Multi-Sensor Data Fusion Technology and Its Application in Meteorology and Air Quality Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 26
Special Issue Editor
Interests: intelligent security and trust provision for Internet of Things (IoT) networks; IoT data analytics and applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Multi-sensor data fusion technology integrates information from various sensors to enhance the accuracy and reliability of data collection and analysis. In the fields of meteorology and air quality monitoring, this technology plays a crucial role by combining data from different sources—such as satellite imagery, ground-based sensors, and weather stations—to provide a comprehensive understanding of atmospheric conditions.
By leveraging advanced algorithms and machine learning techniques, multi-sensor data fusion enables the real-time processing of vast amounts of data, leading to improved forecasting models and timely alerts for environmental hazards. This approach not only enhances the precision of weather predictions but also facilitates the better management of air quality, ultimately contributing to public health and safety. As urbanization and climate change continue to challenge traditional monitoring methods, the adoption of multi-sensor data fusion represents a significant advancement in environmental monitoring and management.
We welcome contributions that explore, but are not limited to, the following areas:
- Innovative Data Fusion Techniques: Algorithms and methodologies for integrating data from diverse sensor networks.
- Real-time Monitoring Systems: Development and implementation of systems for real-time meteorological and air quality data collection.
- Case Studies: Successful applications of multi-sensor data fusion in meteorology and air quality monitoring.
- Machine Learning and AI: Utilization of artificial intelligence in improving data fusion processes and predictive modeling.
Prof. Dr. He Fang
Guest Editor
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Keywords
- multi-sensor data fusion technology
- air quality monitoring
- environmental monitoring
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