[hpc-announce] CALL FOR PAPERS - Future Generation Computer Systems Journal Special Issue: Edge-Cloud Solutions for Big Data Analysis and Distributed Machine Learning

[Wisser, Grace]

CALL FOR PAPERS - Future Generation Computer Systems Journal Special Issue

Edge-Cloud Solutions for Big Data Analysis and Distributed Machine Learning

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MOTIVATION AND SCOPE

Recently there has been a widespread use of edge-cloud solutions to efficiently collect and analyze large amounts of data generated by IoT devices. In many application domains, such as urban mobility, smart cities, healthcare, augmented reality, it is extremely useful to combine resources, applications and services from the edge to the cloud, in order to better support tasks that require real-time processing and analysis, low response times, as well as large computing and storage resources. This approach can help to reduce the latency and network congestion associated with traditional cloud-based Big Data analysis techniques, as the processing can be performed locally on edge devices before being sent to the cloud for further analysis. Big data analysis on the Edge-Cloud involves using advanced data analytics techniques and frameworks to process and process data that is distributed across the infrastructure, having several applications like predictive maintenance, real-time monitoring of industrial processes, smart grid management, and personalized healthcare.

Edge-Cloud solutions are also proving to be very effective in the field of distributed machine learning algorithms to distribute computation and data across the edge and the cloud to achieve efficient, scalable and accurate predictive models. This is a very promising approach that can help organizations to develop intelligent applications that can operate in real-time and make decisions autonomously. However, Big data analysis on the Edge-Cloud also poses several challenges, such as data privacy and security, interoperability, scalability, energy efficiency. Those challenges must be addressed to provide efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining.

We invite original research articles, review articles, and technical notes related to the area of Big Data Analysis and Machine Learning in Edge-Cloud platforms. The objective of this special issue is to provide a venue for researchers, academicians, and industry practitioners to present their latest findings and share their ideas on the latest trends, challenges, and opportunities in this field.

Topics of interest include, but are not limited to:

*Edge-Cloud architectures and infrastructures for Big Data analysis and machine learning;
*Scalable and distributed machine learning algorithms for edge-cloud computing environments;
*Programming models for Big Data analytics on the edge-cloud environments;
*Real-time data analytics on edge devices and cloud infrastructures;
*Federated learning and transfer learning on edge-cloud architectures;
*Security and privacy in edge-cloud computing for big data analysis and machine learning;
*Case studies and real-world applications of Big Data analysis and machine learning in Edge-Cloud computing;
*Edge-Cloud resource allocation and scheduling techniques for efficient data processing;
*Edge-Cloud integration with blockchain technology for secure and decentralized data processing;
*Energy-efficient edge-cloud computing solutions;
*Edge-cloud solutions for autonomous vehicles and smart transportation systems;
*Performance optimization and load balancing in Edge-Cloud architectures;
*Emulation and/or simulation approaches for testing and evaluating large scale Edge-Cloud applications;
*Edge-Cloud solutions for augmented and virtual reality applications;
*Edge-Cloud solutions for smart grid systems and renewable energy integration;
*Edge-Cloud solutions for smart healthcare systems and medical applications;
*Edge-Cloud solutions for disaster response and emergency management systems.
 
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GUEST EDITORS

Domenico Talia, University of Calabria, Italy.
talia at dimes.unical.it 

Jesus Carretero Perez, University Carlos III, Spain.
jcarrete at inf.uc3m.es 

Loris Belcastro, University of Calabria, Italy.
lbelcastro at dimes.unical.it 

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IMPORTANT DATE

Deadline for paper submission: Nov 30, 2023

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MANUSCRIPT SUBMISSION INSTRUCTIONS

The FGCS’s submission system (https://www.editorialmanager.com/FGCS/default.aspx) is now open for submissions to our Special Issue from May 20, 2023. When submitting your manuscript please select the article type VSI: Edge-Cloud for Big Data.

All submissions deemed suitable by the editors to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production to be published in the special issue.

GUIDE FOR AUTHORS: https://www.elsevier.com/journals/future-generation-computer-systems/0167-739X/guide-for-authors

* Please use Elsevier's Latex Style with "5p, times" option. So your document should start with \documentclass[5p,times]{elsarticle}
* You are recommended to use the Elsevier article class elsarticle.cls (https://ctan.org/tex-archive/macros/latex/contrib/elsarticle) to prepare your manuscript and BibTeX (http://www.bibtex.org/) to generate your bibliography.
* Our LaTeX  (https://www.elsevier.com/latex) site has detailed submission instructions, templates and other information.
* Article submitted to FGCS should be strictly double column, single spaced and limited to 18 pages; including all figures, tables, references.
* A manuscript longer than 18 pages will not be considered for review and returned to author to be revised to the correct format.

To view a complete list of the Call for Papers for Future Generation Computer Systems Journal, visit: https://www.sciencedirect.com/journal/future-generation-computer-systems/about/call-for-papers