A Comprehensive Ontology for Knowledge Representation in the Internet of Things

Wang, Wei; De, Suparna; Toenjes, Ralf; Reetz, Eike Steffen; Moessner, Klaus

doi:10.1109/trustcom.2012.20

Cited by 132 publications

(63 citation statements)

References 11 publications

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“…In [37], authors point out that most work related with IoT interoperability has mostly focused on resource management, and not on how to utilize the information generated. They proposed a description ontology for the IoT Domain by integrating and extending existing work in modelling concepts in IoT.…”

Section: Service Discovery For Iot Environmentsmentioning

confidence: 99%

On the application of contextual IoT service discovery in Information Centric Networks

Quevedo

Antunes

Corujo

et al. 2016

Computer Communications

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The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several e↵orts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among di↵erent devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios. IntroductionIn the last few years, the coupling of networking communication capabilities and devices with disparate characteristics and capabilities (e.g., sensors, actuators) has prompted di↵erent actors (ranging from academia, to service providers, manufacturers and operators) into the development of solutions towards an Internet of Things (IoT). These solutions are able to remotely exploit the sensing and actuating capabilities of such devices and convey them into communicating and processing platforms, empowering di↵erent kinds of "smart" scenarios [1,2]. The added value generated by bridging the physical and digital worlds has contributed to a continuously increasing massification of connected devices and generated information exchanges ([3] indicates 7.3 billion Machine-to-Machine (M2M) networked devices by 2018, globally), raising connectivity provisioning and operation concerns at all levels. The stringent new requirements placed over the underlying networking fabric to support this connectivity explosion have prompted the need for ground-breaking ideas and solutions, able not only to support these challenges, but also to confer the capability and flexibility to better face future challenges and requirements.Information Centric Networking (ICN) [4,5] is an emerging networking paradigm that has content at the centre of the networking functions, shifting from the curre...

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Section: Service Discovery For Iot Environmentsmentioning

confidence: 99%

On the application of contextual IoT service discovery in Information Centric Networks

Quevedo

Antunes

Corujo

et al. 2016

Computer Communications

View full text Add to dashboard Cite

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“…Figure 3 shows an overview of the proposed information model. Some of this modules has been adapted from the IoT.est model [15]. In the next subsections we describe these three modules.…”

Section: Data Modelling and Semantic Annotationmentioning

confidence: 99%

“…It describes content-derived data about IoT streams and provides concepts such as sao:StreamData, sao:Segment, sao:SegmentAnalysis on top of the TimeLine 3 [16] and IoTest models [15]. The SAO uses a broad definition of the StreamEvent concept in order to express an artificial classification of a time region, corresponding to a particular stream data.…”

Section: Sao Modulementioning

confidence: 99%

“…Within the EU FP7 IoT-A project [30] an information model was created. The IoT.est [15] defined semantic representations enhancing the IoT-A model with some service and test concepts. The Open Geospatial Consortium (OGC) describes Observation and Measurement (O&M) concepts for sensory data as a part of the Sensor Web Enablement (SWE) standard [31].…”

Section: Semantics In Iot Application Domainmentioning

confidence: 99%

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On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Kolozali

Puschmann

Bermúdez-Edo

et al. 2016

IEEE Internet Things J.

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Abstract-With the growing popularity of Information and Communications Technologies (ICT) and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMQP). We provide a comprehensive analysis on the effect of adaptive and non-adaptive window size in segmentation of time series using SensorSAX and SAX approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilised segmentation approach, due to the fact that each geographically different sensory environment has got different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

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“…The Knowledge Management block is based upon the IoT.est description ontology which is designed as the semantic description framework for the proposed architecture. It provides a light-weight modular framework for describing different aspects such as resources, test and services and is detailed in [3]. The services and resources are linked not only to the concepts in the domain knowledge base but also to other existing sources on the Semantic Web.…”

mentioning

confidence: 99%

Test-Enabled Architecture for IoT Service Creation and Provisioning

Carrez

Reetz

et al. 2013

The Future Internet

Self Cite

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Abstract. The information generated from the Internet of Things (IoT) potentially enables a better understanding of the physical world for humans and supports creation of ambient intelligence for a wide range of applications in different domains. A semantics-enabled service layer is a promising approach to facilitate seamless access and management of the information from the large, distributed and heterogeneous sources. This paper presents the efforts of the IoT.est project towards developing a framework for service creation and testing in an IoT environment. The architecture design extends the existing IoT reference architecture and enables a test-driven, semantics-based management of the entire service lifecycle. The validation of the architecture is shown though a dynamic test case generation and execution scenario.

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A Comprehensive Ontology for Knowledge Representation in the Internet of Things

Cited by 132 publications

References 11 publications

On the application of contextual IoT service discovery in Information Centric Networks

On the application of contextual IoT service discovery in Information Centric Networks

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Test-Enabled Architecture for IoT Service Creation and Provisioning

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