# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 9 Conclusions  
In this paper, we propose a novel consensus algorithm, AE-PBFT, designed for application in the IoV system. The algorithm incorporates two core mechanisms. The first is the trust management model based on a gradual acceleration mechanism. This model adjusts the weight of credit changes for each node based on its continuous historical behavior, enabling the system to quickly distinguish malicious nodes from normal ones. By doing so, it significantly improves the efficiency of node screening and consensus completion. The second mechanism is the dynamic consensus group division model that adapts to environmental changes. This mechanism enhances the traditional consensus group approach by incorporating an environmental adaptive adjustment mechanism based on the overall credit mean of nodes. During operation, the consensus group ratio is dynamically adjusted according to the overall node credit distribution, enabling the system to better handle extreme scenarios and enhance reliability and fault tolerance. Experimental results demonstrate that AE-PBFT outperforms state-of-the-art consensus algorithms, including PBFT, GPBFT, and CBFT, in terms of consensus delay, throughput, communication overhead, and fault tolerance. Its advantages are particularly evident in extreme environments where malicious nodes constitute the majority. Furthermore, we propose a vehicle-service provider-RSU IoV system architecture based on the AE-PBFT algorithm. This architecture features a three-layer structure comprising a CA authentication layer, an edge interaction layer, and a data storage layer. The identity information of entities within the system is managed through registration and authentication, ensuring stable and secure system operation.  
Future work will focus on optimizing the gradual acceleration mechanism in the existing credit model to achieve more refined tiered effects. Additionally, efforts will be directed toward improving the consensus group ratio algorithm to further enhance the environmental adaptability of the dynamic consensus group. Beyond IoV, the proposed framework has the potential for broader applications in fields, such as supply chain management, healthcare, and digital copyright protection, offering secure and efficient services across a wide range of industries.  
Conceptualization, X.S. and R.M.; methodology, X.S. and R.M.; software, X.S. and R.M.; validation, X.S.; formal analysis, X.S.; investigation, X.S. and R.M.; resources, X.S. and R.M.; data curation, X.S. and R.M.; writing--original draft preparation, R.M.; writing--review and editing, R.M.; visualization, X.S. and R.M.; supervision, X.S.; project administration, X.S.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62173171.  
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The authors would like to thank the editors and the reviewers for their valuable time and constructive comments.  
The authors declare no conflicts of interest.