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13
A linkeddata model for semantic sensor streams
- In Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing
, 2013
"... Abstract—This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sen-sor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant n ..."
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Abstract—This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sen-sor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes. I.
K.: Xgsn: An open-source semantic sensing middleware for the web of things
- In: Proc. of the 7th International Workshop on Semantic Sensor Networks
, 2014
"... Abstract. We present XGSN, an open-source system that relies on se-mantic representations of sensor metadata and observations, to guide the process of annotating and publishing sensor data on the Web. XGSN is able to handle the data acquisition process of a wide number of devices and protocols, and ..."
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Abstract. We present XGSN, an open-source system that relies on se-mantic representations of sensor metadata and observations, to guide the process of annotating and publishing sensor data on the Web. XGSN is able to handle the data acquisition process of a wide number of devices and protocols, and is designed as a highly extensible platform, leveraging on the existing capabilities of the Global Sensor Networks (GSN) mid-dleware. Going beyond traditional sensor management systems, XGSN is capable of enriching virtual sensor descriptions with semantically an-notated content using standard vocabularies. In the proposed approach, sensor data and observations are annotated using an ontology network based on the SSN ontology, providing a standardized queryable repre-sentation that makes it easier to share, discover, integrate and interpret the data. XGSN manages the annotation process for the incoming sensor observations, producing RDF streams that are sent to the cloud-enabled Linked Sensor Middleware, which can internally store the data or perform continuous query processing. The distributed nature of XGSN allows de-ploying different remote instances that can interchange observation data, so that virtual sensors can be aggregated and consume data from other remote virtual sensors. In this paper we show how this approach has been implemented in XGSN, and incorporated to the wider OpenIoT platform, providing a highly flexible and scalable system for managing the life-cycle of sensor data, from acquisition to publishing, in the context of the semantic Web of Things. 1
Knowledge Representation in the Internet of Things: Semantic
"... Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the “Things”. We present ..."
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Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the “Things”. We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.
Resource-Constrained Reasoning Using a Reasoner Composition Approach
"... Abstract: To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware comp ..."
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Abstract: To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resource-constrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a “well-suited ” amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices.
Generic semantic platform for the user-friendly development of intelligent IoT services
"... Abstract The rising popularity of the Internet of Things (IoT) requires simple and user-friendly methods for designing and deploying intelligent IoT services. End-to-End IoT platforms should support the straightforward integration of various sensors and devices, provide user-friendly methods to def ..."
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Abstract The rising popularity of the Internet of Things (IoT) requires simple and user-friendly methods for designing and deploying intelligent IoT services. End-to-End IoT platforms should support the straightforward integration of various sensors and devices, provide user-friendly methods to define the complex processing that should be performed on the captured IoT data and allow easy deployment on Big Data frameworks to ensure scalability. In this paper we present a generic and scalable platform that allows the automatic detection and integration of various sensors, provides intelligent processing of the captured data through semantic reasoning technologies and scalable and reliable deployment on a Big Data infrastructure. The configuration of the platform and the definition of its intelligent processes can be performed through a visual interface. The focus of the platform is to provide user-friendly support for defining complex IoT applications with limited to no need for coding.
Internet of things: A survey on . . .
"... This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies and Internet protocols. The basic premise is to have smart senso ..."
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This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols and applications. Compared to other
Domain knowledge Interoperability to build the Semantic Web of Things W3C Workshop on the Web of Things
"... Semantic Web of Things (SWoT) is a new field to combine Internet of Things (IoT) and semantic web technologies. We observe that the semantic web guidelines are generally not known by the IOT community which hinders automation or reuse of domain knowledge (ontologies, datasets and rules) whereas ini ..."
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Semantic Web of Things (SWoT) is a new field to combine Internet of Things (IoT) and semantic web technologies. We observe that the semantic web guidelines are generally not known by the IOT community which hinders automation or reuse of domain knowledge (ontologies, datasets and rules) whereas initially an ontology was designed to be easily shared and reused.
Article Integrated Semantics Service Platform for the Internet of Things: A Case Study of a Smart Office
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For a list of other books in this series, please visit www.riverpublishers.com. Internet of Things: Converging Technologies
"... This series focuses on communications science and technology. This includes the theory and use of systems involving all terminals, computers, and infor-mation processors; wired and wireless networks; and network layouts, pro-contentsols, architectures, and implementations. Furthermore, developments ..."
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This series focuses on communications science and technology. This includes the theory and use of systems involving all terminals, computers, and infor-mation processors; wired and wireless networks; and network layouts, pro-contentsols, architectures, and implementations. Furthermore, developments toward new market demands in systems, prod-ucts, and technologies such as personal communications services, multimedia systems, enterprise networks, and optical communications systems.
unknown title
"... Big data, a new way of managing and interacting with the massive data sets collected and stored by humans. The problem with the massive data collection and distribution systems is to manage this big data as large amount of data that is gathered from various domains of all sizes and types. Most of th ..."
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Big data, a new way of managing and interacting with the massive data sets collected and stored by humans. The problem with the massive data collection and distribution systems is to manage this big data as large amount of data that is gathered from various domains of all sizes and types. Most of the captured data clutters lots of storage space because of which it has become a concern of individuals as awareness grows of breadth and depth of personal information being amazed in big data collection. Big data is a concern rather than precise term. In this paper we have discussed big data definitions with various aspects. Then followed by few case studies where in big data is being used. Smart-mall case study is discussed in detail in which customer behavior is analyzed to provide valuable feedback. Apart from that we have discussed issues such as fraud detections, loss of customers, customer behavior prediction etc. Keywords: Big data, Data Streams.
