Next Article in Journal
An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level
Next Article in Special Issue
Simple Adaptive Single Differential Coherence Detection of BPSK Signals in IEEE 802.15.4 Wireless Sensor Networks
Previous Article in Journal
Adaptive Wavelet Coding Applied in a Wireless Control System
Previous Article in Special Issue
Self-Coexistence among IEEE 802.22 Networks: Distributed Allocation of Power and Channel
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2902;

Performance Analysis of Cluster Formation in Wireless Sensor Networks

Instituto Politécnico Nacional—(CIC-IPN), Mexico City 07738, Mexico
INRIA Rennes—Bretagne Atlantique, Campus Universitaire de Beaulieu, 35042 Rennes CEDEX, France
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 4 December 2017 / Accepted: 7 December 2017 / Published: 13 December 2017
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
Full-Text   |   PDF [1497 KB, uploaded 15 December 2017]   |  


Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes. View Full-Text
Keywords: transmission probability; clustering; fuzzy C-means; K-medoids transmission probability; clustering; fuzzy C-means; K-medoids

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 (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Montiel, E.R.; Rivero-Angeles, M.E.; Rubino, G.; Molina-Lozano, H.; Menchaca-Mendez, R.; Menchaca-Mendez, R. Performance Analysis of Cluster Formation in Wireless Sensor Networks. Sensors 2017, 17, 2902.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top