Knowledge-Driven Sensing and Data Integration for Environmental and Healthcare Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 205
Special Issue Editors
Interests: artificial intelligence; computer vision; data analytics; deep learning; digital twins
Special Issues, Collections and Topics in MDPI journals
Interests: reinforcement learning; AI in rehabilitation; cyber-human systems; robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sensing technologies are rapidly expanding across environmental monitoring and healthcare, producing high-volume, heterogeneous streams from in situ sensors, remote sensing platforms, wearables, mobile devices and clinical information systems. Yet the practical impact of these data is often limited by fragmentation, inconsistent semantics, uncertain quality and weak interoperability across devices, sites and institutions. As a result, models and analytics built on sensor data can be difficult to validate, hard to generalize and challenging to translate into trustworthy decisions or actionable interventions.
This Special Issue focuses on knowledge-driven approaches—including knowledge graphs, ontologies, semantic integration, provenance and reasoning—to improve how sensor data are represented, fused, interpreted and connected to downstream decision-making. We welcome contributions that advance the foundations and applications of knowledge-enhanced sensing and data integration, spanning topics such as multimodal data fusion, uncertainty and bias characterization, interoperable pipelines, explainable and constraint-aware analytics and real-world deployments in environmental and healthcare systems. The goal is to bridge the gap from sensor observations to robust, transparent and decision-ready insights.
Dr. Jiaqi (Jackey) Gong
Dr. Soroush Korivand
Guest Editors
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Keywords
- knowledge-driven sensing
- knowledge graphs
- ontologies and semantic modeling
- semantic interoperability
- sensor data fusion
- multimodal data integration
- provenance and metadata
- uncertainty quantification
- explainable AI for sensing systems
- environmental and healthcare monitoring
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