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Supporting IoT Data Similarity at the Edge towards Enabling Distributed Clustering
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-8506-5839
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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 IoT edge computing. Performance study, on a scalable IoT platform and an edge device, shows feasibility and effectiveness of the approach with respect to efficiency and applicability. 

Place, publisher, year, edition, pages
2018.
Keyword [en]
IoT, distributed data, clustering similarity, edge computing
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-149498OAI: oai:DiVA.org:su-149498DiVA: diva2:1162180
Conference
WorldCist'18 - 6th World Conference on Information Systems and Technologies, Naples, Italy, 27 - 29 March 2018
Note

Submitted.

Available from: 2017-12-03 Created: 2017-12-03 Last updated: 2017-12-04
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