Internet of Drones Applications, Opportunities, and Challenges
"This book covers different aspects of Internet of Drones (IoD) including fundamentals in drone design, deployment challenges, and development of applications. It starts with a detailed description of concepts and processes in designing an efficient system, and architecture. It details differen...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press
[2023]
|
| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009784598506719 |
Tabla de Contenidos:
- Intro
- Half Title
- Title Page
- Copyright Page
- Contents
- About the Editors
- Contributors
- Preface
- 1. Internet of Drones: Applications, Challenges, Opportunities
- 1.1 Introduction
- 1.1.1 How IoDs Work?
- 1.2 Applications of IoDs
- 1.2.1 Warfare
- 1.2.2 Disaster Management
- 1.2.3 Smart Surveillance
- 1.2.4 Smart Agriculture
- 1.2.5 Firefighting
- 1.3 Components of IoDs
- 1.3.1 UAV Parts in Construction
- 1.3.1.1 UAV Frames
- 1.3.1.2 Propellers
- 1.3.1.3 Motors
- 1.3.1.4 Electronic Speed Controller (ESC)
- 1.3.1.5 Radio Transmitter
- 1.3.1.6 Battery, Electronics, and Power Distribution Cables
- 1.3.1.7 Sensors and Camera
- 1.3.1.8 Flight Control Board
- 1.3.1.9 First-Person View (FPV)
- 1.3.2 UAV Communication
- 1.3.2.1 UAV-to-UAV Communication
- 1.3.2.2 UAV to Ground Station
- 1.3.2.3 IoT-Enabled UAV Communication System
- 1.3.3 Communication Requirements (Data)
- 1.3.4 Security Requirements
- 1.4 Artificial Intelligence (AI) for IoD
- 1.5 Conclusion
- References
- 2. Modeling, Simulation, and Analysis Hybrid Unmanned Aerial Vehicle with C-Wing
- 2.1 Introduction
- 2.2 Literature Review
- 2.3 Methodology
- 2.3.1 Design Selection
- 2.3.2 Selection of Airfoil
- 2.3.3 Selection of Motors
- 2.3.4 Schematic Diagram of UAV Components
- 2.3.5 Specifications of UAV
- 2.3.6 Component Selection and Weight Estimation
- 2.3.7 Selection of Winglet
- 2.4 Results and Discussion
- 2.4.1 ANSYS Simulations and Results
- 2.4.1.1 Fluent Analysis for Hybrid UAV with C-Winglet
- 2.4.1.2 Fluent Analysis for Hybrid UAV without C-Winglet
- 2.4.1.3 Fluent Analysis for Wing without Winglet for Various AOA
- 2.4.1.4 Wing Area Calculations for a Wing without a Winglet
- 2.4.1.5 Induced Drag for a Wing with a C-Winglet for Various AOA
- 2.4.1.6 Induced Drag Calculations for a Wing with a C-Winglet.
- 2.4.2 Physical Modeling in a Simulation Platform
- 2.4.3 UAV Dynamics (U1 [N])
- 2.5 Experimental Setup
- 2.5.1 Rotary-Wing UAV
- 2.5.2 Fixed-Wing UAV
- 2.5.3 Software Setup
- 2.6 Conclusion
- References
- 3. Influence of Machine Learning Technique in Unmanned Aerial Vehicle
- 3.1 Introduction to Unmanned Aerial Vehicle
- 3.2 UAV in Industry 5.0
- 3.3 Drone Ecosystem in the Machine Learning Industry
- 3.3.1 Computers and Wireless Networks
- 3.3.2 Sustainable Smart Cities and Military Facilities
- 3.3.3 Farming
- 3.3.4 Others
- 3.4 Market Analysis
- 3.4.1 Machine Learning-Assisted Framework
- 3.4.2 Learning Models
- 3.4.3 Challenges in the Current Research Work
- 3.5 Conclusion
- Acknowledgment
- References
- 4. Review of Medical Drones in Healthcare Applications
- 4.1 Introduction
- 4.1.1 IoD-Internet of Drones
- 4.1.2 Drones in Health Care
- 4.2 Internet of Medical Drone (IoMD) in Health Care
- 4.3 Architecture of Healthcare IoMD
- 4.3.1 Data Collection Phase
- 4.3.2 Data Reporting Phase
- 4.3.3 Data Processing Phase
- 4.3.4 Tools and Technologies of IoMD
- 4.4 Applications of IoMD Healthcare System
- 4.4.1 Search and Rescue
- 4.4.2 Transport and Delivery
- 4.4.3 Medical Care
- 4.5 Usage of IoMD for Health Care
- 4.5.1 Drone Functions to Combat COVID-19 in India
- 4.5.1.1 Surveillance and Lockdown Enforcement
- 4.5.1.2 Public Broadcast
- 4.5.1.3 Monitoring Body Temperatures
- 4.5.1.4 Medical and Emergency Food Supplies Delivery
- 4.5.1.5 Surveying and Mapping
- 4.5.1.6 Spraying Disinfectants
- 4.6 Recent Technologies in Healthcare IoMD
- 4.6.1 IoMDs
- 4.6.2 Cloud/Edge Computing
- 4.6.3 Internet Protocols
- 4.6.4 Artificial Intelligence
- 4.6.5 Blockchain
- 4.7 Challenges and Open Issues for Future Research
- 4.7.1 Challenges in IoMD for Healthcare Services
- 4.7.1.1 Adoption of IoMD.
- 4.7.1.2 Issues in Security
- 4.7.1.3 Governance and Regulation on the Internet of Drones Adaption
- 4.7.2 Research Gap and Future Direction on IoMD
- 4.8 Conclusions
- References
- 5. CoVacciDrone: An Algorithmic-Drone-Based COVID-19 Vaccine Distribution Strategy
- 5.1 Introduction
- 5.2 Dividing Sections into COVID Divisions with Voronoi Diagrams
- 5.3 Cost Optimization with Dynamic Connectivity
- 5.4 Dynamic Connectivity with Euler Tour Trees
- 5.5 Dynamic Connectivity with Cut-sets
- 5.6 Results and Observation
- References
- 6. Ambulance Drone for Rescue - A Perspective on Conceptual Design, Life Detection Systems, and Prototype Development
- 6.1 Introduction
- 6.2 Conceptual Design: Main Components
- 6.3 Systems for Searching Human Presence - A Survey
- 6.3.1 Human Presence Detection Employing MMW
- 6.3.2 Method of Identifying Humans from Other Moving Creatures
- 6.3.3 The Image Processing by Using Thermal Image Technique - Haar-Cascade Classifier
- 6.3.4 The Random Human Body Motion
- 6.3.5 Heartbeat Sensing by Using Doppler Radar
- 6.3.6 Design of Robotic Arm in a UAD
- 6.4 Unmanned Ambulance Drone Prototype Specification Details
- 6.5 Conclusion
- References
- 7. A Comprehensive Review on Internet of Agro Drones for Precision Agriculture
- 7.1 Introduction
- 7.2 Scope for Precision Agriculture
- 7.3 The Architecture of Drones in Precision Agriculture
- 7.3.1 Mode of Operation
- 7.3.2 Ground Control Station (GCS)
- 7.3.3 Drone Control System
- 7.3.4 Data Acquisition Sensors
- 7.3.4.1 RGB Colour Sensors
- 7.3.4.2 Multispectral and Hyperspectral Cameras [26,27]
- 7.3.4.3 Thermal Infrared Sensors
- 7.4 Agro Drones Available on the Market for Precision Agriculture
- 7.5 Implementation of IoT Architecture
- 7.5.1 Image Processing Techniques Implementation
- 7.5.1.1 Image Collection.
- 7.5.1.2 Image Pre-processing
- 7.5.1.3 Image Segmentation
- 7.5.1.4 Extracting Features
- 7.5.1.5 Classification
- 7.5.2 Map Generation Technique
- 7.6 Applications of Agro Drones in Precision Agriculture
- 7.6.1 Weed Mapping and Management
- 7.6.2 Monitoring the Growth of the Vegetation and Estimating the Yield
- 7.6.3 Disease Detection and Monitoring of Vegetation
- 7.6.4 Irrigation Management
- 7.6.5 Corps Spraying
- 7.6.6 Crop Scouting [45]
- 7.6.7 Irrigation Monitoring and Inspection
- 7.6.8 Pesticide Spraying
- 7.7 Challenges to Implementing Precision Agriculture Using Agro Drones
- 7.8 Benefits of Drones in Agriculture
- 7.8.1 Safety
- 7.8.2 Time Saving
- 7.8.3 Wastage Reduction
- 7.8.4 Water Conservation
- 7.8.5 Cost Saving
- 7.8.6 User-Friendly
- 7.8.7 Improves Productivity
- 7.8.8 Pollution Reduction
- 7.8.9 Employment Opportunities
- 7.9 Conclusion
- References
- 8. A Smart WeeDrone for Sustainable Agriculture
- 8.1 Introduction
- 8.1.1 Competition Between Crops and Weeds
- 8.2 Related Work
- 8.3 WeeDrone
- 8.4 Features of WeeDrone
- 8.4.1 Shortest Path Algorithm
- 8.4.2 OpenDroneMap
- 8.4.3 Machine Learning Algorithm
- 8.4.4 Removal of Diseased/Dead Plant
- 8.4.5 Sowing and Spreading Manure
- 8.4.6 Data Analysis
- 8.5 Design and Working Principle
- 8.6 Outputs and Simulation
- 8.6.1 Autonomous Flight
- 8.6.1.1 Drone Flight
- 8.6.1.2 Automatic Flight Subsystem
- 8.6.2 Image Processing
- 8.6.3 Robotic Arm Design
- 8.6.3.1 Statistical Analysis of WeeDrone Model
- 8.7 Conclusion
- References
- 9. Internet of Agro Drones for Precision Agriculture
- 9.1 Introduction
- 9.2 Precision Agriculture
- 9.2.1 What Is Precision Agriculture?
- 9.2.2 Importance of Precision Agriculture
- 9.2.3 Advantages of Precision Agriculture
- 9.3 Drones' Part in Precision Agriculture.
- 9.3.1 Need and Use of Drones in Agriculture
- 9.3.2 Best Agricultural Drone Practices in Precision Agriculture
- 9.4 Types of Precision Agriculture
- 9.4.1 Precision Seeding
- 9.4.1.1 Belt Type
- 9.4.1.2 Plate Type
- 9.4.1.3 Vacuum Type
- 9.4.1.4 Spoon Type
- 9.4.1.5 Pneumatic Type
- 9.4.1.6 Grooved Cylinder Type
- 9.4.2 Precision Soil Preparation
- 9.4.2.1 Grid Sampling
- 9.4.2.2 Direct Sampling
- 9.4.3 Precision Crop Management
- 9.4.4 Precision Fertilizing
- 9.4.4.1 Target Metrics
- 9.4.4.2 Bounded Field Metrics
- 9.4.4.3 Contemplate with the Temporal Situations
- 9.4.4.4 Study about the Responses from the Crop
- 9.4.5 Precision Harvesting
- 9.5 Use Cases of Agricultural Drones
- 9.5.1 Case Study: Reducing Herbicide Use in Brazil
- 9.5.2 Drones - VTOL and VTOL Hybrid Drones
- 9.5.3 Japan's Suitable Point of Reference
- 9.5.4 Using Drones to Spot Disease and Weed Infestations in Sugar Beets
- 9.6 Future of Agriculture Drones
- 9.6.1 Application of 3D Printing Technology to Food
- 9.6.2 Internet of Things (IoT)
- 9.6.3 Automation of Skills and Workforce
- 9.6.4 Data-Driven Farming
- 9.6.5 Chatbots
- 9.6.6 Drone Technology
- 9.6.7 Unmanned Aerial Vehicles
- 9.6.8 Blockchain and Securing the Agriculture Value Chain
- 9.6.9 Nanotechnology and Precision Agriculture
- 9.6.10 Food Sharing and Crowd Farming
- 9.6.11 Internet of Agro-Drone Things (IoDT)
- 9.7 Conclusion
- References
- 10. IoD-Enabled Swarm of Drones for Air Space Control
- 10.1 Introduction
- 10.1.1 Rescue
- 10.1.2 Surveillance
- 10.1.3 Detection of Bogeys
- 10.1.4 Warfare
- 10.1.5 Disaster Management
- 10.1.6 Firefighting Drones
- 10.2 Swarm of Drones
- 10.3 General System Architecture
- 10.3.1 Air Space Control
- 10.3.1.1 Air Collision Avoidance
- 10.3.1.2 Formation Control.
- 10.3.1.3 Parameters Used for Formation Control and Collision Avoidance.
