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

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

Deadline for manuscript submissions: closed (10 March 2026) | Viewed by 11680

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Special Issue Editors

Dr. Zhao Li

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Guest Editor

School of Cyber Engineering, Xidian University, Xi’an 710065, China
Interests: beyond 5G & 6G technologies and communication networks; interference management and utilization; physical layer security; intelligent reflecting surfaces; multiple-input multiple-output and array signal processing; AI empowered wireless communications; network simulation; space–air–ground integrated networks; covert communications; cognitive radio networks; IoT/D2D communication networks
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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (6 papers)

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Research

22 pages, 10245 KB

Open AccessArticle

TransBridge: A Transparent Communication Middleware with Unified RoCE and TCP Semantics

by Cong Zhou, Yulei Yuan and Peng Xun

Sensors 2026, 26(8), 2482; https://doi.org/10.3390/s26082482 - 17 Apr 2026

Viewed by 313

Abstract

In low-latency edge-intelligence scenarios such as autonomous driving and industrial edge analytics, the processing of large-scale sensor data imposes extremely stringent requirements on communication latency. However, the high overhead of the traditional TCP protocol makes it difficult to satisfy such demands, while the semantic gap between the high-performance RoCE protocol and the standard Socket API prevents existing applications from directly exploiting its advantages. To address this problem, this paper proposes TransBridge, a lightweight user-space communication middleware that transparently bridges TCP and RoCE. Its design is realized through three key innovations: a transparent user-space compatibility architecture that enables unmodified Socket-based applications to benefit from RoCE performance; a microsecond-level low-latency transmission engine that bypasses kernel and protocol stack overhead; and a lightweight lock-free resource management mechanism based on a decentralized peer-to-peer architecture and deferred buffer updates. Experiments on a real RoCE network show that TransBridge significantly outperforms mainstream schemes: it achieves an average round-trip latency of 5.926

μ

s for 16 B messages and a throughput of 20.254 Gbps for 16 KB messages; in the Fast DDS application-level evaluation, it achieves a throughput of 188 Mbps and an average round-trip latency of about 150

μ

s. The results indicate that TransBridge can provide transparent and effective RoCE acceleration for existing Socket-based applications in resource-constrained edge environments. 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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25 pages, 2368 KB

Open AccessArticle

Multi-Probing Opportunistic Routing in Buffer-Constrained Wireless Sensor Networks

by Nannan Sun, Shouxin Cao, Xiaoyuan Liu, Yue Gao, Yang Xu and Jia Liu

Sensors 2026, 26(8), 2295; https://doi.org/10.3390/s26082295 - 8 Apr 2026

Viewed by 344

Abstract

Wireless sensor networks (WSNs) are fundamental building blocks of modern ubiquitous sensing systems. In many practical WSN deployments, sensing devices are tightly constrained in buffer capacity, while device mobility leads to topology decentralization. These characteristics pose significant challenges for reliable and timely data delivery across WSNs. In this paper, we propose a general multi-probing opportunistic routing strategy tailored for buffer-constrained WSNs, aiming to enhance transmission opportunity utilization under realistic sensing device limitations. With the help of Queueing Theory and Markov Chain Theory, we capture the sophisticated queueing processes for the buffer space of sensors, which enables the limiting distribution of the buffer occupation state to be determined. On this basis, we develop a theoretical performance modeling framework to evaluate the fundamental performance metrics of the WSN with the multi-probing opportunistic routing, including the per-flow throughput and the expected end-to-end delay. The validity of the performance modeling framework is verified by network simulations. Moreover, extensive numerical results demonstrate the network performance behaviors comprehensively and reveal some insightful findings that can serve as important guidelines for the configuration and operation of WSNs. 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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28 pages, 5155 KB

Open AccessArticle

Efficient Human Posture Recognition and Assessment in Visual Sensor Systems: An Experimental Study

by Lei Lei, Haonan Zhang, Qi Zhang, Weihua Wu, Weijia Han and Runzi Liu

Sensors 2025, 25(21), 6789; https://doi.org/10.3390/s25216789 - 6 Nov 2025

Cited by 1 | Viewed by 1501

Abstract

Currently, recognition and assessment of human posture have become significant topics of interest, particularly through the use of visual sensor systems. These approaches can effectively address the drawbacks associated with traditional manual assessments, which include fatigue, variations in experience, and inconsistent judgment criteria. However, systems based on visual sensors encounter substantial implementation challenges when a large number of such sensors are used. To address these issues, we propose a human posture recognition and assessment system architecture, which comprises four distinct subsystems. Specifically, these subsystems include a Visual Sensor Subsystem (VSS), a Posture Assessment Subsystem (PAS), a Control-Display Subsystem, and a Storage Management Subsystem. Through the cooperation of subsystems, the architecture has achieved support for parallel data processing. Furthermore, the proposed architecture has been implemented by building an experimental testbed, which effectively verifies the rationality and feasibility of this architecture. In the experiments, the proposed architecture was evaluated by using pull-up and push-up exercises. The results demonstrate that the proposed architecture achieves an overall accuracy exceeding 96%, while exhibiting excellent real-time performance and scalability in different assessment scenarios. 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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21 pages, 2893 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1984

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 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

Open AccessArticle

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 908

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) 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

Open AccessArticle

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

Cited by 5 | Viewed by 4512

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 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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