Transforming Sensor Data to RDF based on SSN Ontology

Zhang, Xiaoming; Zhao, Yunping; Liu, Wanming

doi:10.14257/astl.2015.81.20

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Thus, many experiments set up at Smart Homes and intelligent environments adopt these expression formats. Zhang [ 54 ] mainly focuses on transforming sensor data to RDF in his experiments. Satterfield [ 55 ] designs a smart home system in which it models sensor data to RDF triples.…”

Section: Key Techniquesmentioning

confidence: 99%

A Survey of Data Semantization in Internet of Things

Shi

Zhu

2018

Sensors

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With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.

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Section: Key Techniquesmentioning

confidence: 99%

A Survey of Data Semantization in Internet of Things

Shi

Zhu

2018

Sensors

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“…In recent years, many studies [3][4][5][6] based on SPARQL retrieval have made significant progress in sensor recommendation under the semantic sensor network. Zhang et al 4 proposed a schedule that it expressed sensor data into Resource Description Framework (RDF) form based on semantic sensor network standard, including XML-based mapping language algorithm (SASML) and Sensor Data to RDF Mapping (SDRM) algorithm. Bermudezedo et al 5 reduced the semantic description's complexity and processing time in the semantic sensor network.…”

Section: Introductionmentioning

confidence: 99%

A graph-based sensor recommendation model in semantic sensor network

Chen

Lin

et al. 2022

International Journal of Distributed Sensor Networks

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In the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user’s specific requirements of sensor attributes. Therefore, the recommendation results cannot satisfy the user’s needs. In this article, we propose a graph-based sensor recommendation model. The model mainly includes two parts: (1) Filtering nodes in data graph. In addition to using the traditional graph matching algorithm, we propose a threshold pruning algorithm to narrow the matching scope and improve the matching efficiency. (2) Recommending top- k sensors. We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. Finally, we recommend the top- k sensors to the user. By comparison, the graph-based sensor recommendation algorithm meets user’s needs more than other algorithms, and experiments show that our model outperforms several baselines in terms of both response time and precision.

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Multimapping Design of Complex Sensor Data in Environmental Observatories

Hajj-Hassan¹,

Arnaud

Castelltort

et al. 2016

Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

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International audienceEnvironmental resources (e.g., air quality, water quantity) are needed to understand fundamental questions such as global change. Such resources are often collected from sensors, including humans acting as sensors. Tools have emerged to manage such data in the form of time series and, in particular, the Sensor Observation Service (SOS) which offers a framework based on predefined relational database schema. Environmental observatories can be built using such frameworks, allowing to address specific key scientific questions by collecting and sharing large-scale environmental data. However, the strict schema of SOS database makes it difficult to integrate some data that cannot be directly mapped to the schema. Guidelines and best practices are offered in the literature in order to reuse standards from the Semantic Web but they do not cover all needs. In particular, they do not help to reflect the fact that a single environmental database can lead to several SOS models. Since being aware of these multiple possibilities is crucial for a better use of the observatories, we argue that some extensions of the existing works are required. In this paper, we thus propose an extension of existing vocabularies to achieve this goal. Our contribution is illustrated on the real case of the Lebanese-French O-LiFE environmental observatory

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Transforming Sensor Data to RDF based on SSN Ontology

Cited by 5 publications

References 7 publications

A Survey of Data Semantization in Internet of Things

A Survey of Data Semantization in Internet of Things

A graph-based sensor recommendation model in semantic sensor network

Multimapping Design of Complex Sensor Data in Environmental Observatories

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