Semantic Sensor Web

Sheth, Amit P.; Henson, Cory; Sahoo, Satya S.

doi:10.1109/mic.2008.87

Cited by 526 publications

(352 citation statements)

References 2 publications

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“…using standardized semantics or ontological data structures as depicted in Fig. 1 [16][17][18][19]). The need for such a convergence of IoT ecosystems has been recognised from the early advent of the impact of IoT diversity in many environments including the foreseen growth in Smart Cities [18].…”

Section: Formulations Of the Concepts For Novel Iot Communication Modelsmentioning

confidence: 99%

“…However, IoT bring a physical, spatial, timely, use-related and connectivity factors in the equation when thinking about the search and discovery. As shown in [16], some of these factors have been recognised as key descriptions of sensors from the early advent of the technology and have in fact been the triggers for development of Semantic Sensor Web (SSW) using ontology languages such as OWL/RDF that are being perfected by international initiatives and working groups [17,19]. Much of the SSW ideas have gone into complex environmental descriptions and application-level search and processes.…”

Section: Different Fundamentals Of Search and Discovery Of Iot Devicementioning

confidence: 99%

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Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

Mihailović

2016

Wireless Pers Commun

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There is an ongoing enormous expansion of Internet of Things devices and services in everyday life, notably in novel large scale urban environments called Smart Cities. There, availability and uses of Internet of Things by end users and businesses is mainly palpable subject to prior knowledge of the relevant providers and use of dedicated applications that are associated with them. This current reality can be largely ascribed to the property of ''verticality'' of autonomous Internet of Things eco-systems in Smart Cities, where Internet of Things devices (e.g. sensor nodes) are connected over a communication infrastructure to service-cloud platforms that deliver and process data that is then presented at the applications level. This paper explains possibilities for revolutionary changes needed towards liberalising deployment and visibility of IoT services and data associated with them. It advocates a conceptual approach termed ''horizontal networking for Internet of Things'' facilitating a more open and generic presence of Internet of Things through the proposed Internet of Things identification meta-data. The vision is built on needed novel practical features in the current communication setups. The features comprise combinations of the opportunistic and near-match search and discovery model, Internet of Things identification meta-data also reflecting the physical and network-based dimensions of devices' locations, novel routing and data flow models emerging via InformationCentric Networking and changes required in the elements of the current telecommunication infrastructure and the Internet.

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Section: Formulations Of the Concepts For Novel Iot Communication Modelsmentioning

confidence: 99%

Section: Different Fundamentals Of Search and Discovery Of Iot Devicementioning

confidence: 99%

Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

Mihailović

2016

Wireless Pers Commun

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“…It describes the capabilities and properties of sensors, the act of sensing and the resulting observations. It is compatible with OGC standards and can be used to enable the Semantic Sensor Web [25].…”

Section: Related Workmentioning

confidence: 99%

Negotiation in Internet of Things

Misura¹,

Žagar²

2016

Automatika Journal

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“…In the following, we outline the functioning of a new built-in to collect observations from the Sensor Web, which is currently under development. The presented approach can be regarded as a more light-weight variant of the Semantic Sensor Web [41], which builds on fully annotated sensor web services.…”

Section: The Swrl Built-in Bridgementioning

confidence: 99%

Semantic Rules for Context-Aware Geographical Information Retrieval

Keßler

Raubal

Wosniok

2009

Lecture Notes in Computer Science

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Abstract. Geographical information retrieval (GIR) can benefit from context information to adapt the results to a user's current situation and personal preferences. In this respect, semantics-based GIR is especially challenging because context information -such as collected from sensors -is often provided through numeric values, which need to be mapped to ontological representations based on nominal symbols. The Web Ontology Language (OWL) lacks mathematical processing capabilities that require free variables, so that even basic comparisons and distance calculations are not possible. Therefore, the context information cannot be interpreted with respect to the task and the current user's preferences. In this paper, we introduce an approach based on semantic rules that adds these processing capabilities to OWL ontologies. The task of recommending personalized surf spots based on user location and preferences serves as a case study to evaluate the capabilities of semantic rules for context-aware geographical information retrieval. We demonstrate how the Semantic Web Rule Language (SWRL) can be utilized to model user preferences and how execution of the rules successfully retrieves surf spots that match these preferences. While SWRL itself enables free variables, mathematical functions are added via built-ins -external libraries that are dynamically loaded during rule execution. Utilizing the same mechanism, we demonstrate how SWRL built-ins can query the Semantic Sensor Web to enable the consideration of real-time measurements and thus make geographical information retrieval truly context-aware.

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Semantic Sensor Web

Cited by 526 publications

References 2 publications

Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

Negotiation in Internet of Things

Semantic Rules for Context-Aware Geographical Information Retrieval

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