Название: Drone Data Analytics in Aerial Computing Автор: P. Karthikeyan, Sathish Kumar, V. Anbarasu Издательство: Springer Серия: Transactions on Computer Systems and Networks Год: 2023 Страниц: 282 Язык: английский Формат: pdf (true), epub Размер: 57.7 MB
Drone data analytics guarantee that the right people have the correct data at their fingertips whenever needed. Drone flight gathers a Terabyte of data and geo-tagged reference points without traditional ground control. The data automatically uploads to the aerial computing platform. Aerial computing analyzes the data and generates the required output in visualization, graph, and action. This book presents a comprehensive overview of Drone Data Analytics in Aerial Computing, exploring the latest techniques, tools, and technologies in this rapidly growing field.
This book aims to provide readers with a comprehensive understanding of drone data analytics and aerial computing. It aims to bridge the gap between the technical and non-technical aspects, making it accessible and understandable to a wide range of readers. The book provides practical insights and real-world examples to illustrate the concepts discussed, making it an invaluable resource for anyone interested in drone data analytics and aerial computing.
The world of AI and Data Science is changing fast, and so are its successor technologies. One of the latest additions to this stack is Federated analytics. Federated analytics is a systematic method used for data analytics that does not provide input data from individual devices. Where conventional data science brings much of the information into a typical central data lake, federated analytics collects information from far-distributed datasets without collecting it in one central server. Collaborative Data Science without data collection, as Google terms it. Federated Learning (FL) is a way to train centralized Machine Learning models on decentralized data. A dominant application of Federated Learning has been In the medical sector, where data privacy and accuracy are of prime importance. Another application where Federated Learning can make a mark is federated analytics for drone technology. An application that connects multiple drone data for varied applications like agriculture, animal disease deduction, defence system, etc. Drone technology is booming for its easy-to-use architecture and its ability to collect data where human reach ability is restricted. Drones combined with IOT devices and connected to cloud services can generate massive data. To train these algorithms better, you still need to access more data. A single decentralized system that can execute a Machine Learning algorithm on a single node And then pass the trained model to another node for further training.
This book is intended for professionals, researchers, and students who are interested in aerial computing, drone data analytics, and their applications. It is also ideal for data scientists, engineers, and practitioners who are looking to expand their knowledge and skills in this field.