2024 and 2025 Feature Papers from Future Internet’s Editorial Board Members

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 594

Special Issue Editor


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Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; networking and communication; signal processing; smart systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We have launched a Special Issue titled “2024 and 2025 Feature Papers from Future Internet’s Editorial Board Members”. This Special Issue will be a collection of high-quality, open-access papers from the Editorial Board Members of Future Internet, or those recommended and invited by the Editorial Board Members and the Editor-in-Chief. 

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

  • computer networking/communications and information systems;
  • Internet of Things;
  • big data and augmented intelligence;
  • smart systems (in terms of technologies, architectures, and applications);
  • network virtualization;
  • edge/fog computing;
  • cybersecurity.

Prof. Dr. Gianluigi Ferrari
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1600 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

  • Internet of Things
  • artificial intelligence
  • network virtualization
  • cybersecurity

Published Papers (2 papers)

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Research

14 pages, 3202 KiB  
Article
Reversible Data Hiding in Encrypted 3D Mesh Models Based on Multi-Group Partition and Closest Pair Prediction
by Xu Wang, Jui-Chuan Liu, Ching-Chun Chang and Chin-Chen Chang
Future Internet 2024, 16(6), 210; https://doi.org/10.3390/fi16060210 - 15 Jun 2024
Viewed by 144
Abstract
The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. [...] Read more.
The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. However, studies on reversible data hiding in encrypted 3D mesh models are still in the preliminary stage. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to improve performance. The MGP technique adaptively classifies vertices into reference and embeddable vertices, while the CPP technique efficiently predicts embeddable vertices and generates shorter recovery information to vacate more redundancy for additional data embedding. Experimental results indicate that the proposed scheme significantly improves the embedding rate compared to state-of-the-art schemes and can be used in real-time applications. Full article
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22 pages, 890 KiB  
Article
Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems
by Tahani Baabdullah, Amani Alzahrani, Danda B. Rawat and Chunmei Liu
Future Internet 2024, 16(6), 196; https://doi.org/10.3390/fi16060196 - 2 Jun 2024
Viewed by 222
Abstract
Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment [...] Read more.
Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy. Full article
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