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Sensors
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Emerging Trends in Cybersecurity for Wireless Communication and IoT
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Emerging Trends in Cybersecurity for Wireless Communication and IoT

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

Deadline for manuscript submissions: 30 January 2026 | Viewed by 590

Special Issue Editors

Dr. Kiho Lim

E-Mail Website
Guest Editor
Department of Computer Science, William Paterson University, Wayne, NJ 07470, USA
Interests: cybersecurity; network security; machine learning; vehicular networks; wireless communication; internet of things
Special Issues, Collections and Topics in MDPI journals
Dr. Weihua Liu

E-Mail Website
Guest Editor
Department of Computer Science, State University of New York at Oswego, Oswego, NY, USA
Interests: cryptography and security; cybersecurity and intelligent vehicle systems; performance prediction; application of machine learning and data mining

Special Issue Information

Dear Colleagues,

Wireless communication and the Internet of Things (IoT) are revolutionizing industries such as healthcare, transportation, manufacturing, and smart homes by enabling unprecedented levels of connectivity and automation. As these technologies converge, they offer immense benefits but also introduce complex cybersecurity challenges. The ever-evolving landscape of cyber threats necessitates the use of innovative and proactive strategies, including the use of Artificial Intelligence (AI), to protect wireless networks and IoT devices from sophisticated attacks.

This Special Issue focuses on identifying and analyzing emerging trends in cybersecurity that are critical for protecting wireless communication systems and IoT devices. We seek to gather forward-thinking research that explores novel security architectures, cutting-edge defense mechanisms, and proactive threat intelligence. By addressing the pressing cybersecurity issues of today and anticipating those of tomorrow, this collection aims to contribute to the development of resilient wireless and IoT ecosystems.

Potential topics of interest to this Special Issue include, but are not limited to, the following:

  • Advanced cryptography and key management;
  • AI and machine learning in IoT and wireless security;
  • Blockchain-based cybersecurity applications;
  • Secure architectures and protocols;
  • Security in critical infrastructure and emerging technologies;
  • Mechanisms of access control, authentication, and authorization;
  • Privacy and data protection;
  • Edge and fog computing security;
  • Intrusion detection and malware protection;
  • Emerging security issues, trends, and future directions.

Dr. Kiho Lim
Dr. Weihua 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

  • wireless communication
  • Internet of Things (IoT)
  • cybersecurity

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Published Papers (1 paper)

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Research

25 pages, 34645 KiB  
Article
DFN-YOLO: Detecting Narrowband Signals in Broadband Spectrum
by Kun Jiang, Kexiao Peng, Yuan Feng, Xia Guo and Zuping Tang
Sensors 2025, 25(13), 4206; https://doi.org/10.3390/s25134206 - 5 Jul 2025
Viewed by 257
Abstract
With the rapid development of wireless communication technologies and the increasing demand for efficient spectrum utilization, broadband spectrum sensing has become critical in both civilian and military fields. Detecting narrowband signals under broadband environments, especially under low-signal-to-noise-ratio (SNR) conditions, poses significant challenges due [...] Read more.
With the rapid development of wireless communication technologies and the increasing demand for efficient spectrum utilization, broadband spectrum sensing has become critical in both civilian and military fields. Detecting narrowband signals under broadband environments, especially under low-signal-to-noise-ratio (SNR) conditions, poses significant challenges due to the complexity of time–frequency features and noise interference. To this end, this study presents a signal detection model named deformable feature-enhanced network–You Only Look Once (DFN-YOLO), specifically designed for blind signal detection in broadband scenarios. The DFN-YOLO model incorporates a deformable channel feature fusion network (DCFFN), replacing the concatenate-to-fusion (C2f) module to enhance the extraction and integration of channel features. The deformable attention mechanism embedded in DCFFN adaptively focuses on critical signal regions, while the loss function is optimized to the focal scaled intersection over union (Focal_SIoU), improving detection accuracy under low-SNR conditions. To support this task, a signal detection dataset is constructed and utilized to evaluate the performance of DFN-YOLO. The experimental results for broadband time–frequency spectrograms demonstrate that DFN-YOLO achieves a mean average precision (mAP50–95) of 0.850, averaged over IoU thresholds ranging from 0.50 to 0.95 with a step of 0.05, significantly outperforming mainstream object detection models such as YOLOv8, which serves as the benchmark baseline in this study. Additionally, the model maintains an average time estimation error within 5.55×10−5 s and provides preliminary center frequency estimation in the broadband spectrum. These findings underscore the strong potential of DFN-YOLO for blind signal detection in broadband environments, with significant implications for both civilian and military applications. Full article
(This article belongs to the Special Issue Emerging Trends in Cybersecurity for Wireless Communication and IoT)
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