University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Adaptive Clustering for Dynamic IoT Data Streams

Puschmann, D, Barnaghi, P and Tafazolli, R (2016) Adaptive Clustering for Dynamic IoT Data Streams IEEE Internet of Things Journal.

This is the latest version of this item.


Download (1MB) | Preview


The emergence of the Internet of Things (IoT) has led to the production of huge volumes of real-world streaming data. We need effective techniques to process IoT data streams and to gain insights and actionable information from realworld observations and measurements. Most existing approaches are application or domain dependent. We propose a method which determines how many different clusters can be found in a stream based on the data distribution. After selecting the number of clusters, we use an online clustering mechanism to cluster the incoming data from the streams. Our approach remains adaptive to drifts by adjusting itself as the data changes. We benchmark our approach against state-of-the-art stream clustering algorithms on data streams with data drift. We show how our method can be applied in a use case scenario involving near real-time traffic data. Our results allow to cluster, label and interpret IoT data streams dynamically according to the data distribution. This enables to adaptively process large volumes of dynamic data online based on the current situation. We show how our method adapts itself to the changes. We demonstrate how the number of clusters in a real-world data stream can be determined by analysing the data distributions.

Item Type: Article
Subjects : Communcation Systems
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Authors : Puschmann, D, Barnaghi, P and Tafazolli, R
Date : 2016
Funders : European Commission
Grant Title : EU FP7 CityPulse project
Copyright Disclaimer : Copyright 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Projects : CityPulse
Related URLs :
Depositing User : Payam Barnaghi
Date Deposited : 21 Dec 2016 11:59
Last Modified : 06 Jul 2019 05:16

Available Versions of this Item

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800