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A Benchmarking Model for Sensors in Smart Environments

Braun, Andreas ; Wichert, Reiner ; Kuijper, Arjan ; Fellner, Dieter W. (2014)
A Benchmarking Model for Sensors in Smart Environments.
Ambient Intelligence.
doi: 10.1007/978-3-319-14112-1_20
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

In smart environments, developers can choose from a large variety of sensors supporting their use case that have specific advantages or disadvantages. In this work we present a benchmarking model that allows estimating the utility of a sensor technology for a use case by calculating a single score, based on a weighting factor for applications and a set of sensor features. This set takes into account the complexity of smart environment systems that are comprised of multiple subsystems and applied in non-static environments. We show how the model can be used to find a suitable sensor for a use case and the inverse option to find suitable use cases for a given set of sensors. Additionally, extensions are presented that normalize differently rated systems and compensate for central tendency bias. The model is verified by estimating technology popularity using a frequency analysis of associated search terms in two scientific databases.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2014
Autor(en): Braun, Andreas ; Wichert, Reiner ; Kuijper, Arjan ; Fellner, Dieter W.
Art des Eintrags: Bibliographie
Titel: A Benchmarking Model for Sensors in Smart Environments
Sprache: Englisch
Publikationsjahr: 2014
Verlag: Springer, Berlin, Heidelberg, New York
Reihe: Lecture Notes in Computer Science (LNCS); 8850
Veranstaltungstitel: Ambient Intelligence
DOI: 10.1007/978-3-319-14112-1_20
Kurzbeschreibung (Abstract):

In smart environments, developers can choose from a large variety of sensors supporting their use case that have specific advantages or disadvantages. In this work we present a benchmarking model that allows estimating the utility of a sensor technology for a use case by calculating a single score, based on a weighting factor for applications and a set of sensor features. This set takes into account the complexity of smart environment systems that are comprised of multiple subsystems and applied in non-static environments. We show how the model can be used to find a suitable sensor for a use case and the inverse option to find suitable use cases for a given set of sensors. Additionally, extensions are presented that normalize differently rated systems and compensate for central tendency bias. The model is verified by estimating technology popularity using a frequency analysis of associated search terms in two scientific databases.

Freie Schlagworte: Business Field: Digital society, Research Area: Modeling (MOD), Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Benchmarking, Smart environments, Modeling, Sensor technologies
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 04 Feb 2022 12:39
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