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Advanced Wireless Networks and IoT Technologies for Emerging Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1211

Special Issue Editor


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Guest Editor
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
Interests: wireless networks; distributed computing; Internet-of-Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of wireless networks and the Internet of Things (IoT) has enabled new applications that leverage the connectivity, mobility, and intelligence of these technologies. Some of these applications include smart cities, smart health, smart agriculture, smart transportation, and smart industry. The driving force behind these transformative applications lies in the symbiotic relationship between cutting-edge wireless networks and the IoT. As we stand on the brink of the 6G era, the horizon is adorned with the promises of high data rates, ultralow latency, unwavering reliability, and unparalleled connectivity. Imagine the orchestration of massive sensor arrays that seamlessly interact with AI-driven, big models, leading to a harmonious duet that powers the heart of smart cities and beyond.

Within this paradigm, researchers and visionaries are seizing the moment to unravel the complex tapestry of advanced wireless networks and IoT technologies intertwined. The complexity is multifaceted: designing wireless architectures that can adapt to the diverse needs of the IoT, accurately coordinating the allocation of spectrum resources, and enhancing security and privacy. Yet, amidst these challenges, opportunities are also blossoming. This has attracted the attention of academia and industry, sparking a fervor for exploration into uncharted territory.

In response to this transformative juncture, we cordially invite researchers to contribute their insights and findings to our Special Issue. For this collection, we seek to solicit original and high-quality research papers on advanced wireless networks and IoT technologies for emerging applications. Topics of interest include, but are not limited to, the following:

  • Wireless network architectures and protocols for IoT applications, e.g., 6G networks, etc.
  • Wireless network resource allocation and optimization for IoT applications.
  • Wireless network edge and cloud computing for IoT applications, such as AI-driven, etc.
  • Wireless network testbeds and experimental studies for IoT applications.
  • Machine learning for wireless networks and IoT applications.
  • Wireless network performance evaluation and modeling for IoT applications.
  • Wireless network security and privacy for IoT applications.
  • Other emergent wireless networks and IoT applications, for example, backscatter applications, etc.

Prof. Dr. Wei Gong
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 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. Applied Sciences 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 2400 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.

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Published Papers (1 paper)

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28 pages, 9557 KiB  
Article
Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
by Yujing Bai, Jun Wang, Penghui Chen, Ziwei Gong and Qingxu Xiong
Appl. Sci. 2024, 14(15), 6782; https://doi.org/10.3390/app14156782 - 3 Aug 2024
Cited by 1 | Viewed by 790
Abstract
Gesture plays an important role in human–machine interaction. However, the insufficient accuracy and high complexity of gesture recognition have blocked its widespread application. A gesture recognition method that combines state machine and bidirectional long short-term memory (Bi-LSTM) fusion neural network is proposed to [...] Read more.
Gesture plays an important role in human–machine interaction. However, the insufficient accuracy and high complexity of gesture recognition have blocked its widespread application. A gesture recognition method that combines state machine and bidirectional long short-term memory (Bi-LSTM) fusion neural network is proposed to improve the accuracy and efficiency. Firstly, gestures with large movements are categorized into simple trajectory gestures and complex trajectory gestures in advance. Afterwards, different recognition methods are applied for the two categories of gestures, and the final result of gesture recognition is obtained by combining the outputs of the two methods. The specific method used is a state machine that recognizes six simple trajectory gestures and a bidirectional LSTM fusion neural network that recognizes four complex trajectory gestures. Finally, the experimental results show that the proposed simple trajectory gesture recognition method has an average accuracy of 99.58%, and the bidirectional LSTM fusion neural network has an average accuracy of 99.47%, which can efficiently and accurately recognize 10 gestures with large movements. In addition, by collecting more gesture data from untrained participants, it was verified that the proposed neural network has good generalization performance and can adapt to the various operating habits of different users. Full article
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