A Core Ontological Model for Semantic Sensor Web Infrastructures

García‐Castro, Raúl; Corcho, Óscar; Hill, Chris

doi:10.4018/jswis.2012010102

Cited by 10 publications

(1 citation statement)

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“…Furthermore, our evaluation results can be used to understand the scalability and computational performance of their working big data paradigm as both approaches use the SSN ontology. Garcia-Castro et al [22] have defined a core ontological model for Semantic sensor web infrastructures. It can be used to model sensor networks (by extending the SSN ontology), sensor data sources, and the web services that expose the data sources.…”

Section: Background and Related Workmentioning

confidence: 99%

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Perera¹,

Zaslavsky²,

Liu

et al. 2014

IEEE Sensors J.

175

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Abstract-The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Index Terms-Internet of Things, context awareness, sensors, search and selection, indexing and ranking, semantic querying, quantitative reasoning, multi-dimensional data fusion.

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Section: Background and Related Workmentioning

confidence: 99%