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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: 15 June 2025 | Viewed by 9401

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
Special Issues, Collections and Topics in MDPI journals
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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

19 pages, 12741 KiB  
Article
Explainable Machine Learning-Based Electric Field Strength Mapping for Urban Environmental Monitoring: A Case Study in Paris Integrating Geographical Features and Explainable AI
by Yiannis Kiouvrekis, Ioannis Psomadakis, Kostas Vavouranakis, Sotiris Zikas, Ilias Katis, Ioannis Tsilikas, Theodor Panagiotakopoulos and Ioannis Filippopoulos
Electronics 2025, 14(2), 254; https://doi.org/10.3390/electronics14020254 - 9 Jan 2025
Cited by 1 | Viewed by 1051
Abstract
The objective of this study is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, advancing the field of environmental monitoring. These models are unique because they use a detailed dataset that goes beyond electromagnetic readings, [...] Read more.
The objective of this study is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, advancing the field of environmental monitoring. These models are unique because they use a detailed dataset that goes beyond electromagnetic readings, incorporating information like population density, urbanization levels, and building characteristics. This novel approach, combined with explainable AI, helps identify the key factors affecting electromagnetic exposure. The models enable the creation of highly detailed and dynamic maps of electromagnetic pollution. These maps are not just static snapshots, they can track changes over time, evaluate the success of mitigation efforts, and provide deeper insights into how electromagnetic fields are distributed in urban areas. To construct a detailed electric field strength map, we conducted an extensive analysis using 410 machine learning models across the urban area of Paris, incorporating three fundamental approaches: k-nearest neighbors, neural networks, and decision trees. This comprehensive exploration allowed us to evaluate and optimize various model configurations, ensuring robust and accurate predictions of electric field strength across diverse urban environments. The kNN model exhibited the most consistent performance, with an RMSE of 1.63 and an SD of 0.20. The analysis indicates that kNN outperforms simple neural networks and decision trees in terms of both RMSE and performance stability. From the SHAP analysis, we conclude that the feature representing the total volume of buildings in the area around each antenna (V) is the most significant in predicting electromagnetic field strength in the kNN regression model, consistently showing a high impact across predictions. The population density feature (POP) also demonstrates considerable influence. Full article
(This article belongs to the Special Issue Advances in Mobile Networked Systems)
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20 pages, 2859 KiB  
Article
A Mobility Handover Decision Method Based on Multi-Topology
by Chi Zhang, Haojiang Deng and Rui Han
Electronics 2024, 13(23), 4777; https://doi.org/10.3390/electronics13234777 - 3 Dec 2024
Viewed by 757
Abstract
With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain [...] Read more.
With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods. Full article
(This article belongs to the Special Issue Advances in Mobile Networked Systems)
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16 pages, 8826 KiB  
Article
Research on Path Planning Method for Autonomous Patrol Robots
by Qiang Zou, Haipeng Wang, Tianle Zhang, Zhengqi Li and Yaoming Zhuang
Electronics 2024, 13(14), 2865; https://doi.org/10.3390/electronics13142865 - 20 Jul 2024
Cited by 2 | Viewed by 1594
Abstract
For autonomous patrol robots, how to complete multi-point path planning efficiently is a crucial challenge. To address this issue, this work proposes a practical and efficient path planning method for patrol robots. Firstly, the evaluation function of the traditional A* method is improved [...] Read more.
For autonomous patrol robots, how to complete multi-point path planning efficiently is a crucial challenge. To address this issue, this work proposes a practical and efficient path planning method for patrol robots. Firstly, the evaluation function of the traditional A* method is improved to ensure that the planned path maintains a safe distance from the obstacles. Secondly, a Dubins curve is used to optimize the planned path to minimize the number of turning points while adhering to kinematic constraints. Thirdly, a trajectory-preserving strategy is proposed to preserve the continuous trajectory, linking multi-points for future inspection tasks. Finally, the proposed method is validated through both simulation and real-world experiments. Experimental results demonstrate that our proposed method performs exceptionally well in terms of safety, actual trajectory distance, and total execution efficiency. Full article
(This article belongs to the Special Issue Advances in Mobile Networked Systems)
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18 pages, 4038 KiB  
Article
Multi-Height and Heterogeneous Sensor Fusion Discriminant with LSTM for Weak Fire Signal Detection in Large Spaces with High Ceilings
by Li Wang, Boning Li, Xiaosheng Yu and Jubo Chen
Electronics 2024, 13(13), 2572; https://doi.org/10.3390/electronics13132572 - 30 Jun 2024
Viewed by 991
Abstract
Fire is a significant cause of fatalities and property loss. In tall spaces, early smoke dispersion is hindered by thermal barriers, and initial flames with limited smoke production may be obscured by ground-level structures. Consequently, smoke, temperature, and other fire sensor signals are [...] Read more.
Fire is a significant cause of fatalities and property loss. In tall spaces, early smoke dispersion is hindered by thermal barriers, and initial flames with limited smoke production may be obscured by ground-level structures. Consequently, smoke, temperature, and other fire sensor signals are weakened, leading to delays in fire detection by sensor networks. This paper proposes a multi-height and heterogeneous fusion discriminant model with a multilayered LSTM structure for the robust detection of weak fire signals in such challenging situations. The model employs three LSTM structures with cross inputs in the first layer and an input-weighted LSTM structure in the second layer to capture the temporal and cross-correlation features of smoke concentration, temperature, and plume velocity sensor data. The third LSTM layer further aggregates these features to extract the spatial correlation patterns among different heights. The experimental results demonstrate that the proposed algorithm can effectively expedite alarm response during sparse smoke conditions and mitigate false alarms caused by weak signals. Full article
(This article belongs to the Special Issue Advances in Mobile Networked Systems)
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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 7 | Viewed by 1841
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
Cited by 5 | Viewed by 1677
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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