TU Darmstadt / ULB / TUbiblio

Benchmarking Sensors in Smart Environments - Method and Use Cases

Braun, Andreas and Wichert, Reiner and Kuijper, Arjan and Fellner, Dieter W. (2016):
Benchmarking Sensors in Smart Environments - Method and Use Cases.
In: Journal of Ambient Intelligence and Smart Environments, pp. 645-664, 8, (6), DOI: 10.3233/AIS-160402, [Article]

Abstract

Smart environment applications can be based on a large variety of different sensors that may support the same use case, but have specific advantages or disadvantages. Benchmarking can allow determining the most suitable sensor systems for a given application by calculating a single benchmarking score, based on weighted evaluation of features that are relevant in smart environments. This set of features has to represent the complexity of applications in smart environments. In this work we present a benchmarking model that can calculate a benchmarking score, based on nine selected features that cover aspects of performance, the environment and the pervasiveness of the application. Extensions are presented that normalize the benchmark-ing score if required and compensate central tendency bias, if necessary. We outline how this model is applied to capacitive proximity sensors that measure properties of conductive objects over a distance. The model is used to identify existing and find potential new application domains for this upcoming technology in smart environments.

Item Type: Article
Erschienen: 2016
Creators: Braun, Andreas and Wichert, Reiner and Kuijper, Arjan and Fellner, Dieter W.
Title: Benchmarking Sensors in Smart Environments - Method and Use Cases
Language: English
Abstract:

Smart environment applications can be based on a large variety of different sensors that may support the same use case, but have specific advantages or disadvantages. Benchmarking can allow determining the most suitable sensor systems for a given application by calculating a single benchmarking score, based on weighted evaluation of features that are relevant in smart environments. This set of features has to represent the complexity of applications in smart environments. In this work we present a benchmarking model that can calculate a benchmarking score, based on nine selected features that cover aspects of performance, the environment and the pervasiveness of the application. Extensions are presented that normalize the benchmark-ing score if required and compensate central tendency bias, if necessary. We outline how this model is applied to capacitive proximity sensors that measure properties of conductive objects over a distance. The model is used to identify existing and find potential new application domains for this upcoming technology in smart environments.

Journal or Publication Title: Journal of Ambient Intelligence and Smart Environments
Volume: 8
Number: 6
Uncontrolled Keywords: Guiding Theme: Smart City, Research Area: Human computer interaction (HCI), Smart environments, Modeling, Sensor technologies, Capacitive sensors, Capacitive proximity sensing, Benchmarking
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 03 May 2019 05:30
DOI: 10.3233/AIS-160402
Export:

Optionen (nur für Redakteure)

View Item View Item