2008
DOI: 10.1007/978-3-540-77419-8_8
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OASiS: A Service-Oriented Architecture for Ambient-Aware Sensor Networks

Abstract: Abstract. Heterogeneous sensor networks are comprised of ensembles of small, smart, and cheap sensing and computing devices that permeate the environment, as well as resource intensive sensors such as satellite imaging systems, meteorological stations, and security cameras. Emergency response, homeland security, and many other applications have a very real need to interconnect these diverse networks and access information in real-time. Web service technologies provide well-developed mechanisms for exchanging d… Show more

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Cited by 7 publications
(8 citation statements)
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References 19 publications
(20 reference statements)
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“…The OASiS is a tracking application for example fire detection and vehicle tracking [34,35]. The WSN-SOA has been tested for surveillance applications with the ability to detect seismic vibrations [36,37].…”
Section: The Taxonomy Of Middleware Architectures For Wsnsmentioning
confidence: 99%
See 3 more Smart Citations
“…The OASiS is a tracking application for example fire detection and vehicle tracking [34,35]. The WSN-SOA has been tested for surveillance applications with the ability to detect seismic vibrations [36,37].…”
Section: The Taxonomy Of Middleware Architectures For Wsnsmentioning
confidence: 99%
“…The OASiS is an Object-centric, Ambient aware Service-Oriented Sensor network applications, and Service-Oriented Framework introduced in [34]. The OASiS middleware includes various services, such as a dynamic service configurator, node manager, and object manager [34].…”
Section: Service-oriented Middleware (Som) Architectures Approachementioning
confidence: 99%
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“…This combination of methods is already in use in other areas of application. Koutsoukos, Kushwaha, Amundson, Neema and Sztipanovits, for example, use SOA combined with a dynamic service discovery in the field of wireless sensor networks [10]. New approaches on the development of embedded systems based on service orientation are discussed by Tsai, Wei, Paul, Chung, Huang and Chen in [11].…”
Section: International Journal Of Machine Learning and Computing Volmentioning
confidence: 99%