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Applications and Perspectives of Real-Time Data Collection in Sensor Networks

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

Deadline for manuscript submissions: 20 October 2025 | Viewed by 783

Special Issue Editor


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Guest Editor
Center of Artificial Intelligence for Medical Instruments (CAIMI), Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea
Interests: IoT; big data; WSN; reinforcement learning; computer vision; medical imaging
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Special Issue Information

Dear Colleagues,

Real-time data collection in sensor networks is a crucial aspect of modern intelligent systems, enabling advanced decision-making, automation, and monitoring across domains such as healthcare, smart cities, industrial automation, and environmental surveillance. As sensor technologies and edge computing continue to evolve, there is a growing need for novel methodologies, frameworks, and architectures to enhance the efficiency, accuracy, and scalability of real-time data collection and processing.

This Special Issue aims to present state-of-the-art research on real-time data collection in sensor networks, highlighting emerging technologies, innovative applications, and future perspectives. It will cover key topics including, but not limited to, the following:

  • Advanced sensor network architectures for real-time data collection;
  • Edge and fog computing techniques for efficient data aggregation;
  • AI-driven data processing and decision-making in sensor networks;
  • Energy-efficient protocols and low-power communication in real-time applications;
  • Data compression and transmission strategies for high-speed sensing networks;
  • The integration of IoT and cloud computing for real-time sensor analytics;
  • Security and privacy challenges in real-time data collection;
  • Real-world case studies and industrial applications of real-time sensor networks;
  • Machine learning and deep learning approaches for real-time data analysis;
  • Emerging trends in multimodal sensor data fusion;
  • Applications in healthcare, environmental monitoring, and smart cities.

This Special Issue will serve as a valuable platform for researchers, industry professionals, and academics to present their latest findings and foster collaborations on cutting-edge developments in real-time sensor networks.

Dr. Shabir Ahmad
Guest Editor

Manuscript Submission Information

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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

  • real-time data collection
  • sensor networks
  • edge and fog computing
  • AI-driven sensing
  • IoT-based real-time monitoring
  • security and privacy in sensor networks
  • energy-efficient data transmission
  • machine learning for sensor data analysis
  • multimodal sensor fusion
  • smart cities and industrial applications

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Published Papers (1 paper)

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Research

15 pages, 6562 KB  
Article
Smart City Infrastructure Monitoring with a Hybrid Vision Transformer for Micro-Crack Detection
by Rashid Nasimov and Young Im Cho
Sensors 2025, 25(16), 5079; https://doi.org/10.3390/s25165079 - 15 Aug 2025
Viewed by 515
Abstract
Innovative and reliable structural health monitoring (SHM) is indispensable for ensuring the safety, dependability, and longevity of urban infrastructure. However, conventional methods lack full efficiency, remain labor-intensive, and are susceptible to errors, particularly in detecting subtle structural anomalies such as micro-cracks. To address [...] Read more.
Innovative and reliable structural health monitoring (SHM) is indispensable for ensuring the safety, dependability, and longevity of urban infrastructure. However, conventional methods lack full efficiency, remain labor-intensive, and are susceptible to errors, particularly in detecting subtle structural anomalies such as micro-cracks. To address this issue, this study proposes a novel deep-learning framework based on a modified Detection Transformer (DETR) architecture. The framework is enhanced by integrating a Vision Transformer (ViT) backbone and a specially designed Local Feature Extractor (LFE) module. The proposed ViT-based DETR model leverages ViT’s capability to capture global contextual information through its self-attention mechanism. The introduced LFE module significantly enhances the extraction and clarification of complex local spatial features in images. The LFE employs convolutional layers with residual connections and non-linear activations, facilitating efficient gradient propagation and reliable identification of micro-level defects. Thorough experimental validation conducted on the benchmark SDNET2018 dataset and a custom dataset of damaged bridge images demonstrates that the proposed Vision-Local Feature Detector (ViLFD) model outperforms existing approaches, including DETR variants and YOLO-based models (versions 5–9), thereby establishing a new state-of-the-art performance. The proposed model achieves superior accuracy (95.0%), precision (0.94), recall (0.93), F1-score (0.93), and mean Average Precision (mAP@0.5 = 0.89), confirming its capability to accurately and reliably detect subtle structural defects. The introduced architecture represents a significant advancement toward automated, precise, and reliable SHM solutions applicable in complex urban environments. Full article
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