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Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 10 March 2026 | Viewed by 5045

Special Issue Editors

Dr. Zhao Li

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi’an 710071, China
Interests: cognitive radio networks; wireless interference management; signal processing for wireless communications; adaptive beamforming; MIMO
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Xu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: wireless communications; air–space–ground integrated networks; physical layer security; network economics; cognitive radio networks
Special Issues, Collections and Topics in MDPI journals
Dr. Kechen Zheng

E-Mail Website
Guest Editor
School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: cognitive radio networks; energy harvesting networks; wireless-powered communication networks; spectrum sensing; supervised learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoying Liu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: green communication; energy harvesting; physical payer security; cooperative communication; cognitive radio networks
Special Issues, Collections and Topics in MDPI journals
Dr. Jia Liu

E-Mail Website
Guest Editor
Center for Strategic Cyber Resilience Research and Development, National Institute of Informatics, Tokyo 101-8430, Japan
Interests: wireless systems security; covert communications; Internet of Things; cognitive radio networks; 5G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Embedded and ubiquitous computing represents a paradigm designed to deliver continuous computing and communication services to end users universally and perpetually. These systems are currently permeating every aspect of our daily lives, poised to revolutionize our existence in ways that are more profound than ever before. The inception of this technology stems from the intersection of research and technological advancements across a spectrum of disciplines, encompassing embedded systems, pervasive computing, communication technologies, wireless networks, mobile computing, distributed computing, and agent technologies.

Sensors play a critical role in the functionality and effectiveness of embedded and ubiquitous computing systems. They serve as the primary means of gathering data from the environment, thus enabling intelligent decision-making and real-time responses. Despite the advancements in embedded and pervasive computing, there remain numerous topics worth further investigation, particularly surrounding the integration, application, and optimization of sensors in these systems. This includes novel sensor technologies, their deployment in smart environments, and the challenges associated with data acquisition and management.

The aim of this Special Issue is to bring together and disseminate state-of-the-art research advances in the analysis, design, optimization, implementation, and standardization of embedded and ubiquitous computing, with a specific focus on sensors and their applications. We welcome original research and review articles. The potential topics include, but are not limited to, the following:

  • Integration of emerging wireless technologies with sensor-enabled embedded and ubiquitous computing;
  • Smart mobile systems and applications utilizing sensors in embedded and ubiquitous computing;
  • Design, analysis, and optimization of sensor-enabled embedded and ubiquitous computing networks;
  • Sensing data analysis and management for embedded and ubiquitous computing;
  • Security, safety, and reliability/dependability in sensor-enabled embedded and ubiquitous computing networks;
  • Machine learning techniques for embedded and ubiquitous computing;
  • Spectrum sensing and sharing in embedded and ubiquitous computing systems with other systems;
  • Spectrum and interference management in sensor-enabled embedded and ubiquitous computing;
  • Enhancements in living environments and intelligentization of habitats with sensor-enabled embedded and ubiquitous computing;
  • Embedded and ubiquitous computing standards, testbeds, simulation tools, and hardware prototypes;
  • Mobile edge computing and fog computing in embedded and ubiquitous computing;
  • Speed, connectivity, and smart communication for sensor-enabled embedded and ubiquitous computing systems;
  • Challenges and issues in designing sensor-enabled embedded and ubiquitous computing systems.

Dr. Zhao Li
Dr. Yang Xu
Dr. Kechen Zheng
Dr. Xiaoying Liu
Dr. Jia Liu
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. Sensors 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 2600 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

  • sensors
  • embedded computing
  • ubiquitous computing

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

Published Papers (3 papers)

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Research

21 pages, 2893 KB  
Article
Intelligent Fault Diagnosis System for Running Gear of High-Speed Trains
by Shuai Yang, Guoliang Gao, Ziyang Wang, Shengfeng Zeng, Yikai Ouyang and Guanglei Zhang
Sensors 2025, 25(17), 5269; https://doi.org/10.3390/s25175269 - 24 Aug 2025
Viewed by 997
Abstract
Conventional rail transit train running gear fault diagnosis mainly depends on routine maintenance inspections and manual judgment. However, these approaches lack robustness under complex operational environments and elevated noise levels, rendering them inadequate for real-time performance and the rigorous accuracy standards demanded by [...] Read more.
Conventional rail transit train running gear fault diagnosis mainly depends on routine maintenance inspections and manual judgment. However, these approaches lack robustness under complex operational environments and elevated noise levels, rendering them inadequate for real-time performance and the rigorous accuracy standards demanded by modern rail transit systems. Furthermore, many existing deep learning–based methods suffer from inherent limitations in feature extraction or incur prohibitive computational costs when processing multivariate time series data. This study represents one of the early efforts to introduce the TimesNet time series modeling framework into the domain of fault diagnosis for rail transit train running gear. By utilizing an innovative multi-period decomposition strategy and a mechanism for reshaping one-dimensional data into two-dimensional tensors, the framework enables advanced temporal-spatial representation of time series data. Algorithm validation is performed on both the high-speed train running gear bearing fault dataset and the multi-mode fault diagnosis datasets of gearbox under variable working conditions. The TimesNet model exhibits outstanding diagnostic performance on both datasets, achieving a diagnostic accuracy of 91.7% on the high-speed train bearing fault dataset. Embedded deployment experiments demonstrate that single-sample inference is completed within 70.3 ± 5.8 ms, thereby satisfying the real-time monitoring requirement (<100 ms) with a 100% success rate over 50 consecutive tests. The two-dimensional reshaping approach inherent to TimesNet markedly enhances the capacity of the model to capture intrinsic periodic structures within multivariate time series data, presenting a novel paradigm for the intelligent fault diagnosis of complex mechanical systems in train running gears. The integrated human–machine interaction system includes a comprehensive closed-loop process encompassing detection, diagnosis, and decision-making, thereby laying a robust foundation for the continued development of train running gear predictive maintenance technologies. Full article
(This article belongs to the Special Issue Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing)
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19 pages, 1370 KB  
Article
Airborne-Platform-Assisted Transmission and Control Separation for Multiple Access in Integrated Satellite–Terrestrial Networks
by Chaoran Huang, Xiao Ma, Xiangren Xin, Weijia Han and Yanjie Dong
Sensors 2025, 25(15), 4732; https://doi.org/10.3390/s25154732 - 31 Jul 2025
Viewed by 472
Abstract
Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) [...] Read more.
Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article
(This article belongs to the Special Issue Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing)
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24 pages, 2941 KB  
Article
Real-Time Acoustic Detection of Critical Incidents in Smart Cities Using Artificial Intelligence and Edge Networks
by Ioannis Saradopoulos, Ilyas Potamitis, Stavros Ntalampiras, Iraklis Rigakis, Charalampos Manifavas and Antonios Konstantaras
Sensors 2025, 25(8), 2597; https://doi.org/10.3390/s25082597 - 20 Apr 2025
Viewed by 1801
Abstract
We present a system that integrates diverse technologies to achieve real-time, distributed audio surveillance. The system employs a network of microphones mounted on ESP32 platforms, which transmit compressed audio chunks via an MQTT protocol to Raspberry Pi5 devices for acoustic classification. These devices [...] Read more.
We present a system that integrates diverse technologies to achieve real-time, distributed audio surveillance. The system employs a network of microphones mounted on ESP32 platforms, which transmit compressed audio chunks via an MQTT protocol to Raspberry Pi5 devices for acoustic classification. These devices host an audio transformer model trained on the AudioSet dataset, enabling the real-time classification and timestamping of audio events with high accuracy. The output of the transformer is kept in a database of events and is subsequently converted into JSON format. The latter is further parsed into a graph structure that encapsulates the annotated soundscape, providing a rich and dynamic representation of audio environments. These graphs are subsequently traversed and analyzed using dedicated Python code and large language models (LLMs), enabling the system to answer complex queries about the nature, relationships, and context of detected audio events. We introduce a novel graph parsing method that achieves low false-alarm rates. In the task of analyzing the audio from a 1 h and 40 min long movie featuring hazardous driving practices, our approach achieved an accuracy of 0.882, precision of 0.8, recall of 1.0, and an F1 score of 0.89. By combining the robustness of distributed sensing and the precision of transformer-based audio classification, our approach that treats audio as text paves the way for advanced applications in acoustic surveillance, environmental monitoring, and beyond. Full article
(This article belongs to the Special Issue Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing)
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