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Enhanced sensor web services by incorporating IoT interface protocols and spatio-temporal data streams for edge computing-based sensing.

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Titel: Enhanced sensor web services by incorporating IoT interface protocols and spatio-temporal data streams for edge computing-based sensing.
Autoren: Chen, Dong, Wang, Shaoju, Wang, Chao, Zhang, Xiang, Chen, Nengcheng
Quelle: Geo-Spatial Information Science; Oct2025, Vol. 28 Issue 5, p2507-2524, 18p
Schlagwörter: INTERNET of things, EDGE computing, AUTOMATED planning & scheduling, SENSOR networks, RESOURCE allocation, SPATIOTEMPORAL processes, SYNCHRONIZATION
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhanced sensor web services by incorporating IoT interface protocols and spatio-temporal data streams for edge computing-based sensing.
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  Data: Geo-Spatial Information Science; Oct2025, Vol. 28 Issue 5, p2507-2524, 18p
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10095020.2025.2450510
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      – Code: eng
        Text: English
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      – SubjectFull: INTERNET of things
        Type: general
      – SubjectFull: EDGE computing
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      – SubjectFull: AUTOMATED planning & scheduling
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      – SubjectFull: SENSOR networks
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      – SubjectFull: SPATIOTEMPORAL processes
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      – SubjectFull: SYNCHRONIZATION
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            NameFull: Chen, Dong
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              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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