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Mobile Sensing and Computing in Internet of Things
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Mobile Sensing and Computing in Internet of Things

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2412

Special Issue Editor

Prof. Dr. Tien-Wen Sung

E-Mail Website
Guest Editor
College of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
Interests: artificial internet of things; wireless sensor networks; mobile computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile Sensing and Computing in the Internet of Things is a crucial research area that enables real-time data collection, processing, and decision-making in dynamic environments. By leveraging mobile devices, sensors, and edge computing, this technology enhances the adaptability and efficiency of IoT applications, such as smart cities, healthcare, and intelligent transportation. Mobile sensing improves data accuracy and responsiveness, reducing reliance on centralized cloud computing while optimizing energy consumption and latency. With advancements in 5G, AI, and edge computing, mobile sensing plays a pivotal role in enabling context-aware and autonomous systems. Research in this field drives innovations in ubiquitous computing, human-centric sensing, and cyber-physical systems, making IoT solutions more scalable, secure, and intelligent for future applications.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of Mobile Sensing and Computing in the Internet of Things.

Potential topics include, but are not limited to, the following:

  •  AIDriven Mobile Sensing and Data Analytics.
  •  Ubiquitous Computing Technology.
  •  EnergyEfficient Mobile Sensing and Computing.
  •  Autonomous and Collaborative Mobile Sensing Networks.
  •  Integration of AI and Machine Learning in IoT.
  •  Crowdsensing and Participatory Sensing in Smart Cities.
  •  MultiModal Sensor Fusion in IoT.
  •  5G and Beyond for Mobile Sensing and Computing.
  •  Development of Smart Wearable Devices.
  •  HumanCentric Mobile Sensing for Smart Healthcare.
  •  Security and Privacy in Mobile Sensing IoT Systems.
  •  Edge Computing for RealTime Mobile Sensing.
  •  Integration of Blockchain for Secure Mobile Sensing and Computing.
  •  Standardization of IoT Protocols.
  •  Environmental Monitoring through Mobile Sensing and Computing.
  •  IoT Applications with Mobile Sensing and Computing.

Prof. Dr. Tien-Wen Sung
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 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

  • mobile sensing
  • ubiquitous computing
  • internet of things
  • artificial intelligence
  • smart city

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Published Papers (2 papers)

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Research

29 pages, 1089 KB  
Article
Time-Aware Graph Neural Network for Asynchronous Multi-Station Integrated Sensing and Communications Fusion in Open RAN
by Zhiqiang Shen, Wooseok Shin and Jitae Shin
Sensors 2026, 26(8), 2376; https://doi.org/10.3390/s26082376 - 12 Apr 2026
Viewed by 403
Abstract
Multi-station sensing telemetry typically arrives out-of-order at the Open RAN (O-RAN) Near-RT RIC due to non-deterministic jitter in cloud-native protocol stacks, inducing a “temporal scrambling” effect that invalidates traditional spatial fusion. To bridge this gap, we introduce Age-of-Sensing (AoS) as a dynamic reliability [...] Read more.
Multi-station sensing telemetry typically arrives out-of-order at the Open RAN (O-RAN) Near-RT RIC due to non-deterministic jitter in cloud-native protocol stacks, inducing a “temporal scrambling” effect that invalidates traditional spatial fusion. To bridge this gap, we introduce Age-of-Sensing (AoS) as a dynamic reliability metric for asynchronous sensing reports and establish an AoS-aware graph neural network (GNN) paradigm for asynchronous sensing fusion. This paradigm shifts the focus from conventional spatial-only aggregation to time-aware inference by explicitly incorporating sensing freshness into graph-based fusion. As a physics-informed realization of this paradigm, we present Time-Aware Fusion (TA-Fusion), which introduces a TA-Gate mechanism to recalibrate node trust prior to graph aggregation. Unlike passive feature concatenation, the TA-Gate serves as an active gating signal to prioritize fresh telemetry while adaptively suppressing stale outliers. On a standardized O-RAN benchmark, TA-Fusion achieves a root mean square error (RMSE) of 12.22 m, delivering a 21.7% reduction in Mean absolute error (MAE) over the AoS-aware GNN baseline and maintaining robustness in extreme jitter scenarios where traditional linear methods suffer from severe accuracy degradation due to their static weighting logic. Extensive Monte Carlo simulations confirm that the framework preserves consistent error bounds across diverse base station geometries without manual recalibration. These findings support the real-time feasibility of the proposed paradigm for delay-critical Integrated Sensing and Communication (ISAC) services, providing a resilient spatial foundation for 6G orchestration under substantial network-layer jitter. Full article
(This article belongs to the Special Issue Mobile Sensing and Computing in Internet of Things)
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22 pages, 4727 KB  
Article
Intelligent Robust Control Design with Closed-Loop Voltage Sensing for UPS Inverters in IoT Devices
by En-Chih Chang, Yuan-Wei Tseng and Chun-An Cheng
Sensors 2025, 25(13), 3849; https://doi.org/10.3390/s25133849 - 20 Jun 2025
Viewed by 1309
Abstract
High-performance UPS inverters prevent IoT devices from power outages, thus protecting critical data. This paper suggests an intelligent, robust control technique with closed-loop voltage sensing for UPS (uninterruptible power supply) inverters in IoT (internet of things) devices. The suggested control technique synthesizes a [...] Read more.
High-performance UPS inverters prevent IoT devices from power outages, thus protecting critical data. This paper suggests an intelligent, robust control technique with closed-loop voltage sensing for UPS (uninterruptible power supply) inverters in IoT (internet of things) devices. The suggested control technique synthesizes a modified gray fast variable structure sliding mode control (MGFVSSMC) together with a neural network (NN). The MGFVSSMC allows system states to speedily converge towards the equilibrium within a shorter time while eliminating the problems of chattering and steady-state errors. The MGFVSSMC may experience state prediction errors when the UPS inverter is subjected to external highly nonlinear loads or internal parameters changing drastically. This results in high harmonic distortion and inferior dynamic response of the inverter output, affecting the guarding of the IoT device. An NN by means of a learning mechanism is employed to properly compensate for the prediction error of the MGFVSSMC, achieving a high-performance UPS inverter. The suggested control technique operates with one voltage sensing, which can yield fast transience and low inverter output-voltage distortion. Both simulations and digital signal processing (DSP) implementation results demonstrate the effectiveness of the suggested control technique under a variety of load conditions. Full article
(This article belongs to the Special Issue Mobile Sensing and Computing in Internet of Things)
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