Wireless Signal-Based Ubiquitous Sensing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 551

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
Interests: deep learning-based Human Activity Recognition (HAR); gesture detection; Artificial Intelligence of Things (AIoT); Indoor Positioning System (IPS); next-generation network systems
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Guest Editor
School of Computer Science and Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea
Interests: wireless networks; mobile networks; AI-applied wireless networks; semantic communications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Engineering, Kumoh National Institute of Technology (KIT), Gumi 39177, Republic of Korea
Interests: wireless LAN MAC protocol; Artificial Intelligence of Things (AIoT); smart farm; big data

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Guest Editor
School of Electronic Engineering, Kumoh National University of Technology, Gumi 39177, Republic of Korea
Interests: PHY & MAC for next-generation mobile networks; network simulator; next-generation communication; semantic communications; wireless networking with deep neural networks

Special Issue Information

Dear Colleagues,

Recent advances in wireless communication technologies have enabled a paradigm shift in sensing systems, where ambient wireless signals are exploited to infer environmental context, human activities, and physiological states without dedicated sensors or user cooperation. Wireless signal-based ubiquitous sensing leverages existing wireless infrastructures such as Wi-Fi, UWB, Bluetooth, and cellular networks to provide pervasive and contactless sensing capabilities, creating new opportunities across healthcare, smart environments and human–computer interactions.

Despite rapid progress, significant challenges remain in achieving robust, scalable, and generalizable sensing performance under realistic wireless conditions. Variability in propagation environments, interference, and mobility poses fundamental limitations on sensing accuracy and reliability. Furthermore, the tight coupling between communication and sensing functionalities raises critical research questions spanning signal processing, machine learning, network protocol design, and system-level optimization.

Thus, this Special Issue invites original research, comprehensive review contributions that advance theoretical foundations, algorithmic frameworks, and practical implementations of wireless signal-based sensing.

Topics of interest include, but are not limited to, the following:

  • RF-based sensing and localization;
  • Wireless sensing with commodity devices;
  • Applications using wireless sensing technologies;
  • Learning-driven signal interpretation;
  • Integrated sensing and communication (ISAC) in B5G/6G wireless systems;
  • Wireless sensing technologies based on IEEE 802.11bf;
  • Energy-efficient and privacy-preserving sensing architecture;
  • Real-world deployments and experimental evaluations.

Prof. Dr. Young-Joo Suh
Dr. Young Deok Park
Dr. Hyeongtae Ahn
Dr. Harim Lee
Guest Editors

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Keywords

  • wireless signal-based sensing
  • contactless sensing
  • AI-driven wireless sensing

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Published Papers (1 paper)

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Research

15 pages, 1512 KB  
Article
Indoor–Outdoor User Detection in Cellular Networks: A Kalman Filtering and Likelihood Ratio Testing Approach
by Hamidreza Khaleghi and Thierry Lucidarme
Electronics 2026, 15(6), 1177; https://doi.org/10.3390/electronics15061177 - 12 Mar 2026
Viewed by 363
Abstract
Reliable indoor–outdoor user detection is essential for optimizing resource allocation and network performance in cellular networks. While Artificial Intelligence (AI)-based techniques have gained attention, statistical signal processing remains a robust and interpretable approach. This paper presents a lightweight and practical detection framework that [...] Read more.
Reliable indoor–outdoor user detection is essential for optimizing resource allocation and network performance in cellular networks. While Artificial Intelligence (AI)-based techniques have gained attention, statistical signal processing remains a robust and interpretable approach. This paper presents a lightweight and practical detection framework that leverages signal quality metrics, specifically the signal-to-interference-plus-noise ratio (SINR). To enhance detection accuracy, a Kalman filter is employed to mitigate signal fluctuations while preserving consistency. Furthermore, a likelihood-based hypothesis test is introduced to improve indoor user classification. Simulation results show that the proposed detector achieves a recall of up to 99% and F1-scores of around 98% while maintaining prescribed false-alarm targets. Compared with simple SINR thresholding, the Kalman-regularized generalized likelihood ratio test (GLRT) provides substantially improved robustness under stringent false-alarm constraints while retaining constant-time inference complexity. Full article
(This article belongs to the Special Issue Wireless Signal-Based Ubiquitous Sensing)
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