A Region-based Collaborative Management Scheme for Dynamic Clustering in the Green VANET PROJECT TITLE : A Region-based Collaborative Management Scheme for Dynamic Clustering in Green VANET ABSTRACT: Green Vehicular Ad-hoc Network (VANET) is a newly-emerging research field with the primary objective of lessening the negative effects that vehicular Communication devices have on their surrounding natural environment. Recent research has shown that the NetWorking efficiency of VANETs can be significantly improved by grouping vehicles into clusters for green Communications. This can also significantly reduce the costs associated with infrastructure. Green VANET clustering faces two significant challenges due to the fact that it is a dynamic network system: preserving the network connectivity and cutting down on the amount of Communication overlap. However, the majority of the existing research studies connectivity and overlap independently, which prevents a comprehensive comprehension of the relationship between the two concepts. In order to provide a solution to this problem, we have provided a comprehensive analysis that takes into account the two important factors in a single model. To be more specific, we first design a state resemblance prediction model (also known as SRP), which is based on the historical trajectory feature relevance between vehicles; In conjunction with the SRP model, we propose the region-based collaborative management scheme (RCMS) to establish the dynamic clustering; Finally, we take extensive experiments to verify the region-based collaborative management scheme for dynamic clustering. [Citation needed] The findings indicate that the clustering algorithm that was proposed is capable of achieving high NetWorking efficiency as well as improved Communication stability. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest VANET/V2X Routing From the Viewpoint of Non-Learning- and Learning-Based Approaches: A Survey Machine Learning for Misbehavior Detection of Position Falsification Attacks in VANETs