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Группа авторов. Unmanned Aerial Vehicles for Internet of Things (IoT)
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Unmanned Aerial Vehicles for Internet of Things (IoT) Concepts, Techniques, and Applications
Preface
1. Unmanned Aerial Vehicle (UAV): A Comprehensive Survey
1.1 Introduction
1.2 Related Work
1.3 UAV Technology. 1.3.1 UAV Platforms
1.3.1.1 Fixed-Wing Drones
1.3.1.2 Multi-Rotor Drones
1.3.1.3 Single-Rotor Drones
1.3.1.4 Fixed-Wing Hybrid VTOL
1.3.2 Categories of the Military Drones
1.3.3 How Drones Work
1.3.3.1 Firmware—Platform Construction and Design
1.3.4 Comparison of Various Technologies. 1.3.4.1 Drone Types & Sizes
1.3.4.2 Radar Positioning and Return to Home
1.3.4.3 GNSS on Ground Control Station
1.3.4.4 Collision Avoidance Technology and Obstacle Detection
1.3.4.5 Gyroscopic Stabilization, Flight Controllers and IMU
1.3.4.6 UAV Drone Propulsion System
1.3.4.6.1 Proposed Technology
1.3.4.7 Flight Parameters Through Telemetry
1.3.4.8 Drone Security & Hacking
1.3.4.9 3D Maps and Models With Drone Sensors
1.3.5 UAV Communication Network
1.3.5.1 Classification on the Basis of Spectrum Perspective
1.3.5.2 Various Types of Radiocommunication Links
1.3.5.2.1 Radio-Communications for the Air-Traffic Control
1.3.5.2.2 Radio-Communications for Command and Control
1.3.5.2.3 Radio-Communications for the Sense and Avoid Function
1.3.5.3 VLOS (Visual Line-of-Sight) and BLOS (Beyond Line-of-Sight) Communication in Unmanned Aircraft System
1.3.5.4 Frequency Bands for the Operation of UAS
1.3.5.5 Cellular Technology for UAS Operation
1.4 Application of UAV
1.4.1 In Military
1.4.2 In Geomorphological Mapping and Other Similar Sectors
1.4.3 In Agriculture
1.5 UAV Challenges
1.6 Conclusion and Future Scope
References
2. Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope
2.1 Introduction
2.2 Technical Challenges
2.2.1 Variations in Channel Characteristics
2.2.2 UAV-Assisted Cellular Network Planning and Provisioning
2.2.3 Millimeter Wave Cellular Connected UAVs
2.2.4 Deployment of UAV
2.2.5 Trajectory Optimization
2.2.6 On-Board Energy
2.3 Conclusion
References
3. Battery and Energy Management in UAV-Based Networks
3.1 Introduction
3.2 The Need for Energy Management in UAV-Based Communication Networks
3.2.1 Unpredictable Trajectories of UAVs in Cellular UAV Networks
3.2.2 Non-Homogeneous Power Consumption
3.2.3 High Bandwidth Requirement/Low Spectrum Availability/Spectrum Scarcity
3.2.4 Short-Range Line-of-Sight Communication
3.2.5 Time Constraint (Time-Limited Spectrum Access)
3.2.6 Energy Constraint
3.2.7 The Joint Design for the Sensor Nodes’ Wake-Up Schedule and the UAV’s Trajectory (Data Collection)
3.3 Efficient Battery and Energy Management Proposed Techniques in Literature
3.3.1 Cognitive Radio (CR)-Based UAV Communication to Solve Spectrum Congestion
3.3.2 Compressed Sensing
3.3.3 Power Allocation and Position Optimization
3.3.4 Non-Orthogonal Multiple Access (NOMA)
3.3.5 Wireless Charging/Power Transfer (WPT)
3.3.6 UAV Trajectory Design Using a Reinforcement Learning Framework in a Decentralized Manner
3.3.7 Efficient Deployment and Movement of UAVs
3.3.8 3D Position Optimization Mixed With Resource Allocation to Overcome Spectrum Scarcity and Limited Energy Constraint
3.3.9 UAV-Enabled WSN: Energy-Efficient Data Collection
3.3.10 Trust Management
3.3.11 Self-Organization-Based Clustering
3.3.12 Bandwidth/Spectrum-Sharing Between UAVs
3.3.13 Using Millimeter Wave With SWIPT
3.3.14 Energy Harvesting
3.4 Conclusion
References
4. Energy Efficient Communication Methods for Unmanned Ariel Vehicles (UAVs): Last Five Years’ Study
4.1 Introduction
4.1.1 Introduction to UAV
4.1.2 Communication in UAV
4.2 Literature Survey Process
4.2.1 Research Questions
4.2.2 Information Source
4.3 Routing in UAV
4.3.1 Communication Methods in UAV
4.3.1.1 Single-Hop Communication
4.3.1.2 Multi-Hop Communication
4.4 Challenges and Issues
4.4.1 Energy Consumption
4.4.2 Mobility of Devices
4.4.3 Density of UAVs
4.4.4 Changes in Topology
4.4.5 Propagation Models
4.4.6 Security in Routing
4.5 Conclusion
References
5. A Review on Challenges and Threats to Unmanned Aerial Vehicles (UAVs)
5.1 Introduction
5.2 Applications of UAVs and Their Market Opportunity
5.2.1 Applications
5.2.2 Market Opportunity
5.3 Attacks and Solutions to Unmanned Aerial Vehicles (UAVs)
5.3.1 Confidentiality Attacks
5.3.2 Integrity Attacks
5.3.3 Availability Attacks
5.3.4 Authenticity Attacks
5.4 Research Challenges
5.4.1 Security Concerns
5.4.2 Safety Concerns
5.4.3 Privacy Concerns
5.4.4 Scalability Issues
5.4.5 Limited Resources
5.5 Conclusion
References
6. Internet of Things and UAV: An Interoperability Perspective
6.1 Introduction
6.2 Background
6.2.1 Issues, Controversies, and Problems
6.3 Internet of Things (IoT) and UAV
6.4 Applications of UAV-Enabled IoT
6.5 Research Issues in UAV-Enabled IoT
6.6 High-Level UAV-Based IoT Architecture
6.6.1 UAV Overview
6.6.2 Enabling IoT Scalability
6.6.3 Enabling IoT Intelligence
6.6.4 Enabling Diverse IoT Applications
6.7 Interoperability Issues in UAV-Based IoT
6.8 Conclusion
References
7. Practices of Unmanned Aerial Vehicle (UAV) for Security Intelligence
7.1 Introduction
7.2 Military
7.3 Attack
7.4 Journalism
7.5 Search and Rescue
7.6 Disaster Relief
7.7 Conclusion
References
8. Blockchain-Based Solutions for Various Security Issues in UAV-Enabled IoT
8.1 Introduction
8.1.1 Organization of the Work
8.2 Introduction to UAV and IoT
8.2.1 UAV
8.2.2 IoT
8.2.3 UAV-Enabled IoT
8.2.4 Blockchain
8.3 Security and Privacy Issues in UAV-Enabled IoT
8.4 Blockchain-Based Solutions to Various Security Issues
8.5 Research Directions
8.6 Conclusion
8.7 Future Work
References
9. Efficient Energy Management Systems in UAV-Based IoT Networks
9.1 Introduction
9.2 Energy Harvesting Methods
9.2.1 Basic Energy Harvesting Mechanisms
9.2.2 Markov Decision Process-Based Energy Harvesting Mechanisms
9.2.3 mm Wave Energy Harvesting Mechanism
9.3 Energy Recharge Methods
9.4 Efficient Energy Utilization Methods
9.4.1 GLRM Method
9.4.2 DRL Mechanism
9.4.3 Onboard Double Q-Learning Mechanism
9.4.4 Collision-Free Scheduling Mechanism
9.5 Conclusion
References
10. A Survey on IoE-Enabled Unmanned Aerial Vehicles
10.1 Introduction
10.2 Overview of Internet of Everything
10.2.1 Emergence of IoE
10.2.2 Expectation of IoE
10.2.2.1 Scalability
10.2.2.2 Intelligence
10.2.2.3 Diversity
10.2.3 Possible Technologies. 10.2.3.1 Enabling Scalability
10.2.3.2 Enabling Intelligence
10.2.3.3 Enabling Diversity
10.2.4 Challenges of IoE
10.2.4.1 Coverage Constraint
10.2.4.2 Battery Constraint
10.2.4.3 Computing Constraint
10.2.4.4 Security Constraint
10.3 Overview of Unmanned Aerial Vehicle (UAV)
10.3.2 UAV Communication Networks
10.3.2.1 Ad Hoc Multi-UAV Networks
10.3.2.2 UAV-Aided Communication Networks
10.4 UAV and IoE Integration
10.4.1 Possibilities to Carry UAVs
10.4.1.1 Widespread Connectivity
10.4.1.2 Environmentally Aware
10.4.1.3 Peer-Maintenance of Communications
10.4.1.4 Detector Control and Reusing
10.4.2 UAV-Enabled IoE
10.4.3 Vehicle Detection Enabled IoE Optimization
10.4.3.1 Weak-Connected Locations
10.4.3.2 Regions with Low Network Support
10.5 Open Research Issues
10.6 Discussion. 10.6.1 Resource Allocation
10.6.2 Universal Standard Design
10.6.3 Security Mechanism
10.7 Conclusion
References
11. Role of AI and Big Data Analytics in UAV-Enabled IoT Applications for Smart Cities
11.1 Introduction
11.1.1 Related Work
11.1.2 Contributions
11.1.3 Organization of the Work
11.2 Overview of UAV-Enabled IoT Systems
11.3 Overview of Big Data Analytics
11.4 Big Data Analytics Requirements in UAV-Enabled IoT Systems
11.4.1 Big Data Analytics in UAV-Enabled IoT Applications
11.4.2 Big Data Analytics for Governance of UAV-Enabled IoT Systems
11.5 Challenges
11.6 Conclusion
11.7 Future Work
References
12. Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module
12.1 Introduction
12.2 Existing System
12.3 Proposed System
12.4 IoT Sensors and Architecture
12.4.1 Sensors and Theory
12.4.2 Architectures Available
12.4.2.1 3-Layer IoT Architecture
12.4.2.2 5-Layer IoT Architecture
12.4.2.3 Architecture & Supporting Modules
12.4.2.4 Integration Approach
12.4.2.5 System of Modules
12.5 Advantages of the Proposed System
12.6 Design
12.6.1 System Design
12.6.2 Auto-Leveling
12.6.3 Amphibious Landing Module
12.6.4 Processing Module
12.6.5 Surround Sense Module
12.7 Results
12.8 Conclusion
12.9 Future Scope
References
13. Mind-Controlled Unmanned Aerial Vehicle (UAV) Using Brain–Computer Interface (BCI)
13.1 Introduction
13.1.1 Classification of UAVs
13.1.2 Drone Controlling
13.2 Mind-Controlled UAV With BCI Technology
13.3 Layout and Architecture of BCI Technology
13.4 Hardware Components
13.4.1 Neurosky Mindwave Headset
13.4.2 Microcontroller Board—Arduino
13.4.3 A Computer
13.4.4 Drone for Quadcopter
13.5 Software Components
13.5.1 Processing P3 Software
13.5.2 Arduino IDE Software
13.5.3 ThinkGear Connector
13.6 Hardware and Software Integration
13.7 Conclusion
References
14. Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0
14.1 Introduction
14.2 Drone Technology as an Instrument for Increasing Farm Productivity
14.3 Mapping and Tracking of Rice Farm Areas With Information and Communication Technology (ICT) and Remote Sensing Technology
14.3.1 Methodology and Development of ICT
14.4 Strong Intelligence From UAV to the Agricultural Sector
14.4.1 Latest Agricultural Drone History
14.4.2 The Challenges
14.4.3 SAP’s Next Wave of Drone Technologies
14.4.4 SAP Connected Agriculture
14.4.5 Cases of Real-World Use
14.4.5.1 Crop Surveying
14.4.5.2 Capture the Plantation
14.4.5.3 Image Processing
14.4.5.4 Working to Create GeoTiles and an Image Pyramid
14.5 Drones-Based Sensor Platforms
14.5.1 Context and Challenges
14.5.2 Stakeholder and End Consumer Benefits
14.5.3 The Technology
14.5.3.1 Provisions of the Unmanned Aerial Vehicles
14.6 Jobs of Space Technology in Crop Insurance
14.7 The Institutionalization of Drone Imaging Technologies in Agriculture for Disaster Managing Risk
14.7.1 A Modern Working
14.7.2 Discovering Drone Mapping Technology
14.7.3 From Lowland to Uplands, Drone Mapping Technology
14.7.4 Institutionalization of Drone Monitoring Systems and Farming Capability
14.8 Usage of Internet of Things in Agriculture and Use of Unmanned Aerial Vehicles
14.8.1 System and Application Based on UAV-WSN
14.8.2 Using a Complex Comprehensive System
14.8.3 Benefits Assessment of Conventional System and the UAV-Based System
14.8.3.1 Merit
14.8.3.2 Saving Expenses
14.8.3.3 Traditional Agriculture
14.8.3.4 UAV-WSN System-Based Agriculture
14.9 Conclusion
References
15. IoT-Based UAV Platform Revolutionized in Smart Healthcare
15.1 Introduction
15.2 IoT-Based UAV Platform for Emergency Services
15.3 Healthcare Internet of Things: Technologies, Advantages
15.3.1 Advantage. 15.3.1.1 Concurrent Surveillance and Tracking
15.3.1.2 From End-To-End Networking and Availability
15.3.1.3 Information and Review Assortment
15.3.1.4 Warnings and Recording
15.3.1.5 Wellbeing Remote Assistance
15.3.1.6 Research
15.3.2 Complications. 15.3.2.1 Privacy and Data Security
15.3.2.2 Integration: Various Protocols and Services
15.3.2.3 Overload and Accuracy of Data
15.3.2.4 Expenditure
15.4 Healthcare’s IoT Applications: Surgical and Medical Applications of Drones
15.4.1 Hearables
15.4.2 Ingestible Sensors
15.4.3 Moodables
15.4.4 Technology of Computer Vision
15.4.5 Charting for Healthcare
15.5 Drones That Will Revolutionize Healthcare
15.5.1 Integrated Enhancement in Efficiency
15.5.2 Offering Personalized Healthcare
15.5.3 The Big Data Manipulation
15.5.4 Safety and Privacy Optimization
15.5.5 Enabling M2M Communication
15.6 Healthcare Revolutionizing Drones
15.6.1 Google Drones
15.6.2 Healthcare Integrated Rescue Operations (HiRO)
15.6.3 EHang
15.6.4 TU Delft
15.6.5 Project Wing
15.6.6 Flirtey
15.6.7 Seattle’s VillageReach
15.6.8 ZipLine
15.7 Conclusion
References
Index
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