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A New Affinity Propagation Clustering Algorithm for V2V-Supported VANETs
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DOI：10.1109/ACCESS.2020.2987968.png)
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Clustering is an efficient method for improving the communication performance of Vehicular Ad hoc NETworks (VANETs) that adopt Vehicle to Vehicle (V2V) communications. However, how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem. In this paper, we first reconstruct the similarity function of the Affinity Propagation (AP) clustering algorithm by introducing communication-related parameters, so the vehicles with low relative mobility and good communication performance can easily be selected as cluster heads. Then, by formally defining three scaling functions, a weighted mechanism is designed to quantitatively assess the effect on the cluster stability when a vehicle joins it. Base on them, from the perspective of global balance, a new AP clustering algorithm for the whole clustering process is proposed. To ensure the validity of simulations, we use the vehicular mobility data generated on the realistic map of Cologne, Germany, and perform a series of simulations for eleven metrics commonly adopted in similar works. The results show that our proposed algorithm performs better than other algorithms in terms of the cluster stability, and it also effectively improves throughput and reduces packet loss rate of VANETs over the classical APROVE algorithm and the NMDP-APC algorithm.
Keyword:
Clustering algorithms
Stability analysis
Vehicular ad hoc networks
Measurement
Roads
Mobile computing
VANETs
V2V
clustering
affinity propagation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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Clustering in vehicular ad hoc networks: Taxonomy, challenges and solutions车载自组织网络中的聚类: 分类，挑战和解决方案
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