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Supporting IoT Data Similarity at the Edge Towards Enabling Distributed Clustering

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

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.

Keywords

IoT Distributed data Clustering similarity Edge computing 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer and Systems Sciences (DSV)Stockholm UniversityKistaSweden

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