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Intelligent Multi-Sensor Fusion for IoT Applications

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 868

Special Issue Editor


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Guest Editor
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: sensor fusion; intelligent geospatial computation

Special Issue Information

Dear Colleagues,

Intelligent multi-sensor fusion is a pivotal technology in the realm of IoT applications. It involves the integration of data from various sensors to create a unified and coherent picture of the observed environment. This approach enhances the accuracy, reliability, and robustness of data interpretation, making it invaluable for critical applications such as smart cities, environmental monitoring, and healthcare. By employing sophisticated algorithms and machine learning techniques, intelligent multi-sensor fusion enables the seamless combination of diverse data sources, facilitating real-time decision-making and predictive analytics. As IoT continues to expand, the importance of efficient sensor data fusion becomes increasingly evident, promising to revolutionize how we interact with and understand our surroundings.

Dr. Sara Saeedi
Guest Editor

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Keywords

  • intelligent multi-sensor fusion
  • Internet of Things (IoT)
  • sensor integration
  • data interoperability
  • machine learning
  • high-performance computing
  • real-time data integration
  • data management
  • sensor data fusion ·innovation in IoT

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Published Papers (1 paper)

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Research

38 pages, 3142 KB  
Article
GICEDCam: A Geospatial Internet of Things Framework for Complex Event Detection in Camera Streams
by Sepehr Honarparvar, Yasaman Honarparvar, Zahra Ashena, Steve Liang and Sara Saeedi
Sensors 2025, 25(17), 5331; https://doi.org/10.3390/s25175331 - 27 Aug 2025
Viewed by 272
Abstract
Complex event detection (CED) adds value to camera stream data in various applications such as workplace safety, task monitoring, security, and health. Recent CED frameworks have addressed the issues of limited spatiotemporal labels and costly training by decomposing the CED into low-level features, [...] Read more.
Complex event detection (CED) adds value to camera stream data in various applications such as workplace safety, task monitoring, security, and health. Recent CED frameworks have addressed the issues of limited spatiotemporal labels and costly training by decomposing the CED into low-level features, as well as spatial and temporal relationship extraction. However, these frameworks suffer from high resource costs, low scalability, and an increased number of false positives and false negatives. This paper proposes GICEDCAM, which distributes CED across edge, stateless, and stateful layers to improve scalability and reduce computation cost. Additionally, we introduce a Spatial Event Corrector component that leverages geospatial data analysis to minimize false negatives and false positives in spatial event detection. We evaluate GICEDCAM on 16 camera streams covering four complex events. Relative to a strong open-source baseline configured for our setting, GICEDCAM reduces end-to-end latency by 36% and total computational cost by 45%, with the advantage widening as objects per frame increase. Among corrector variants, Bayesian Network (BN) yields the lowest latency, Long Short-Term Memory (LSTM) achieves the highest accuracy, and trajectory analysis offers the best accuracy–latency trade-off for this architecture. Full article
(This article belongs to the Special Issue Intelligent Multi-Sensor Fusion for IoT Applications)
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