone Mapping Technology 26914.7.4 Institutionalization of Drone Monitoring Systems and Farming Capability 26914.8 Usage of Internet of Things in Agriculture and Use of Unmanned Aerial Vehicles 27014.8.1 System and Application Based on UAV-WSN 27014.8.2 Using a Complex Comprehensive System 27114.8.3 Benefits Assessment of Conventional System and the UAV-Based System 27114.8.3.1 Merit 27214.8.3.2 Saving Expenses 27214.8.3.3 Traditional Agriculture 27314.8.3.4 UAV-WSN System-Based Agriculture 27314.9 Conclusion 273References 27315 IoT-Based UAV Platform Revolutionized in Smart Healthcare 277Umesh Kumar Gera, Dilip Kumar Saini, Preeti Singh and Dhirendra Siddharth15.1 Introduction 27815.2 IoT-Based UAV Platform for Emergency Services 27915.3 Healthcare Internet of Things: Technologies, Advantages 28115.3.1 Advantage 28115.3.1.1 Concurrent Surveillance and Tracking 28115.3.1.2 From End-To-End Networking and Availability 28215.3.1.3 Information and Review Assortment 28215.3.1.4 Warnings and Recording 28215.3.1.5 Wellbeing Remote Assistance 28315.3.1.6 Research 28315.3.2 Complications 28315.3.2.1 Privacy and Data Security 28315.3.2.2 Integration: Various Protocols and Services 28415.3.2.3 Overload and Accuracy of Data 28415.3.2.4 Expenditure 28415.4 Healthcare’s IoT Applications: Surgical and Medical Applications of Drones 28515.4.1 Hearables 28515.4.2 Ingestible Sensors 28515.4.3 Moodables 28515.4.4 Technology of Computer Vision 28615.4.5 Charting for Healthcare 28615.5 Drones That Will Revolutionize Healthcare 28615.5.1 Integrated Enhancement in Efficiency 28615.5.2 Offering Personalized Healthcare 28715.5.3 The Big Data Manipulation 28715.5.4 Safety and Privacy Optimization 28715.5.5 Enabling M2M Communication 28815.6 Healthcare Revolutionizing Drones 28815.6.1 Google Drones 28815.6.2 Healthcare Integrated Rescue Operations (HiRO) 28915.6.3 EHang 28915.6.4 TU Delft 28915.6.5 Project Wing 28915.6.6 Flirtey 28915.6.7 Seattle’s VillageReach 29015.6.8 ZipLine 29015.7 Conclusion 290References 290Index 295