Enhanced sensor web services by incorporating IoT interface protocols and spatio-temporal data streams for edge computing-based sensing
The Geospatial Sensor Web (GSW) integrates heterogeneous aerial and ground sensors via cloud-edge linkages and GIS-based approaches, forming a multi-dimensional observation network. However, existing systems struggle to support edge-side collaborative observation due to fragmented physical standards...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2450510 |
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| Summary: | The Geospatial Sensor Web (GSW) integrates heterogeneous aerial and ground sensors via cloud-edge linkages and GIS-based approaches, forming a multi-dimensional observation network. However, existing systems struggle to support edge-side collaborative observation due to fragmented physical standards, incompatible protocols, and limited self-configuration. This study proposes an enhanced Sensor Web, integrating IoT protocols and spatio-temporal models for unified access, collaborative management, and dynamic planning. Validated through the City Sensing Base Station (CSBS), a pilot experiment demonstrated the framework integrates diverse sensing resources across over eight protocols, achieving autonomous alignment of more than five platforms with rapid aerial-ground network formation during emergencies. It also validated autonomous collaboration and coordination of aerial-ground resources, enabling dynamic task allocation and execution across heterogeneous systems. Compared with cloud-based architectures, this approach significantly improves resource accessibility and real-time processing. By extending SensorML and Sensor Observation Service (SOS), the framework bridges the gap between conventional Sensor Webs and edge computing demands. Results confirm its effectiveness in coordinating heterogeneous resources and managing dynamic spatio-temporal data. These findings show how Internet of Things (IoT) protocols advance earth observation, modeling and improve GSW efficiency. |
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| ISSN: | 1009-5020 1993-5153 |