Edge-cloud solutions for big data analysis and distributed machine learning - 2

Abstract

In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated techniques for distributed data processing, enabling applications such as predictive maintenance and smart grid management. Nevertheless, carrying out big data analysis within the edge-cloud presents several challenges, including data privacy and security, interoperability, scalability, and energy efficiency. Addressing these challenges is imperative for providing efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining. The special issue concludes with 27 scientific papers, divided into two parts for a streamlined editorial process. This editorial, as part two, presents 12 rigorously peer-reviewed papers, complementing the 15 papers covered in the previous editorial.