Enhancing Data Collection in Vehicular Network Through Clustering Optimization
Résumé
In this paper, we present a novel approach to enhance data collection in
Vehicular Ad-Hoc NETworks (VANETs). VANETs are a growing area of
interest due to their unique characteristics and challenges, such as
rapidly changing topology and frequent network disruptions. Efficient
data collection is a critical issue in vehicular networks and has
therefore become a focus of research. To address this challenge, we
propose a stable clustering optimization solution based on adaptive
multiple metrics. The cluster head selection is done based on both
mobility metrics, such as position and relative speed, and Quality of
Service (QoS) metrics, such as neighborhood degree and link quality. The
proposed solution has been tested and evaluated through simulations
using a vehicular mobility simulator in a realistic urban environment.
The results show that the proposed approach provides more stable
clusters with higher QoS, and allows for the selection of the
appropriate cluster head to collect data from the vehicles and forward
it to the destination.
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