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Sensors 2018, 18(4), 1205; https://doi.org/10.3390/s18041205

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

1
Institute of Communication and Computer Systems (ICCS), School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens 157 80, Greece
2
Department of Computer Science, Boston University, Boston, MA 02215, USA
Current address: Iroon Polytechniou 9, Zografou, Athens 157 80, Greece.
The author was with the Institute of Communication and Computer Systems (ICCS), School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Greece, for the main part of this work. He is now with Boston University, MA, USA. Current address: 111 Cummington Mall, Boston, MA 02215, USA.
*
Author to whom correspondence should be addressed.
Received: 16 March 2018 / Revised: 12 April 2018 / Accepted: 13 April 2018 / Published: 15 April 2018
(This article belongs to the Section Sensor Networks)
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Abstract

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. View Full-Text
Keywords: data clustering; community detection; Girvan–Newman algorithm; hyperbolic network embedding; Rigel embedding; edge-betweenness centrality; smart-cities/buildings data clustering; community detection; Girvan–Newman algorithm; hyperbolic network embedding; Rigel embedding; edge-betweenness centrality; smart-cities/buildings
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Karyotis, V.; Tsitseklis, K.; Sotiropoulos, K.; Papavassiliou, S. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications. Sensors 2018, 18, 1205.

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