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Electronics
Special Issues
Cloud Computing, IoT, and Big Data: Technologies and Applications
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Cloud Computing, IoT, and Big Data: Technologies and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 4278

Special Issue Editors

Dr. Wajid Rafique

E-Mail Website
Guest Editor
Ericsson Canada Inc., Ville Saint Laurent, QC H4S 0B6, Canada
Interests: cybersecurity; edge/cloud computing; SDN
Dr. Sudheer Kumar Battula

E-Mail Website
Guest Editor
Centre for Smart Analytics, Federation University Australia, Gippsland Campus, Churchill, VIC 3841, Australia
Interests: fog/edge computing; crowd computing; blockchain; next-generation computing

Special Issue Information

Dear Colleagues,

This Special Issue on "Cloud Computing, IoT, and Big Data: Technologies and Applications" aims to gather cutting-edge research and advancements in these inter-related fields. It will cover a broad spectrum of topics, including the latest developments in cloud infrastructure, innovative applications of IoT technologies, and sophisticated big data analytics techniques. Our goal is to provide a comprehensive platform for researchers and practitioners to share their findings, discuss challenges, and explore future directions in leveraging these technologies for various applications, from smart cities to healthcare and beyond. We invite original research articles, reviews, and case studies that contribute to the understanding and advancement of these dynamic areas.

Dr. Wajid Rafique
Dr. Sudheer Kumar Battula
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cloud computing
  • big data analytics
  • smart cities
  • data security and privacy
  • machine learning in cloud and IoT
  • edge computing
  • IoT applications
  • big data infrastructure
  • cloud services

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 4674 KB  
Article
Merging Deep Learning Neural Networks with the Stochastic Parameterized Expectations Algorithm for Solving Nonlinear Rational Expectations Models
by Alexie Alupoaiei, Leonardo Badea, Iulian Panait, Valentin Radu and Mircea Constantin Șcheau
Electronics 2025, 14(23), 4712; https://doi.org/10.3390/electronics14234712 - 29 Nov 2025
Viewed by 223
Abstract
This paper proposes a novel framework that integrates Deep Learning Neural Networks into the Stochastic Parameterized Expectations Algorithm (DLNN-PEA) to solve nonlinear rational expectations models. This method enhances traditional PEA-based solvers by employing a deep neural expectations operator that captures complex nonlinearities and [...] Read more.
This paper proposes a novel framework that integrates Deep Learning Neural Networks into the Stochastic Parameterized Expectations Algorithm (DLNN-PEA) to solve nonlinear rational expectations models. This method enhances traditional PEA-based solvers by employing a deep neural expectations operator that captures complex nonlinearities and asymmetries. The DLNN-PEA is implemented in Matlab R2024b. It combines deep learning approximation with the standard expectation-iteration structure of the PEA, replacing the conventional shallow ANN-based operator with a deeper architecture that improves both accuracy and stability. The methodology is applied to the stochastic Neoclassical Growth Model, where the DLNN-PEA is trained to approximate conditional expectations and decision rules under uncertainty. The results show rapid convergence, reduced boundary-related issues, and stable performance even in high-volatility environments. Compared with ANN-PEA, deep architectures exhibit greater robustness and adaptability, making them suitable for economic models characterized by stronger nonlinearities and richer state dynamics. Beyond the benchmark model, the proposed framework is well-suited for medium-scale DSGE models, nonlinear monetary policy environments, and macro-financial simulations involving high-dimensional state spaces. These features make DLNN-PEA a practical tool for applied macroeconomic analysis and model-based policy evaluation. Full article
(This article belongs to the Special Issue Cloud Computing, IoT, and Big Data: Technologies and Applications)
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34 pages, 16501 KB  
Article
Vehicle-to-Everything-Car Edge Cloud Management with Development, Security, and Operations Automation Framework
by DongHwan Ku, Hannie Zang, Anvarjon Yusupov, Sun Park and JongWon Kim
Electronics 2025, 14(3), 478; https://doi.org/10.3390/electronics14030478 - 24 Jan 2025
Cited by 1 | Viewed by 3365
Abstract
Modern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic [...] Read more.
Modern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic and resource-constrained edge environments. To address these challenges, this study introduces the V2X-Car Edge Cloud system, which is a cloud-native architecture driven by DevSecOps principles to ensure secure deployment, dynamic resource orchestration, and real-time monitoring across distributed edge nodes. The proposed system integrates multicluster orchestration with Kubernetes, hybrid communication protocols (C-V2X, 5G, and WAVE), and data-fusion pipelines to enhance transparency in artificial intelligence (AI)-driven decision making. A software-in-the-loop simulation environment was implemented to validate AI models, and the SmartX MultiSec framework was integrated into the proposed system to dynamically monitor network traffic flow and security. Experimental evaluations in a virtual driving environment demonstrate the ability of the proposed system to perform automated security updates, continuous performance monitoring, and dynamic resource allocation without manual intervention. Full article
(This article belongs to the Special Issue Cloud Computing, IoT, and Big Data: Technologies and Applications)
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