TY - JOUR
T1 - Performance analysis of cluster formation in wireless sensor networks
AU - Montiel, Edgar Romo
AU - Rivero-Angeles, Mario E.
AU - Rubino, Gerardo
AU - Molina-Lozano, Heron
AU - Menchaca-Mendez, Rolando
AU - Menchaca-Mendez, Ricardo
N1 - Publisher Copyright:
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - 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.
AB - 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.
KW - Clustering
KW - Fuzzy C-means
KW - K-medoids
KW - Transmission probability
UR - http://www.scopus.com/inward/record.url?scp=85038207444&partnerID=8YFLogxK
U2 - 10.3390/s17122902
DO - 10.3390/s17122902
M3 - Artículo
C2 - 29236065
SN - 1424-8220
VL - 17
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 12
M1 - 2902
ER -