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Table of Contents
Intro
Preface
Acknowledgments
Contents
Editors and Contributors
Abbreviations
1 Introduction to Drone Data Analytics in Aerial Computing
1.1 Introduction
1.2 Literature Survey
1.3 Data Analytics
1.4 Drone Data Analytics
1.5 Working Principle of Drone and Essential Principles of Data Analytics
1.6 Aerial Computing
1.7 Fundamental Elements of Data Analytics
1.8 Reasons for Using AI in Data Analytics
1.9 Drone Technology Using AI
1.10 Challenges in Drone Data Analytics
1.11 Future Research Direction of Drone Data Analytics
1.12 Conclusion
References
2 A Study in Federated Learning Analytics for UAV
2.1 Introduction
2.2 Federated Learning Architecture for UAV
2.2.1 Original FL (Google)
2.2.2 Collaborative FL
2.2.3 Multihop FL
2.2.4 Fog Learning
2.3 A Decentralized FL for UAV Network
2.3.1 Advantages
2.3.2 Future Research Directions in DFL-UN
2.4 A Simple Federated Learning in UAV Networks
2.5 Federated Applications in UAV-Enabled Networks
2.5.1 Federated Learning in UAV for 6G Cellular Networks
2.5.2 Federated UAV Ad-Hoc Networks
2.5.3 Federated UAV for IOT Networks
2.5.4 Federated Learning in UAVs in Edge Computing
2.6 The Future of Federated Learning
References
3 Analysis of Geospatial Data Collected by Drones as Part of Aerial Computing
3.1 Introduction
3.2 Related Work
3.2.1 Unmanned Aerial Vehicles (UAVs)
3.2.2 High Altitude Platforms (HAPs)
3.2.3 Mobile Edge Computing (MEC)
3.2.4 Geographic Information System (GIS)
3.2.5 Space Air-Ground Integrated Network (SAGIN)
3.2.6 Offloading of Calculations in Satellite Networks
3.2.7 Using Machine Learning to Offload Computation
3.3 Drones
3.3.1 History of Drones
3.3.2 Key Features of a Drone
3.3.3 Classification of Drones
3.3.4 Software's for Drones
3.4 Aerial Computing
3.4.1 Features
3.4.2 Network Design
3.4.3 Enabling Technologies
3.4.4 Domain Applications
3.4.5 Challenges
3.5 Geospatial Data Analysis
3.5.1 Definition of Geospatial Data
3.5.2 Types of Geospatial Data
3.5.3 Geospatial Big Data Challenges
3.5.4 Geospatial Data Collection and Management
3.5.5 Benefits of Using Geospatial Data
3.5.6 Photogrammetry
3.6 Conclusion
References
4 Beach Wrack Identification on Unmanned Aerial Vehicles Dataset Using Artificial Intelligence for Coastal Environmental Management
4.1 Introduction
4.2 Related Review About Beach Wrack
4.3 Material and Methods
4.3.1 Working Procedure of BPWCNN
4.3.2 Working Procedure of KNN
4.3.3 Working Procedure of RF
4.4 Results on Beach Wrack
4.4.1 Comparison of Beach Wrack Identification Between BPWCNN and KNN
4.4.2 Comparison of Beach Wrack Identification Between BPWCNN and RF
4.5 Discussion with Similar and Opposite Finding
Preface
Acknowledgments
Contents
Editors and Contributors
Abbreviations
1 Introduction to Drone Data Analytics in Aerial Computing
1.1 Introduction
1.2 Literature Survey
1.3 Data Analytics
1.4 Drone Data Analytics
1.5 Working Principle of Drone and Essential Principles of Data Analytics
1.6 Aerial Computing
1.7 Fundamental Elements of Data Analytics
1.8 Reasons for Using AI in Data Analytics
1.9 Drone Technology Using AI
1.10 Challenges in Drone Data Analytics
1.11 Future Research Direction of Drone Data Analytics
1.12 Conclusion
References
2 A Study in Federated Learning Analytics for UAV
2.1 Introduction
2.2 Federated Learning Architecture for UAV
2.2.1 Original FL (Google)
2.2.2 Collaborative FL
2.2.3 Multihop FL
2.2.4 Fog Learning
2.3 A Decentralized FL for UAV Network
2.3.1 Advantages
2.3.2 Future Research Directions in DFL-UN
2.4 A Simple Federated Learning in UAV Networks
2.5 Federated Applications in UAV-Enabled Networks
2.5.1 Federated Learning in UAV for 6G Cellular Networks
2.5.2 Federated UAV Ad-Hoc Networks
2.5.3 Federated UAV for IOT Networks
2.5.4 Federated Learning in UAVs in Edge Computing
2.6 The Future of Federated Learning
References
3 Analysis of Geospatial Data Collected by Drones as Part of Aerial Computing
3.1 Introduction
3.2 Related Work
3.2.1 Unmanned Aerial Vehicles (UAVs)
3.2.2 High Altitude Platforms (HAPs)
3.2.3 Mobile Edge Computing (MEC)
3.2.4 Geographic Information System (GIS)
3.2.5 Space Air-Ground Integrated Network (SAGIN)
3.2.6 Offloading of Calculations in Satellite Networks
3.2.7 Using Machine Learning to Offload Computation
3.3 Drones
3.3.1 History of Drones
3.3.2 Key Features of a Drone
3.3.3 Classification of Drones
3.3.4 Software's for Drones
3.4 Aerial Computing
3.4.1 Features
3.4.2 Network Design
3.4.3 Enabling Technologies
3.4.4 Domain Applications
3.4.5 Challenges
3.5 Geospatial Data Analysis
3.5.1 Definition of Geospatial Data
3.5.2 Types of Geospatial Data
3.5.3 Geospatial Big Data Challenges
3.5.4 Geospatial Data Collection and Management
3.5.5 Benefits of Using Geospatial Data
3.5.6 Photogrammetry
3.6 Conclusion
References
4 Beach Wrack Identification on Unmanned Aerial Vehicles Dataset Using Artificial Intelligence for Coastal Environmental Management
4.1 Introduction
4.2 Related Review About Beach Wrack
4.3 Material and Methods
4.3.1 Working Procedure of BPWCNN
4.3.2 Working Procedure of KNN
4.3.3 Working Procedure of RF
4.4 Results on Beach Wrack
4.4.1 Comparison of Beach Wrack Identification Between BPWCNN and KNN
4.4.2 Comparison of Beach Wrack Identification Between BPWCNN and RF
4.5 Discussion with Similar and Opposite Finding