Sensor search techniques for sensing as a service architecture for the internet of things

Charith Perera, Arkady Zaslavsky, Chi Harold Liu, Michael Compton, Peter Christen, Dimitrios Georgakopoulos

    Research output: Contribution to journalArticlepeer-review

    179 Citations (Scopus)

    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.

    Original languageEnglish
    Article number6605518
    Pages (from-to)406-420
    Number of pages15
    JournalIEEE Sensors Journal
    Volume14
    Issue number2
    DOIs
    Publication statusPublished - Feb 2014

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