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 2481

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 (6 papers)

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Research

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24 pages, 16681 KiB  
Article
Achieving Accountability and Data Integrity in Message Queuing Telemetry Transport Using Blockchain and Interplanetary File System
by Sara Lazzaro and Francesco Buccafurri
Future Internet 2024, 16(7), 246; https://doi.org/10.3390/fi16070246 - 13 Jul 2024
Viewed by 203
Abstract
Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates [...] Read more.
Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates in discrete temporal rounds, during which the broker constructs a Merkle hash tree (MHT) from the messages received. Then the broker publishes the root on the blockchain and the MHT itself on IPFS. This mechanism guarantees that both publishers and subscribers can verify the integrity of the message exchanged. Furthermore, the interactions with the blockchain made by the publishers and the broker ensure they cannot deny having sent the exchanged messages. We provide a detailed security analysis, showing that under standard assumptions, the proposed solution achieves both data integrity and accountability. Additionally, we provided an experimental campaign to study the scalability and the throughput of the system. Our results show that our solution scales well with the number of clients. Furthermore, from our results, it emerges that the throughput reduction depends on the integrity check operations. However, since the frequency of these checks can be freely chosen, we can set it so that the throughput reduction is negligible. Finally, we provided a detailed analysis of the costs of our solution showing that, overall, the execution costs are relatively low, especially given the critical security and accountability benefits it guarantees. Furthermore, our analysis shows that the higher the number of subscribers in the system, the lower the costs per client in our solution. Again, this confirms that our solution does not present any scalability issues. Full article
18 pages, 715 KiB  
Article
Optimizing Drone Energy Use for Emergency Communications in Disasters via Deep Reinforcement Learning
by Wen Qiu, Xun Shao, Hiroshi Masui and William Liu
Future Internet 2024, 16(7), 245; https://doi.org/10.3390/fi16070245 - 11 Jul 2024
Viewed by 263
Abstract
For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration [...] Read more.
For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration of UAVs remains a major challenge due to limitations in their communication range and energy capacity. In addition, the relatively high cost of drones and the issue of mutual communication interference make it impractical to deploy an unlimited number of drones in a given area. To maximize the communication services provided by a limited number of drones to the ground user equipment (UE) within a certain time frame while minimizing the drone energy consumption, we propose a multi-agent proximal policy optimization (MAPPO) algorithm. Considering the dynamic nature of the environment, we analyze diverse observation data structures and design novel objective functions to enhance the drone performance. We find that, when drone energy consumption is used as a penalty term in the objective function, the drones—acting as agents—can identify the optimal trajectory that maximizes the UE coverage while minimizing the energy consumption. At the same time, the experimental results reveal that, without considering the machine computing power required for training and convergence time, the proposed key algorithm demonstrates better performance in communication coverage and energy saving as compared with other methods. The average coverage performance is 1045% higher than that of the other three methods, and it can save up to 3% more energy. Full article
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16 pages, 729 KiB  
Article
INFLUTRUST: Trust-Based Influencer Marketing Campaigns in Online Social Networks
by Adedamola Adesokan, Aisha B Rahman and Eirini Eleni Tsiropoulou
Future Internet 2024, 16(7), 222; https://doi.org/10.3390/fi16070222 - 25 Jun 2024
Viewed by 368
Abstract
This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning [...] Read more.
This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms. Full article
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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 426
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, 912 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 442
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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Review

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23 pages, 714 KiB  
Review
Smart Irrigation Systems from Cyber–Physical Perspective: State of Art and Future Directions
by Mian Qian, Cheng Qian, Guobin Xu, Pu Tian and Wei Yu
Future Internet 2024, 16(7), 234; https://doi.org/10.3390/fi16070234 - 29 Jun 2024
Viewed by 422
Abstract
Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The [...] Read more.
Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, developing countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people’s rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber–physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT’s applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring, autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field. Full article
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