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Supporting IoT Data Similarity at the Edge Towards Enabling Distributed Clustering

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Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 745))

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Abstract

Hundreds of billions of things are expected to be integrated for heterogeneous Internet-of-Things (IoT) applications, which promises to drive the Future Internet. This variant IoT data mandates intelligent solutions to make sense of current data in real-time closer to the data origin. Clustering physically distributed data would enable efficient utilization where finding similarity becomes the central issue. To counter this, Jaro-Winkler and Jaccard-like algorithm have been proposed and extended to a distributed protocol to enable distributed clustering at the edge. Performance study, on a scalable IoT platform and an edge device, shows feasibility and effectiveness of the approach with respect to efficiency and applicability.

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Authors and Affiliations

  1. Department of Computer and Systems Sciences (DSV), Stockholm University, Nod Building, SE-164 55, Kista, Sweden

    Hasibur Rahman

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  1. Hasibur Rahman
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Correspondence to Hasibur Rahman .

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Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Rahman, H. (2018). Supporting IoT Data Similarity at the Edge Towards Enabling Distributed Clustering. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-77703-0_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77702-3

  • Online ISBN: 978-3-319-77703-0

  • eBook Packages: EngineeringEngineering (R0)

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