  Green Vehicular Ad-hoc Network (VANET) is a newly-emerged research area which
focuses on reducing harmful impacts of vehicular communication equipments on
the natural environment. Recent studies have shown that grouping vehicles into
clusters for green communications in VANETs can significantly improve
networking efficiency and reduce infrastructure costs. As a dynamic network
system, maintaining the network connectivity and reducing the communication
overlap are two critical challenges for green VANET clustering. However, most
existing work studies connectivity and overlap separately, lacking a deep
understanding of the relationship between them. To address this issue, we
present a comprehensive analysis that jointly considers the two critical
factors in one model. Specifically, we first design a state resemblance
prediction (SRP) model based on the historical trajectory feature relevance
between vehicles; Combined with the SRP model, we propose the region-based
collaborative management scheme (RCMS) to establish the dynamic clustering;
Lastly, we take extensive experiments to verify the region-based collaborative
management scheme for dynamic clustering. The results demonstrate that the
proposed clustering algorithm can achieve high networking efficiency and better
communication stability.
