Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things
by
Christophe Guyeux 1,*,†
, 
Stéphane Chrétien 2,3,†, Gaby Bou Tayeh 1,†, Jacques Demerjian 4,† and Jacques Bahi 1,†
Christophe Guyeux 1,*,†, Stéphane Chrétien 2,3,†, Gaby Bou Tayeh 1,†, Jacques Demerjian 4,† and Jacques Bahi 1,†
1
Femto-ST Institute, UMR 6174 CNRS, University of Bourgogne-Franche-Comté, 90000 Besançon, France
2
National Physical Laboratory, Teddington, Middlesex TW11 0LW, UK
3
Laboratoire ERIC, Université Lyon 2, 69500 Bron, France
4
LaRRIS, Faculty of Sciences, Lebanese University, 90656 Fanar, Lebanon
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Sens. Actuator Netw. 2019, 8(4), 56; https://doi.org/10.3390/jsan8040056
Received: 28 October 2019 / Revised: 2 December 2019 / Accepted: 4 December 2019 / Published: 9 December 2019
(This article belongs to the Special Issue Massive Sensory Data Management in WSN, IoT and CPS)
The use of wireless sensor networks, which are the key ingredient in the growing Internet of Things (IoT), has surged over the past few years with a widening range of applications in the industry, healthcare, agriculture, with a special attention to monitoring and tracking, often tied with security issues. In some applications, sensors can be deployed in remote, large unpopulated areas, whereas in others, they serve to monitor confined busy spaces. In either case, clustering the sensor network’s nodes into several clusters is of fundamental benefit for obvious scalability reasons, and also for helping to devise maintenance or usage schedules that might greatly improve the network’s lifetime. In the present paper, we survey and compare popular and advanced clustering schemes and provide a detailed analysis of their performance as a function of scale, type of collected data or their heterogeneity, and noise level. The testing is performed on real sensor data provided by the UCI Machine Learning Repository, using various external validation metrics.
View Full-Text
▼
Show Figures
Figure 1
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
MDPI and ACS Style
Guyeux, C.; Chrétien, S.; Bou Tayeh, G.; Demerjian, J.; Bahi, J. Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things. J. Sens. Actuator Netw. 2019, 8, 56.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
