A distributed resource management architecture for interconnecting Web-of-Things using uBox

Namatame, Naoya; Ding, Yong; Riedel, Till; Tokuda, Hideyuki; Miyaki, Takashi; Beigl, Michael

doi:10.1145/1993966.1993972

Cited by 9 publications

(4 citation statements)

References 11 publications

(12 reference statements)

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“…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. Our approach can also be integrated into the uBox [23] approach, to search things in the WoT domain using context information. Currently, uBox performs searches based on location tags and object (sensor) classes (types) (e.g.…”

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.

173

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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%

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

Perera¹,

Zaslavsky²,

Liu

et al. 2014

IEEE Sensors J.

173

View full text Add to dashboard Cite

show abstract

“…Therefore local or remote context information are streamed to the logitag service, which then get evaluated with the implemented DSL. To implement this, our platform features an API to access data in the SmartTECO system [11]. Logitag needs to have access to an appool service as an application source.…”

Section: System Architecturementioning

confidence: 99%

Temporally-relaxed conditions for activation of services in the web of things

Neumann

Hassler

Ding

et al. 2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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We present a language of temporal conditions for detecting concurrent events in embedded Web-enabled devices, and for triggering pervasive services on these systems. Based on the assumption that evaluating conditions on distributed devices is relevant for providing robustness and to foster scalability and web real-time, we discuss the feasibility of in-situ evaluation of the proposed conditions and conduct a performance study. In a smart environments use case, it is illustrated how the language can be used to state activation concerns of services on distributed Web resources. Our proposed architecture integrates the language with the Web of Things to foster simplified development of applications that mash up Webenabled devices.

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“…Usually consider sensor network positioning, but the node to be found has to repeatedly communicate with the locomotive node. If you want to find multiple nodes, the location service or the node to be found must be muted at the same time due to operational needs [ 3 ]. In this case, a silent location algorithm is proposed for the first time.…”

Section: Introductionmentioning

confidence: 99%

Motion Capture and Dance Image Discrimination Based on Silent Positioning Algorithm

Zhu

2022

Computational Intelligence and Neuroscience

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The silent location algorithm of the sensor network is a time synchronization location algorithm that can serve multiple users. Based on the detailed analysis of the development status of human motion measurement technology, this paper proposes a design of human motion capture device based on sensor network and realizes data conversion and filtering through experimental design. In this experiment, the sensor data processing is used to capture the human body positioning information in real time. The established node does not need to make a sound during the whole process and, at the same time, has strong concealment and easy expansion. In the silent positioning algorithm for sensor network nodes, combined with the sound ray tracking technology, the correction parameters are controlled according to the change of the objective function, and the effectiveness of the method is verified by simulation analysis. In terms of motion capture, the system can monitor, detect, and record human motion in real time. In order to obtain more high-resolution dance images with high-definition details and correspondingly process the dance images to improve the resolution, this paper uses the silent positioning algorithm technology to study the image resolution, and this is of great help to improve the resolution.

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A distributed resource management architecture for interconnecting Web-of-Things using uBox

Cited by 9 publications

References 11 publications

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

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

Temporally-relaxed conditions for activation of services in the web of things

Motion Capture and Dance Image Discrimination Based on Silent Positioning Algorithm

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