IoT and Networking Technologies for Smart Mobile Systems

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Communications and Networking".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 1439

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Shenzhen University, Shenzhen 518060, China
Interests: internet of things; mobile computing; network security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
The School of Information Engineering, China University of Geosciences Beijing
Interests: Intelligent Internet of Things (IIoT); Mobile Computing; Distributed Computing; Ubiquitous Computing

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Guest Editor Assistant
National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
Interests: human gait recognition and analysis based on vision; human body image generation and generative models; perception issues in embodied intelligence systems

Special Issue Information

Dear Colleagues,

The rapid evolution of Internet of Things (IoT) and networking technologies has revolutionized smart mobile systems, enabling seamless connectivity, real-time data processing, and intelligent decision-making across diverse applications such as smart cities, healthcare, transportation, and industrial automation. This Special Issue aims to explore cutting-edge technologies, challenges, and future trends in IoT-driven smart mobile systems, with a focus on low-power sensing and communication, edge intelligence, collaborative learning, and 5G/6G networking.

Key topics include the following:

  • Ultra-low-power IoT communication (e.g., backscatter, RF harvesting) for energy-efficient edge devices.
  • Mobile edge computing (MEC) for latency-sensitive AI-empowered IoT applications, including task offloading and distributed learning.
  • 5G/6G-enabled IoT architectures, leveraging massive MIMO, D2D communication, and network slicing.
  • AI-driven IoT optimization, covering smart sensing, QoS management, security, and resource allocation in dynamic environments.
  • Heterogeneous IoT integration, addressing interoperability, scalability, and real-time sensing challenges.

Prof. Dr. Chengwen Luo
Guest Editor

Dr. Xingyu Feng
Dr. Chao Fan
Guest Editor Assistants

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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

  • Internet of Things (IoT)
  • smart mobile systems
  • 5G/6G networks
  • mobile edge computing (MEC)
  • low-power sensing and communication
  • artificial intelligence (AI) in IoT
  • task offloading
  • QoS Optimization
  • backscatter-based sensing and networking
  • Industrial IoT (IIoT)

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Published Papers (1 paper)

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Research

18 pages, 4674 KB  
Article
AI Correction of Smartphone Thermal Images: Application to Diabetic Plantar Foot
by Hafid Elfahimi, Rachid Harba, Asma Aferhane, Hassan Douzi and Ikram Damoune
J. Sens. Actuator Netw. 2026, 15(1), 13; https://doi.org/10.3390/jsan15010013 - 26 Jan 2026
Cited by 1 | Viewed by 939
Abstract
Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated [...] Read more.
Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated two-region segmentation task (forehead + plantar foot) to enable stable temperature correction. First, we developed a fully automated joint method for this task, building upon a new multimodal thermal–RGB dataset constructed with detailed annotation procedures. Five deep learning methods (U-Net, U-Net++, SegNet, DE-ResUnet, and DE-ResUnet++) were evaluated and compared to traditional baselines (Adaptive Thresholding and Region Growing), demonstrating the clear advantage of data-driven approaches. The best performance was achieved by the DE-ResUnet++ architecture (Dice score: 98.46%). Second, we validated the correction approach through a clinical study. Results showed that the variance of corrected temperatures was reduced by half compared to absolute values (p < 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p < 0.01), unlike absolute temperatures. These findings suggest that our approach could enhance the performance of smartphone-connected thermal devices and contribute to the early prevention of DF complications. Full article
(This article belongs to the Special Issue IoT and Networking Technologies for Smart Mobile Systems)
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