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摘要:
高效安全的数据共享对于智能车联网的深度应用至关重要，在相互不信任的车辆之间实现可信的数据共享成为当前研究的热点. 区块链技术以其防篡改、可追溯等特点，成为支撑智能车联网数据共享流通的主要途径之一. 现有基于区块链的车联网数据共享方案，存在吞吐量小、安全性低等不足. 引入区块链分片方法，提出基于机器学习的分片算法，将地理位置相近的路侧单元（road side unit，RSU）划分到同一分片，并迭代单个分片的数据共享最优负载，降低了片内通信延迟进而提高了吞吐量，平衡了不同分片之间的数据共享负载. 为避免单个分片的贿赂攻击，提出了基于声誉的片内共识协议与监督人机制. 选举具有高声誉的RSU参与片内共识过程，并动态计算RSU的最新声誉. 设定声誉度高的RSU担任监督员，监督员可定期对不同分片产生的区块进行合法性验证. 通过性能评估和安全性分析，证明方案有助于提升智能车联网数据共享的高效性和安全性.
Abstract:Efficient and secure data sharing is crucial for the profound application of the intelligent Internet of vehicles, where achieving trusted data sharing between mutually untrusting vehicles has become a major focus of current research. With the characteristics of tamper resistance and traceability, blockchain has emerged as a primary approach to support data circulation in the intelligent Internet of vehicles. Existing blockchain-based data-sharing solutions for the Internet of vehicles suffer from low throughput and security vulnerabilities. In this paper, the blockchain sharding approach is introduced, where a machine learning-based sharding algorithm is utilized to partition road side unit (RSU) with geographical proximity into the same shard and iteratively optimize data-sharing loads within individual shards. This algorithm reduces intra-shard communication latency and subsequently improves throughput while balancing the data-sharing loads among different shards. To prevent bribery attacks within a single shard, a reputation-based intra-shard consensus protocol and the supervisor mechanism are proposed. The proposed protocol involves the election of RSUs with high reputation to participate in the intra-shard consensus process and dynamically calculates the latest reputation of RSUs. High-reputation RSUs are designated supervisors and regularly verify the legitimacy of blocks generated in different shards. Performance evaluation and security analysis demonstrate the scheme enhances the efficiency and security of data sharing in the intelligent Internet of vehicles.
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图 2 基于声誉的片内共识协议流程图
注：各个节点更新自身当前轮次声誉，并执行片内共识节点选举函数.
Figure 2. Flow chart of reputation-based intra-shard consensus protocol
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图 4 4种算法的簇间负载方差对比
Figure 4. Comparison of load variance between clusters of four algorithms
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图 5 4种算法的聚类距离成本对比
Figure 5. Comparison of clustering distance costs of four algorithms
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图 6 3种算法的聚类距离成本对比
Figure 6. Comparison of clustering distance costs of three algorithms
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图 7 负载呈均匀分布与指数分布的差异
Figure 7. Difference between balanced load distribution and exponential distribution
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图 8 负载呈指数分布下的聚类簇间方差
Figure 8. Inter-cluster variance of clusters under exponential load distribution
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图 9 负载呈指数分布下的聚类距离成本
Figure 9. Clustering distance cost under exponential load distribution
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图 11 不同区块大小对协议性能的影响
Figure 11. Impact of different block sizes on protocol performance
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图 12 RSU数量对协议吞吐量的影响
Figure 12. Impact of the number of RSU on the throughput of protocol
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图 14 分片数量对协议吞吐量的影响
Figure 14. Impact of the number of shards on the throughput of protocol
表 1 云服务器配置参数
Table 1 Elastic Compute Service Configuration Parameters
参数 配置环境 实例规格 ecs.c7a.8xlarge 处理器核心数 32 内存/GB 256 本地存储/GB 100 处理器主频/GHz 3.5 内网带宽/GBps 25 内网收发包/PPS 1200 万处理器型号 AMD EPYC™ Milan 7T83 镜像操作系统 Ubuntu18.04 64位 
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