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Sensor Networks and Communication with AI

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

Deadline for manuscript submissions: 30 December 2025 | Viewed by 930

Special Issue Editors


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Guest Editor
Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: AIoT; multimodal machine learning; intelligent decision
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: machine learning; deep learning; sensors; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the growing development of artificial intelligence, its application in solving practical problems in sensor networks and communication has become a popular topic. Using AI technologies may offer new opportunities to improve the accuracy, efficiency, and usability of sensing, communication, networking, etc., with sensors.

This Special Issue therefore aims to collect original research and review articles on the recent advances, technologies, solutions, and applications, as well as new challenges, in the field of sensor networks and communication with AI.

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

  • Edge and cloud computing;
  • Embedded and energy-harvesting systems;
  • Backscatter communication and wireless power;
  • Implantable and wearable computing;
  • Millimeter-wave and terahertz communications;
  • Mobile health;
  • Applications of machine learning to mobile/wireless research;
  • Transfer learning and domain adaptation for mobile applications;
  • Large language models (LLMs) and generative AI for mobile and wireless systems;
  • The next generation of mobile networks (5G, 6G and beyond);
  • Mobile web, video, virtual reality, augmented reality and other applications;
  • Novel applications of wireless signals;
  • Vehicular, robotic and drone-based networking;
  • Communication with reconfigurable and intelligent reflective surfaces, meta materials, and flexible surfaces;
  • Sensing with radio, light, sound, and acoustics;
  • Smart cities and smart spaces (e.g., smart factories, smart workspaces, smart agriculture, urban mobility);
  • Ubiquitous computing and mobile human–computer interaction;
  • Underwater networking and sensing;
  • Visible light communications;
  • Wireless localization and tracking;
  • Reducing the carbon footprint of wireless networks and mobile devices.

Dr. Ge Wang
Dr. Fei Wang
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

  • edge and cloud computing
  • energy-harvesting systems
  • wearable computing
  • next generation of mobile networks
  • smart cities

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Published Papers (1 paper)

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Research

22 pages, 3131 KiB  
Article
CAREC: Continual Wireless Action Recognition with Expansion–Compression Coordination
by Tingting Zhang, Qunhang Fu, Han Ding, Ge Wang and Fei Wang
Sensors 2025, 25(15), 4706; https://doi.org/10.3390/s25154706 - 30 Jul 2025
Viewed by 496
Abstract
In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over [...] Read more.
In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over time. However, Wi-Fi-based indoor action recognition under incremental learning faces two major challenges: catastrophic forgetting of previously learned knowledge and uncontrolled model expansion as new classes are added. To address these issues, we propose CAREC, a class-incremental framework that balances dynamic model expansion with efficient compression. CAREC adopts a multi-branch architecture to incorporate new classes without compromising previously learned features and leverages balanced knowledge distillation to compress the model by 80% while preserving performance. A data replay strategy retains representative samples of old classes, and a super-feature extractor enhances inter-class discrimination. Evaluated on the large-scale XRF55 dataset, CAREC reduces performance degradation by 51.82% over four incremental stages and achieves 67.84% accuracy with only 21.08 M parameters, 20% parameters compared to conventional approaches. Full article
(This article belongs to the Special Issue Sensor Networks and Communication with AI)
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