【Author】
Deng, Xiaohong; Li, Kangting; Wang, Zhiqiang; Liu, Huiwen
【Source】SECURITY AND COMMUNICATION NETWORKS
【Abstract】Currently, because of the excellent properties of decentralization, hard tamperability, and traceability, blockchain is widely used in WSN and IoT applications. In particular, consortium blockchain plays a fundamental role in the practical application environment, but consensus algorithm is always a key constraint. Over the past decade, we have been witnessing the obvious growth in blockchain consensus algorithms. However, in the existing consortium blockchain consensus algorithms, there is a limited characteristic of scalability, concurrency, and security. To address this problem, this work introduces a new consensus algorithm that is derived from a directed acyclic graph and backpropagation neural network. First, we propose a partitioned structure and segmented directed acyclic graph as data storage structure, which allows us to improve scalability, throughput, and fine-grained granularity of transaction data. Furthermore, in order to provide the accuracy of node credit evaluation and reduce the possibility of Byzantine nodes, we introduce a novel credit evaluation mechanism based on a backpropagation neural network. Finally, we design a resistant double-spending mechanism based on MapReduce, which ensures the transaction data are globally unique and ordered. Experimental results and security analysis demonstrate that the proposed algorithm has advantages in throughput. Compared with the existing methods, it has higher security and scalability.
【标题】一种基于分段DAG和BP神经网络的联盟区块链共识算法
【摘要】目前，由于区块链具有去中心化、不可篡改、可追溯等优良特性，在无线传感器网络和物联网应用中得到广泛应用。尤其是联盟链在实际应用环境中发挥着基础性作用，但共识算法始终是关键约束。在过去的十年中，我们见证了区块链共识算法的明显增长。然而，现有的联盟链共识算法在可扩展性、并发性和安全性方面存在一定的局限性。为了解决这个问题，这项工作引入了一种新的共识算法，该算法源自有向无环图和反向传播神经网络。首先，我们提出了一种分区结构和分段有向无环图作为数据存储结构，这使我们能够提高交易数据的可扩展性、吞吐量和细粒度。此外，为了提供节点信用评估的准确性并降低拜占庭节点的可能性，我们引入了一种基于反向传播神经网络的新型信用评估机制。最后，我们设计了一种基于 MapReduce 的抗双花机制，确保交易数据全局唯一且有序。实验结果和安全性分析表明，该算法在吞吐量方面具有优势。与现有方法相比，具有更高的安全性和可扩展性。
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