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Electronics
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Data-Driven Innovations in Networked Systems and Applications: Recent Developments and...
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Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends

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

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 4483

Special Issue Editor

Prof. Dr. Ming Liu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China
Interests: cloud computing; big data; deep learning; IoT; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Networked systems and applications are at the heart of the digital revolution, powering the connected world we live in. The relentless march of innovation in this domain is redefining the boundaries of what is possible. At the core of this transformation lies data—the lifeblood of our connected ecosystems.  From 5G's connectivity revolution to IoT's intelligent interconnectivity and edge computing's real-time processing, the latest trends are shaping a future in which our networks optimize efficiency and redefine security and privacy. If you're at the forefront of this digital evolution—leveraging data analytics, AI, and ML, and pioneering novel applications—we invite you to share your insights. Join us on a journey into the heart of data-driven innovation, where networks and applications shape a smarter, connected world.

The scope of the Special Issue includes, but is not limited to, the following topics:

  • Emerging Technologies: Exploration of emerging technologies, such as 5G, IoT, edge computing, and beyond, and their impact on networked systems and applications.
  • Network Design and Architecture: Innovations in network design, architecture, and protocols to enhance performance, scalability, and efficiency.
  • Wireless and Mobile Communications: Advancements in wireless and mobile networking, including mobile ad-hoc networks, vehicular networks, and mobile app development.
  • Cloud Computing and Virtualization: Strategies for efficient cloud utilization, virtualization technologies, and cloud-based applications.
  • Data Management and Analytics: Techniques for data management, analytics, and big data processing in networked systems.
  • Security and Privacy: Innovations in network security, privacy preservation, and threat detection.
  • Applications and Use Cases: Case studies and novel applications of networked systems across various domains, including healthcare, transportation, smart cities, and more.
  • Scalability and Performance Optimization: Methods to enhance the scalability and performance of networked systems to meet the growing demands of the digital age.

Prof. Dr. Ming Liu
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. 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

  • networked systems and applications
  • Internet of Things (IoT)
  • artificial intelligence (AI)
  • big data

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Published Papers (3 papers)

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Research

17 pages, 1218 KiB  
Article
Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
by Lin Li, Zhenchuan Wang, Jinqi Zhu and Shizhao Ma
Electronics 2024, 13(15), 2903; https://doi.org/10.3390/electronics13152903 - 23 Jul 2024
Viewed by 982
Abstract
Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster [...] Read more.
Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster relief operations. However, UAVs are limited by their battery capacity, which makes it hard for them to perform both large-scale information gathering and data processing at the same time. Nowadays, smartphones (SPs), which have become portable devices for people, have the characteristics of strong computing power, rich communication means and wide distribution. Therefore, in this study, we developed SPs to assist UAVs in computation incentive-based task execution. To balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem was established, and the Multi-Objective Mutation-Immune Bat (MOMIB) algorithm was developed to optimize the proposed problem. Additionally, considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to ensure the performance requirements of different types of tasks. A large number of simulation experiments show that the task-offloading scheme that we propose is effective. Full article
(This article belongs to the Special Issue Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends)
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23 pages, 849 KiB  
Article
PDPHE: Personal Data Protection for Trans-Border Transmission Based on Homomorphic Encryption
by Yan Liu, Changshui Yang, Qiang Liu, Mudi Xu, Chi Zhang, Lihong Cheng and Wenyong Wang
Electronics 2024, 13(10), 1959; https://doi.org/10.3390/electronics13101959 - 16 May 2024
Cited by 3 | Viewed by 1535
Abstract
In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few [...] Read more.
In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing. Full article
(This article belongs to the Special Issue Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends)
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14 pages, 439 KiB  
Article
Ship Network Traffic Engineering Based on Reinforcement Learning
by Xinduoji Yang, Minghui Liu, Xinxin Wang, Bingyu Hu, Meng Liu and Xiaomin Wang
Electronics 2024, 13(9), 1710; https://doi.org/10.3390/electronics13091710 - 29 Apr 2024
Cited by 1 | Viewed by 1457
Abstract
This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction [...] Read more.
This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction priority, and complex network structure—to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network’s quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%. Full article
(This article belongs to the Special Issue Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends)
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