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Perspectives in Intelligent Sensors and Sensing Systems
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Perspectives in Intelligent Sensors and Sensing Systems

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

Deadline for manuscript submissions: 25 October 2025 | Viewed by 9632

Special Issue Editor

Prof. Dr. Sergio A. Velastin

E-Mail Website
Guest Editor
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Interests: computer vision; object location; human action recognition; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent sensor technologies have improved the everyday lives of human beings through their applications in almost all fields. A wide range of applications is utilized using innovative sensor technologies in lifestyle, healthcare, fitness, manufacturing, and daily life.

What are the prospects for intelligent sensors and systems? Advanced technologies of intelligent sensing, AI promotion policies, and new methods of scientific research and the application of these methods to technologies are being discussed and quickly becoming the standard to which researchers aspire.

This Special Issue, titled “Perspectives in Intelligent Sensors and Sensing Systems”, will collect high-quality perspective papers in fields of interest related to intelligent sensors and systems.

We welcome all relevant researchers to contribute perspectives highlighting the latest developments in their research field or invite relevant experts and colleagues to do so.

Prof. Dr. Sergio Velastin
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. 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

  • intelligent sensors 
  • smart sensing 
  • sensing systems 
  • sensor technology 
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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17 pages, 1603 KiB  
Perspective
A Perspective on Quality Evaluation for AI-Generated Videos
by Zhichao Zhang, Wei Sun and Guangtao Zhai
Sensors 2025, 25(15), 4668; https://doi.org/10.3390/s25154668 - 28 Jul 2025
Viewed by 499
Abstract
Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames [...] Read more.
Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment. Full article
(This article belongs to the Special Issue Perspectives in Intelligent Sensors and Sensing Systems)
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30 pages, 1613 KiB  
Perspective
Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues
by Changqing Zhao, Ling Xia Liao, Guomin Chen and Han-Chieh Chao
Sensors 2025, 25(8), 2368; https://doi.org/10.3390/s25082368 - 8 Apr 2025
Viewed by 766
Abstract
The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited [...] Read more.
The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data. This paper explores data and knowledge condensation methods—such as coreset selection, data compression, knowledge distillation, and dataset distillation—within the context of traffic classification tasks. It clarifies the relationship between these techniques and network traffic classification, introducing each method and its typical applications. This paper also outlines potential scenarios for applying each condensation technique, highlighting the associated challenges and open research issues. To the best of our knowledge, this is the first comprehensive summary of condensation techniques specifically tailored for network traffic classification tasks. Full article
(This article belongs to the Special Issue Perspectives in Intelligent Sensors and Sensing Systems)
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25 pages, 3512 KiB  
Perspective
From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI
by Ilenia Ficili, Maurizio Giacobbe, Giuseppe Tricomi and Antonio Puliafito
Sensors 2025, 25(6), 1763; https://doi.org/10.3390/s25061763 - 12 Mar 2025
Cited by 12 | Viewed by 7647
Abstract
The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The [...] Read more.
The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. The findings contribute to advancing pervasive environmental intelligence, offering a roadmap for building smarter, more sustainable infrastructure. Full article
(This article belongs to the Special Issue Perspectives in Intelligent Sensors and Sensing Systems)
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