Open Sensor Web Architecture: Core Services

Chu, Xingchen; Kobialka, Tom; Durnota, Bohdan; Buyya, Rajkumar

doi:10.1109/icisip.2006.4286069

Cited by 58 publications

(29 citation statements)

References 2 publications

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“…The integration of sensor networks with Grid computing brings out dual benefits (Chu et al 2006): (1) sensor networks can off-load heavy processing activities to the Grid and (2) Gridbased sensor applications can provide advance services for smart-sensing by deploying scenario-specific operators at runtime. Sensor web paradigm Sensor Web is an emerging paradigm and technology stack for integration of heterogeneous sensors into common informational infrastructure (Moe et al 2008;Mandl et al 2006).…”

Section: Sensor Web and Grid Integration: Main Problems And Possible mentioning

confidence: 99%

Grid and sensor web technologies for environmental monitoring

2009

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In this paper, we focus on the description of the Grid infrastructure that is under the development at the Space Research Institute NASU-NSAU (SRI). We describe several real-world applications that are being solved using the Grid infrastructure: numeral weather prediction (NWP), flood monitoring, and biodiversity assessment. We also review issues regarding the integration of the Sensor Web and Grid technologies for flood applications.

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Section: Sensor Web and Grid Integration: Main Problems And Possible mentioning

confidence: 99%

Grid and sensor web technologies for environmental monitoring

2009

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“…In order to be able to adapt to these changes in the long-term, SOPAF's implementation is based on the standards of the Sensor Web Enablement (SWE) initiative (Chu et al, 2006) of the Open GIS Consortium (OGC, 2010) that defines standardized service interfaces for the access to sensor data and networks. SWE completely hides the heterogeneity of the sensor network to the user and simplifies the access to the data.…”

Section: System Operation Processing and Archiving Facility (Sopaf)mentioning

confidence: 99%

A new permanent multi-parameter monitoring network in Central Asian high mountains – from measurements to data bases

Schöne

Zech

Unger‐Shayesteh

et al. 2013

Geosci. Instrum. Method. Data Syst.

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Abstract. Long-term monitoring of water resources and climate parameters at the scale of river basins requires networks of continuously operated in-situ stations. Since 2009, GFZ and CAIAG, in cooperation with the National Hydrometeorological Services (NHMS) of Central Asia, are establishing such a regional monitoring network in Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and lately Afghanistan to collect observations of meteorological and hydrological parameters and to deliver them to the end-users for operational tasks and scientific studies. The newly developed and installed remotely operated multi-parameter stations (ROMPS) do not only monitor standard meteorological and hydrological parameters, but also deliver Global Navigation Satellite System (GNSS) data for atmospheric sounding as well as tectonic studies. Additionally, three stations integrate seismic sensors for earthquake monitoring. The observational data from the ROMPS is transmitted nominally in near-real time, but at least once a day to a centralized geo-database infrastructure for long-term storage and data redistribution. Users can access the data manually using a web-interface or automatically using SOS requests; in addition, data is planed to be distributed to the NHMS through standard communication and data exchange channels.

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“…So, to implement a DMS, it is necessary to use a common and open communication protocol to exchange information and a methodology to auto-configure any smart sensor is linked to the network. This technology, known as smart Web sensor, makes various types of Web-resident sensors, instruments, image devices, and repositories of sensor data, discoverable, accessible, and controllable via the World Wide Web (Chu et al, 2006), (Lee et al, 2007). DMSs based on smart Web sensors represent an interesting solution to many different measurement problems .…”

Section: Smart Web Sensorsmentioning

confidence: 99%