Integration of Cloud Edge Computing into IoT in Modern Industrial Environments

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 346

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40138 Bologna, Italy
Interests: cloud and edge computing; Internet of Things; distributed systems; mobile computing and middleware; cloud continuum; big data; industrial Internet of Things; industry 4/5.0

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Guest Editor
Institute of Science and Engineering, Kanazawa University, Kanazawa 920-1192, Japan
Interests: future network; big data; quantum network; metaverse; blockchain; B5G/6G; Internet of Things (IoT); machine learning; security

Special Issue Information

Dear Colleagues,

The integration of cloud edge computing into Internet of Things (IoT) in modern industrial scenarios represents a pivotal development in enhancing industrial efficiency, reliability, and innovation. This amalgamation leverages the strengths of both cloud and edge computing to address the unique demands of industrial environments, where real-time data processing, robust security, and efficient data management are critical.

This integration has proven to be one of the pillars of the Industry 4.0 revolution. I4.0 has fostered a deep change in the rigid organization model for factories. The multiple implications of the “connected factory" concept will lead to unexpected developments in production environments. Connected and interacting IoT objects will populate the factories of the future.

A progressive and decisive integration of cloud edge computing paradigms into modern industrial IoT objects is expected to occur, which practitioners refer to as the modern industrial digitalization, emphasizing the shortening of the gap between the manufacturing processes running on the shop floor and the storage, networking, computing resources, virtualization, and business-orientated software suites of the cloud and edge paradigms.

In this context, we propose this Special Issue, addressing “The Integration of Cloud Edge computing into IoT in modern Industrial Environments”. The focus of this Special Issue will be on technologies, paradigms, practices, and systems that ease or enable this revolutionary integration.

Emergent paradigms including artificial intelligence (AI), privacy-enhancing technologies (PETs), cybersecurity, blockchain (BC), IIoT systems and platforms, Cloud Continuum, zero defect/waste manufacturing, big data, industrial sustainability, advanced service orchestration in industrial deployments, 5G/6G communication, digital twins (DTs), and the metaverse are key aspects shaping the development of modern smart factories built on the integration of Cloud Edge computing into IoT.

Topics of interest include, but are not limited, to the following:

  • Application of industrial Cloud Continuum;
  • IIoT platforms and service orchestration;
  • 5G/6G communication for industrial deployment;
  • Computing continuum for smart factory services;
  • Digital twins for industrial process or product monitoring;
  • Edge data center deployment in industrial environments;
  • ML- and AI-based approaches to cloud/edge-based smart factory applications;
  • AI-driven models, architectures, and frameworks for industrial cloud edge computing;
  • Design and evaluation tools for scalability and efficient resource allocation in Industry 4.0;
  • Blockchain solutions for secure and reliable transactions between counterparts in data sharing/trading;
  • Post-quantum security and privacy for industrial applications;
  • Design and evaluation of microservice-based networking for 5G edge networks;
  • Zero waste/defect manufacturing;
  • Green manufacturing in Industry 5.0 deployment.

Dr. Riccardo Venanzi
Dr. Ruidong Li
Guest Editors

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Keywords

  • IoT
  • cloud continuum
  • industry 4/5.0
  • big data
  • AI/ML
  • predictive maintenance
  • anomaly detection
  • industrial 5G
  • ML/DevOps
  • smart manufacturing

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Published Papers (1 paper)

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Research

25 pages, 1615 KiB  
Article
Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
by Isam Mashhour Al Jawarneh, Lorenzo Rosa, Riccardo Venanzi, Luca Foschini and Paolo Bellavista
Electronics 2025, 14(13), 2626; https://doi.org/10.3390/electronics14132626 (registering DOI) - 29 Jun 2025
Abstract
The integration of distributed big data analytics into modern industrial environments has become increasingly critical, particularly with the rise of data-intensive applications and the need for real-time processing at the edge. While High-Performance Computing (HPC) systems offer robust petabyte-scale capabilities for efficient big [...] Read more.
The integration of distributed big data analytics into modern industrial environments has become increasingly critical, particularly with the rise of data-intensive applications and the need for real-time processing at the edge. While High-Performance Computing (HPC) systems offer robust petabyte-scale capabilities for efficient big data analytics, the performance of big data frameworks, especially on ARM-based HPC systems, remains underexplored. This paper presents an extensive experimental study on deploying Apache Spark 3.0.2, the de facto standard in-memory processing system, on an ARM-based HPC system. This study conducts a comprehensive performance evaluation of Apache Spark through representative big data workloads, including K-means clustering, to assess the effects of latency variations, such as those induced by network delays, memory bottlenecks, or computational overheads, on application performance in industrial IoT and edge computing environments. Our findings contribute to an understanding of how big data frameworks like Apache Spark can be effectively deployed and optimized on ARM-based HPC systems, particularly when leveraging vectorized instruction sets such as SVE, contributing to the broader goal of enhancing the integration of cloud–edge computing paradigms in modern industrial environments. We also discuss potential improvements and strategies for leveraging ARM-based architectures to support scalable, efficient, and real-time data processing in Industry 4.0 and beyond. Full article
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