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Electronics
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Advances in Mobile Networked Systems
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Advances in Mobile Networked Systems

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 1742

Special Issue Editors

Dr. Wei Cui

E-Mail Website
Guest Editor
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
Interests: AI-enabled internet of things; data analysis; smart city
Dr. Yaoming Zhuang

E-Mail Website
Guest Editor
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: wireless sensor networks; intelligent robot system; machine learning
Dr. Wei Zhou

E-Mail Website
Guest Editor
College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
Interests: anomaly detection; computer vision; image processing

Special Issue Information

Dear Colleagues,

Mobile networked systems have become an integral part of our daily lives, facilitating seamless communication and information sharing. These advanced systems have led to significant advancements in various areas, such as the Internet of Things (IoT), robotics, and healthcare.

The integration of mobile networked systems with the IoT has significant implications for a wide range of applications. One notable example is in the domain of smart cities, where this combination enables advanced functionalities like human activity recognition and anomaly detection. Additionally, in the industry context, mobile networked systems working with IoT drive automation in manufacturing processes, improving productivity and optimizing operations. Furthermore, mobile networked systems have played a vital role in advancing robotics, enabling remote control, coordination, and collaboration among robotic systems. Moreover, in healthcare, advanced mobile networked systems have led to a revolution in elderly/patient care, enabling remote monitoring, telemedicine services, and personalized healthcare applications.

The aim of this Special Issue of Electronics is to present state-of-the-art investigations into various advanced mobile networked systems for future applications. We invite researchers to contribute original and unique articles, as well as sophisticated review articles. The topics include, but are not limited to, the following areas:

  1. Machine learning and deep learning-based smart city;
  2. Applications in smart IoT and wireless sensor networks;
  3. Applications in industrial automation with mobile networks;
  4. Networked robot systems;
  5. Wireless communication and control in robotics;
  6. Networked sensor systems for robotics;
  7. Artificial intelligence and machine learning in networked robotics;
  8. Human–robot interaction and collaboration in networked environments;
  9. Biomedical and health monitoring with networked systems;
  10. Privacy-enhanced machine learning and deep learning for mobile networked systems.

Dr. Wei Cui
Dr. Yaoming Zhuang
Dr. Wei Zhou
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 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

  • mobile networked systems
  • artificial intelligence
  • machine learning
  • networked sensor systems
  • networked robot systems
  • wireless communication
  • Internet of Things (IoT)
  • smart city / smart manufacture
  • human activity recognition / anomaly detection / localization
  • privacy preservation

Published Papers (2 papers)

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Research

23 pages, 1573 KiB  
Article
Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things
by Grzegorz Czeczot, Izabela Rojek and Dariusz Mikołajewski
Electronics 2024, 13(6), 1161; https://doi.org/10.3390/electronics13061161 - 21 Mar 2024
Cited by 1 | Viewed by 592
Abstract
Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules [...] Read more.
Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests. Full article
(This article belongs to the Special Issue Advances in Mobile Networked Systems)
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20 pages, 6864 KiB  
Article
Privacy-Preserving Vertical Federated KNN Feature Imputation Method
by Wenyou Du, Yichen Wang, Guanglei Meng and Yuming Guo
Electronics 2024, 13(2), 381; https://doi.org/10.3390/electronics13020381 - 17 Jan 2024
Viewed by 669
Abstract
Federated learning stands as a pivotal component in the construction of data infrastructure. It significantly fortifies the safety and reliability of data circulation links, facilitating credible sharing and openness among diverse subjects. The presence of missing data poses a pervasive and challenging issue [...] Read more.
Federated learning stands as a pivotal component in the construction of data infrastructure. It significantly fortifies the safety and reliability of data circulation links, facilitating credible sharing and openness among diverse subjects. The presence of missing data poses a pervasive and challenging issue in the implementation of federated learning. Current research on imputation missing values predominantly concentrates on centralized methods and horizontal federation scenarios. However, there is a notable absence of exploration in the context of vertical federated application scenarios. In this paper, the problem of missing imputation in vertical federated learning is investigated and a novel vertical federated k-nearest neighbors (KNN) imputation method is proposed. Extensive experiments are conducted using publicly available data sets to compare existing imputation methods, the results demonstrate the effectiveness and progress of our approach. Full article
(This article belongs to the Special Issue Advances in Mobile Networked Systems)
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