SemSOS: Semantic sensor Observation Service

Henson, Cory; Pschorr, Josh; Sheth, Amit P.; Thirunarayan, Krishnaprasad

doi:10.1109/cts.2009.5067461

Cited by 149 publications

(115 citation statements)

References 4 publications

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“…However, the O&M ontology lacks semantics needed for stream IoT data and lacks temporal features to represent time-series observations in detail. Henson et al [33] proposed a similar approach mapping all XML tags of O&M ontology into OWL concepts. The authors present SPARQL queries to access the annotated data.…”

Section: Semantics In Iot Application Domainmentioning

confidence: 99%

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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Section: Semantics In Iot Application Domainmentioning

confidence: 99%

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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“…The temporal meta-data refers to the time interval duration whose sensor data has been captured. Thematic meta-data provides descriptive information about the sensor node, which can be derived by sensor data analysis, and utilizing tagging and textual descriptions [4]. It propose a new method that uses smarter data than raw sensor data and accomplish this by leveraging semantic technologies in order to provide and apply more meaningful representation of sensor data.…”

Section: E Semantic Sensor Observation Service (Semsos)mentioning

confidence: 99%

“…The main enhancement is providing self-descriptive meta-data for the transducer elements and embedded semantics in the descriptions which could be utilized in various sensor discoveries and reasoning applications. Although OntoSensor illustrates a semantic approach to sensor description and provides an extensive knowledge model, there is no distinctive data description model to facilitate interoperable data representation for sensors observation and measurement data [4,9].…”

Section: Related Workmentioning

confidence: 99%

NSSSD: A new semantic hierarchical storage for sensor data

Gheisari

Movassagh

Qin

et al. 2016

2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Abstract-Sensor networks usually generate mass of data, which if not structured for future applications, will require much effort on analytical processing and interpretations. Thus, storing sensor data in an effective and structured format is a key issue in the area of sensor networks. In the meantime, even a little improvement on data storing structure may lead to a significant effect on the lifetime and performance of the sensor network. This paper describes a new method for sensor storage that combines semantic web concepts, a data aggregation method along with aligning sensors in hierarchical form. This solution is able to reduce the amount of data stored at the sink nodes significantly. At the same time, the method structures sensed data in a way that we can respond to semantic web-based queries with less consumption of energy compared to previous conventional methods. Results show that, in some situations especially when the diversity of query responses and life of network are vital, the efficiency of our new solution is much better.

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“…While we also use the SOS[13] and O&M [9] components, our approach is more lightweight, with our event definitions not requiring substantial knowledge of programming or complex specification language. In [7], the SSW approach is extended to illustrate the advantages of semantic annotation of SOS services, focusing on a deep analysis of sensor data to discover important environmental events.…”

Section: Related Researchmentioning

confidence: 99%

Knowledge Acquisition from Sensor Data in an Equine Environment

Conroy

May

Roantree³

et al. 2011

Data Warehousing and Knowledge Discovery

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Abstract. Recent advances in sensor technology have led to a rapid growth in the availability of accurate, portable and low-cost sensors. In the Sport and Health Science domains, this has been used to deploy multiple sensors in a variety of situations in order to monitor participant and environmental factors of an activity or sport. As these sensors often output their data in a raw, proprietary or unstructured format, it is difficult to identify periods of interest, such as events or actions of interest to the Sport and Exercise Physiologists. In our research, we deploy multiple sensors on horses and jockeys while they engage in horse-racing training exercises. The Exercise Physiologists aim to identify events which contribute most to energy expenditure, and classify both the horse and jockey movement using basic accelerometer sensors. We propose a metadata driven approach to enriching the raw sensor data using a series of Profiles. This data then forms the basis of user defined algorithms to detect events using an Event-Condition-Action approach. We provide an Event Definition interface which is used to construct algorithms based on sensor measurements both before and after integration. The result enables the end user to express high level queries to meet their information needs.

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SemSOS: Semantic sensor Observation Service

Cited by 149 publications

References 4 publications

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

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

NSSSD: A new semantic hierarchical storage for sensor data

Knowledge Acquisition from Sensor Data in an Equine Environment

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