Sensor similarity search in the Web of Things

Cuong Truong; Kay Römer; Kai Chen

doi:10.1109/WoWMoM.2012.6263791

Sensor similarity search in the Web of Things

Cuong Truong, Kay Römer, Kai Chen

Institut für Technische Informatik

Institute of Computer Engineering

14 Zitate (Scopus)

Abstract

An increasing number of sensors is being connected to the Internet and their output is published on theWeb, resulting in the formation of a Web of Things (WoT) that will soon connect tens of Billions of devices. We propose sensor similarity search, where given a sensor, other sensors on the WoT are found that produced similar output in the past. At the heart of our approach is an algorithm that exploits fuzzy sets for efficiently computing a similarity score for a pair of sensors that is used to obtain a ranked list of matching sensors. Using sensor data sets from real deployments, we find that this approach results in a high accuracy.

Originalsprache	Englisch
Titel	2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2012 - Digital Proceedings
Seitenumfang	6
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	24.09.2012
Seiten	1-6
Aufsatznummer	6263791
ISBN (Print)	978-1-4673-1238-7
ISBN (elektronisch)	978-1-4673-1239-4
DOIs	https://doi.org/10.1109/WoWMoM.2012.6263791
Publikationsstatus	Veröffentlicht - 24.09.2012
Veranstaltung	13th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks - San Francisco, USA / Vereinigte Staaten Dauer: 25.06.2012 → 28.06.2012 Konferenznummer: 92704

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10.1109/WoWMoM.2012.6263791

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abstract = "An increasing number of sensors is being connected to the Internet and their output is published on theWeb, resulting in the formation of a Web of Things (WoT) that will soon connect tens of Billions of devices. We propose sensor similarity search, where given a sensor, other sensors on the WoT are found that produced similar output in the past. At the heart of our approach is an algorithm that exploits fuzzy sets for efficiently computing a similarity score for a pair of sensors that is used to obtain a ranked list of matching sensors. Using sensor data sets from real deployments, we find that this approach results in a high accuracy.",

author = "Cuong Truong and Kay R{\"o}mer and Kai Chen",

year = "2012",

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booktitle = "2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2012 - Digital Proceedings",

note = "13th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2012 ; Conference date: 25-06-2012 Through 28-06-2012",

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Truong, C, Römer, K & Chen, K 2012, Sensor similarity search in the Web of Things. in 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2012 - Digital Proceedings., 6263791, IEEE Proceedings, IEEE, S. 1-6, 13th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, San Francisco, California, USA / Vereinigte Staaten, 25.06.12. https://doi.org/10.1109/WoWMoM.2012.6263791

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N2 - An increasing number of sensors is being connected to the Internet and their output is published on theWeb, resulting in the formation of a Web of Things (WoT) that will soon connect tens of Billions of devices. We propose sensor similarity search, where given a sensor, other sensors on the WoT are found that produced similar output in the past. At the heart of our approach is an algorithm that exploits fuzzy sets for efficiently computing a similarity score for a pair of sensors that is used to obtain a ranked list of matching sensors. Using sensor data sets from real deployments, we find that this approach results in a high accuracy.

AB - An increasing number of sensors is being connected to the Internet and their output is published on theWeb, resulting in the formation of a Web of Things (WoT) that will soon connect tens of Billions of devices. We propose sensor similarity search, where given a sensor, other sensors on the WoT are found that produced similar output in the past. At the heart of our approach is an algorithm that exploits fuzzy sets for efficiently computing a similarity score for a pair of sensors that is used to obtain a ranked list of matching sensors. Using sensor data sets from real deployments, we find that this approach results in a high accuracy.

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PB - IEEE

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ER -

Sensor similarity search in the Web of Things

Abstract

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