Sensor search techniques for sensing as a service architecture for the Internet of Things

Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346, Zaslavsky, Arkady, Liu, Chi Harold, Compton, Michael, Christen, Peter and Georgakopoulos, Dimitrios 2013. Sensor search techniques for sensing as a service architecture for the Internet of Things. IEEE Sensors Journal 14 (2) , pp. 406-420. 10.1109/JSEN.2013.2282292

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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 (ICOs) that 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 considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This 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 paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1530-437X
Date of First Compliant Deposit:	10 August 2020
Date of Acceptance:	13 September 2013
Last Modified:	15 Nov 2023 14:13
URI:	https://orca.cardiff.ac.uk/id/eprint/134050

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