Computational Discovery: Diversity Supplement with Sensor Technology
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 1588
Special Issue Editors
Interests: cloud computing; learning system; gas fuel manufacture; electronic learning; web service; learning resource; e-learning systems; quality model
Special Issues, Collections and Topics in MDPI journals
Interests: information system and management; data analysis with AI; NLP; data mining
Special Issues, Collections and Topics in MDPI journals
Interests: big data analytics; network analysis
Special Issue Information
Dear Colleagues,
Computational Discovery: Complementing Diversity through Sensor Technology' refers to a comprehensive technology that maximizes practical effectiveness and efficiency through data collection and analysis using sensors in various fields. As sensors become more sophisticated and generalized, various application technologies are being developed and distributed not only in specialized fields but also more generally real life. In addition, digital twin technologies using VR, AR, XR, MR, etc., can be simulated to prepare for environments with sensors that are difficult or dangerous to apply right away in the real world, reducing the difference between real and virtual environments. In particular, effective application technologies are being developed in various fields such as medicine, environmental monitoring, and smart city development, and more accurate system models and predictability are presented through various data inputs. In other words, Computing Discovery: Complementing Diversity through Sensor Technology' will lead technological innovation while protecting the full potential of sensor technology from unintended consequences.
For example, the field of advanced sensor networks is the study of the design and development of sensor networks in the field of edge and fog computing. The field of multi-sensor data fusion technology studies how to improve the accuracy and reliability of computational models by integrating data from different types of sensors. The field of machine learning for AI and sensor data can apply machine learning technology to analyze complex datasets from sensors, with an emphasis on improving model performance and reducing bias. The field of smart city innovation is being studied to investigate how to use sensor networks to create smarter urban environments and improve infrastructure, transportation, and public services. At this time, unexpected risk factors can be reduced by performing various simulations before actually creating a smart city by applying digital twin technology.
In this Special Issue, we welcome research on various methods and application technologies carried out using sensor technology. In particular, we request studies or academic papers on practical development technologies using machine learning, deep learning, and big data. Topics of particular interest are as follows:
- Multimodal Data Fusion Techniques;
- Real-Time Data Processing Algorithms;
- Digital Twin using VR, MR, XR and AR;
- Empirical Systems and Applications using Digital Twin;
- Bias Detection and Mitigation in Sensor Data;
- Machine Learning for Sensor Data;
- Applications in Healthcare Monitoring;
- Environmental Monitoring;
- Smart City Innovations using Sensors;
- Human-Computer Interaction with Sensors;
- Technology for Autonomous Vehicles;
- Systems or Applications using IoT (Internet of Things) and IoE (Internet of Everything);
- Fog/Edge Computing.
Prof. Dr. Hwa-Young Jeong
Dr. Jason C. Hung
Dr. Jeongeun Byun
Guest Editors
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Keywords
- multimodal data fusion techniques
- real-time data processing algorithms
- digital twin using VR, MR, XR and AR
- empirical systems and applications using Digital Twin
- bias detection and mitigation in sensor data
- machine learning for sensor data
- applications in healthcare monitoring
- environmental monitoring
- smart city innovations using sensors
- human-computer interaction with sensors
- technology for autonomous vehicles
- systems or applications using IoT (Internet of Things) and IoE (Internet of Everything)
- Fog/Edge computing
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