Performance analysis of cluster formation in wireless sensor networks

Edgar Romo Montiel, Mario E. Rivero-Angeles, Gerardo Rubino, Heron Molina-Lozano, Rolando Menchaca-Mendez, Ricardo Menchaca-Mendez

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. 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-basedWireless 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.
Original languageAmerican English
JournalSensors (Switzerland)
DOIs
StatePublished - 13 Dec 2017

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Cluster Analysis
Wireless sensor networks
sensors
energy consumption
Energy utilization
Head
intelligence
Switzerland
Intelligence
Sensor nodes
Sensor networks

Cite this

@article{201668d0148e4e24a58e069bfbfab0f5,
title = "Performance analysis of cluster formation in wireless sensor networks",
abstract = "{\circledC} 2017 by the authors. Licensee MDPI, Basel, Switzerland. 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-basedWireless 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.",
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year = "2017",
month = "12",
day = "13",
doi = "10.3390/s17122902",
language = "American English",
journal = "Sensors (Switzerland)",
issn = "1424-8220",
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}

Performance analysis of cluster formation in wireless sensor networks. / Montiel, Edgar Romo; Rivero-Angeles, Mario E.; Rubino, Gerardo; Molina-Lozano, Heron; Menchaca-Mendez, Rolando; Menchaca-Mendez, Ricardo.

In: Sensors (Switzerland), 13.12.2017.

Research output: Contribution to journalArticle

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AU - Rubino, Gerardo

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AU - Menchaca-Mendez, Ricardo

PY - 2017/12/13

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N2 - © 2017 by the authors. Licensee MDPI, Basel, Switzerland. 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-basedWireless 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.

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