Resumen |
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. |