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A Benchmarking Model for Sensors in Smart Environments

Braun, Andreas and Wichert, Reiner and Kuijper, Arjan and Fellner, Dieter W. (2014):
A Benchmarking Model for Sensors in Smart Environments.
Springer, Berlin, Heidelberg, New York, In: Ambient Intelligence, In: Lecture Notes in Computer Science (LNCS); 8850, DOI: 10.1007/978-3-319-14112-1₂₀, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Braun, Andreas and Wichert, Reiner and Kuijper, Arjan and Fellner, Dieter W.
Title: A Benchmarking Model for Sensors in Smart Environments
Language: English
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.

Series Name: Lecture Notes in Computer Science (LNCS); 8850
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Business Field: Digital society, Research Area: Modeling (MOD), Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Benchmarking, Smart environments, Modeling, Sensor technologies
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Ambient Intelligence
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-319-14112-1₂₀
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