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Future Internet
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Cloud and Edge Computing for the Next-Generation Networks
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Cloud and Edge Computing for the Next-Generation Networks

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 22519

Special Issue Editors

Dr. Jordi Mateo-Fornés

E-Mail Website
Guest Editor
Department of Computer Engineering and Digital Design, University of Lleida, Jaume II 69, 25001 Lleida, Spain
Interests: distributed computing; cloud computing; edge computing; fog computing; e-health; decision support systems; agriculture; parallel computing; IoT
Prof. Dr. Choong Seon Hong

grade E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Republic of Korea
Interests: edge computing; machine learning; networking intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The computing world is rapidly transforming into an expansive, interconnected, highly diverse distributed system. Internet of Things (IoT) devices are at the forefront of this evolution, generating unprecedented volumes of data. This surge in data production is forerunning a new era of computing paradigms, necessitating a fundamental shift from centralized cloud to decentralized edge computation. This transformation requires enabling distributed intelligence for efficient data processing and communication and ensuring a seamless continuum between the edge and cloud. This evolution shapes the future of resilient, efficient, and intelligent next-generation networks.

This Special Issue delves into the challenges and opportunities presented by this transition. Our focus is on cloud and edge computing, particularly in the context of distributed intelligence and networking. Distributed intelligence empowers a system to make autonomous decisions, stay updated, and operate based on local data and models without centralized control and coordination.

Potential submission topics include the following:

  • Edge computing in IoT and 5G networks.
  • Next-generation network architectures for the cloud-edge continuum.
  • Strategies for task offloading in the cloud-edge continuum.
  • Resource management and allocation using distributed intelligence.
  • Update mechanisms for distributed learning systems.
  • Policies for resilient, fault-tolerant distributed computing.
  • Privacy-aware processing and communication in distributed systems.

Dr. Jordi Mateo-Fornés
Prof. Dr. Choong Seon Hong
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. Future Internet is an international peer-reviewed open access monthly 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 1800 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

  • task offloading
  • cloud-edge continuum
  • 5G network architectures
  • Internet of Things (IoT)
  • distributed intelligence
  • resource management
  • resilience
  • privacy-aware processing
  • trust mechanisms
  • edge AI models

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Published Papers (5 papers)

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Research

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26 pages, 3132 KB  
Article
An Unsupervised Cloud-Centric Intrusion Diagnosis Framework Using Autoencoder and Density-Based Learning
by Suresh K. S, Thenmozhi Elumalai, Radhakrishnan Rajamani, Anubhav Kumar, Balamurugan Balusamy, Sumendra Yogarayan and Kaliyaperumal Prabu
Future Internet 2026, 18(1), 54; https://doi.org/10.3390/fi18010054 - 19 Jan 2026
Viewed by 223
Abstract
Cloud computing environments generate high-dimensional, large-scale, and highly dynamic network traffic, making intrusion diagnosis challenging due to evolving attack patterns, severe traffic imbalance, and limited availability of labeled data. To address these challenges, this study presents an unsupervised, cloud-centric intrusion diagnosis framework that [...] Read more.
Cloud computing environments generate high-dimensional, large-scale, and highly dynamic network traffic, making intrusion diagnosis challenging due to evolving attack patterns, severe traffic imbalance, and limited availability of labeled data. To address these challenges, this study presents an unsupervised, cloud-centric intrusion diagnosis framework that integrates autoencoder-based representation learning with density-based attack categorization. A dual-stage autoencoder is trained exclusively on benign traffic to learn compact latent representations and to identify anomalous flows using reconstruction-error analysis, enabling effective anomaly detection without prior attack labels. The detected anomalies are subsequently grouped using density-based learning to uncover latent attack structures and support fine-grained multiclass intrusion diagnosis under varying attack densities. Experiments conducted on the large-scale CSE-CIC-IDS2018 dataset demonstrate that the proposed framework achieves an anomaly detection accuracy of 99.46%, with high recall and low false-negative rates in the optimal latent-space configuration. The density-based classification stage achieves an overall multiclass attack classification accuracy of 98.79%, effectively handling both majority and minority attack categories. Clustering quality evaluation reports a Silhouette Score of 0.9857 and a Davies–Bouldin Index of 0.0091, indicating strong cluster compactness and separability. Comparative analysis against representative supervised and unsupervised baselines confirms the framework’s scalability and robustness under highly imbalanced cloud traffic, highlighting its suitability for future Internet cloud security ecosystems. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for the Next-Generation Networks)
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23 pages, 3647 KB  
Article
A Physics-Aware Latent Diffusion Framework for Mitigating Adversarial Perturbations in Manufacturing Quality Control
by Nikolaos Nikolakis and Paolo Catti
Future Internet 2026, 18(1), 23; https://doi.org/10.3390/fi18010023 - 1 Jan 2026
Viewed by 494
Abstract
Data-driven quality control (QC) systems for the hot forming of steel parts increasingly rely on deep learning models deployed at the network edge, making multivariate sensor time series a critical asset for both local decisions and management information system (MIS) reporting. However, these [...] Read more.
Data-driven quality control (QC) systems for the hot forming of steel parts increasingly rely on deep learning models deployed at the network edge, making multivariate sensor time series a critical asset for both local decisions and management information system (MIS) reporting. However, these models are vulnerable to adversarial perturbations and realistic signal disturbances, which can induce misclassification and distort key performance indicators (KPIs) such as first-pass yield (FPY), scrap-related losses, and latency service-level objectives (SLOs). To address this risk, this study introduces a Digital-Twin-Conditioned Diffusion Purification (DTCDP) framework that constrains latent diffusion-based denoising using process states from a lightweight digital twin of the hot-forming line. At each reverse-denoising step, the twin provides physics residuals that are converted into a scalar penalty, and the diffusion latent is updated with a guidance term. This directly bends the sampling trajectory toward reconstructions that adhere to process constraints while removing adversarial perturbations. DTCDP operates as an edge-side preprocessing module that purifies sensor sequences before they are consumed by existing long short-term memory (LSTM)-based QC models, while exposing purification metadata and physics-guidance diagnostics to the plant MIS. In a four-week production dataset comprising more than 40,000 bars, with white-box ℓ∞ attacks crafted on multivariate sensor time series using Fast Gradient Sign Method and Projected Gradient Descent at perturbation budgets of 1–3% of the physical range, combined with additional realistic disturbances, DTCDP improves the robust classification performance of an LSTM-based QC model from 61.0% to 81.5% robust accuracy, while keeping clean accuracy (≈93%) and FPY on clean data (≈97%) essentially unchanged. These results indicate that physics-aware, digital-twin-guided diffusion purification can enhance the adversarial robustness of edge QC in hot forming without compromising operational KPIs. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for the Next-Generation Networks)
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30 pages, 5336 KB  
Article
Railway Cloud Resource Management as a Service
by Ivaylo Atanasov, Dragomira Dimitrova, Evelina Pencheva and Ventsislav Trifonov
Future Internet 2025, 17(5), 192; https://doi.org/10.3390/fi17050192 - 24 Apr 2025
Cited by 7 | Viewed by 3528
Abstract
Cloud computing has the potential to accelerate the digital journey of railways. Railway systems are big and complex, involving a lot of parts, like trains, tracks, signaling systems, and control systems, among others. The application of cloud computing technologies in the railway industry [...] Read more.
Cloud computing has the potential to accelerate the digital journey of railways. Railway systems are big and complex, involving a lot of parts, like trains, tracks, signaling systems, and control systems, among others. The application of cloud computing technologies in the railway industry has the potential to enhance operational efficiency, data management, and overall system performance. Cloud management is essential for complex systems, and the automation of management services can speed up the provisioning, deployment, and maintenance of cloud infrastructure and applications by enabling visibility across the environment. It can provide consistent and unified management over resource allocation, streamline security processes, and automate the monitoring of key performance indicators. Key railway cloud management challenges include the lack of open interfaces and standardization, which are related to the vendor lock-in problem. In this paper, we propose an approach to design the railway cloud resource management as a service. Based on typical use cases, the requirements to fault and performance management of the railway cloud resources are identified. The main functionality is designed as RESTful services. The approach feasibility is proved by formal verification of the cloud resource management models supported by cloud management application and services. The proposed approach is open, in contrast to any proprietary solutions and feature scalability and interoperability. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for the Next-Generation Networks)
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19 pages, 2534 KB  
Article
A Cross-Chain-Based Access Control Framework for Cloud Environment
by Saad Belcaid, Mostapha Zbakh, Siham Aouad, Abdellah Touhafi and An Braeken
Future Internet 2025, 17(4), 149; https://doi.org/10.3390/fi17040149 - 27 Mar 2025
Cited by 1 | Viewed by 1411
Abstract
Cloud computing presents itself as one of the leading technologies in the IT solutions field, providing a variety of services and capabilities. Meanwhile, blockchain-based solutions emerge as advantageous as they permit data immutability, transaction efficiency, transparency, and trust due to decentralization and the [...] Read more.
Cloud computing presents itself as one of the leading technologies in the IT solutions field, providing a variety of services and capabilities. Meanwhile, blockchain-based solutions emerge as advantageous as they permit data immutability, transaction efficiency, transparency, and trust due to decentralization and the use of smart contracts. In this paper, we are consolidating these two technologies into a secure framework for access control in cloud environments. A cross-chain-based methodology is used, in which transactions and interactions between multiple blockchains and cloud computing systems are supported, such that no separate third-party certificates are required in the authentication and authorization processes. This paper presents a cross-chain-based framework that integrates a full, fine-grained, attribute-based access control (ABAC) mechanism that evaluates cloud user access transaction attributes. It grants or denies access to the cloud resources by inferring knowledge about the attributes received using semantic reasoning based on ontologies, resulting in a more reliable method for information sharing over the cloud network. Our implemented cross-chain framework on the Cosmos ecosystem with the integrated semantic ABAC scored an overall access control (AC) processing time of 9.72 ms. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for the Next-Generation Networks)
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Review

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54 pages, 5836 KB  
Review
A Survey on Edge Computing (EC) Security Challenges: Classification, Threats, and Mitigation Strategies
by Abdul Manan Sheikh, Md. Rafiqul Islam, Mohamed Hadi Habaebi, Suriza Ahmad Zabidi, Athaur Rahman Bin Najeeb and Adnan Kabbani
Future Internet 2025, 17(4), 175; https://doi.org/10.3390/fi17040175 - 16 Apr 2025
Cited by 14 | Viewed by 15205
Abstract
Edge computing (EC) is a distributed computing approach to processing data at the network edge, either by the device or a local server, instead of centralized data centers or the cloud. EC proximity to the data source can provide faster insights, response time, [...] Read more.
Edge computing (EC) is a distributed computing approach to processing data at the network edge, either by the device or a local server, instead of centralized data centers or the cloud. EC proximity to the data source can provide faster insights, response time, and bandwidth utilization. However, the distributed architecture of EC makes it vulnerable to data security breaches and diverse attack vectors. The edge paradigm has limited availability of resources like memory and battery power. Also, the heterogeneous nature of the hardware, diverse communication protocols, and difficulty in timely updating security patches exist. A significant number of researchers have presented countermeasures for the detection and mitigation of data security threats in an EC paradigm. However, an approach that differs from traditional data security and privacy-preserving mechanisms already used in cloud computing is required. Artificial Intelligence (AI) greatly improves EC security through advanced threat detection, automated responses, and optimized resource management. When combined with Physical Unclonable Functions (PUFs), AI further strengthens data security by leveraging PUFs’ unique and unclonable attributes alongside AI’s adaptive and efficient management features. This paper investigates various edge security strategies and cutting-edge solutions. It presents a comparison between existing strategies, highlighting their benefits and limitations. Additionally, the paper offers a detailed discussion of EC security threats, including their characteristics and the classification of different attack types. The paper also provides an overview of the security and privacy needs of the EC, detailing the technological methods employed to address threats. Its goal is to assist future researchers in pinpointing potential research opportunities. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for the Next-Generation Networks)
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