Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
Fecha
2024-08-27
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Editor
MDPI, Basel, Switzerland.
Resumen
Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes
powered by batteries that need efficient resource management to achieve the WSN’s goals. One of
the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters
around a cluster head (CH), which must be chosen carefully. In this article, a new centralized
clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability
of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs
three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from
the beginning to the instant when the first node depletes its battery, the second KB is then applied
from that moment to the instant when half of the nodes are dead, and the last KB is loaded from
that point until the last node runs out of power. These three KBs are obtained from the original KB
designed by the authors after an optimization process. It is based on a particle swarm optimization
algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the
KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used
to obtain better results in complex systems where the number of variables or rules could make them
unaffordable. The results of the presented optimized approach significantly improved upon those
from other authors with similar methods. Finally, the paper presents an analysis of why some rule
weights change more than others, in order to design more suitable controllers in the future.
Descripción
Palabras clave
wireless sensor network, fuzzy logic, clustering
Citación
Muñoz-Exposito, Jose-Enrique, Antonio-Jesus Yuste-Delgado, Alicia Triviño-Cabrera, and Juan-Carlos Cuevas-Martinez. 2024. "Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks" Sensors 24, no. 17: 5548. https://doi.org/10.3390/s24175548