There has been significant recent research on secure control problems that arise from the open and complex realworld industrial environments.This paper focuses on addressing the issue of secure consensus control in mu...There has been significant recent research on secure control problems that arise from the open and complex realworld industrial environments.This paper focuses on addressing the issue of secure consensus control in multi-agent systems(MASs)under malicious attacks,utilizing the practical Byzantine fault tolerance(PBFT)and Raft consensus algorithm in blockchain.Unlike existing secure consensus control algorithms that have strict requirements for topology and high communication costs,our approach introduces a node grouping methodology based on system topology.Additionally,we utilize the PBFT consensus algorithm for intergroup leader identity verification,effectively reducing the communication complexity of PBFT in large-scale networks.Furthermore,we enhance the Raft algorithm through cryptographic validation during followers’log replication,which enhances the security of the system.Our proposed consensus process not only identifies the identities of malicious agents but also ensures consensus among normal agents.Through extensive simulations,we demonstrate robust convergence,particularly in scenarios with the relaxed topological requirements.Comparative experiments also validate the algorithm’s lower consensus latency and improved efficiency compared to direct PBFT utilization for identity verification and classical secure consensus control method mean subsequence reduced(MSR)algorithm.展开更多
针对物联网场景下实用拜占庭容错(Practical Byzantine Fault Tolerance,PBFT)算法在点数量大时协商效率低、主节点恶意行为导致协商失败等问题,提出一种基于节点评估模型的多层PBFT(Multi-layer PBFT,M-PBFT)算法。构建结合层次分析法(...针对物联网场景下实用拜占庭容错(Practical Byzantine Fault Tolerance,PBFT)算法在点数量大时协商效率低、主节点恶意行为导致协商失败等问题,提出一种基于节点评估模型的多层PBFT(Multi-layer PBFT,M-PBFT)算法。构建结合层次分析法(Analytic Hierarchy Process,AHP)、逼近理想解排序(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)法与波达(Borda)的节点评价模型,该模型以节点行为为评价指标,根据节点偏好获得综合得分;根据节点总数与动态分组策略,将共识过程分为多层结构,次一层组内主节点担任高一层的成员节点。设置节点模型优化策略与异常检测机制,针对不同场景调整模型,在节点出现异常时及时处理。在每层PBFT共识后,依据已达成共识的节点表现反馈至评估模型,对节点进行再次评估,层层共识递进,最终完成全局共识。实验结果表明,M-PBFT算法提高了节点的可扩展性和容错性,在物联网各类大节点数场景下显著降低了通信复杂度与视图切换频率;还验证了该算法在拜占庭节点占比达总数20%的情况下,仍能达到98.2%的共识成功率。因此,该算法可高效应用于各类场景的联盟区块链系统。展开更多
基金supported by the Fundamental Research Funds for the Central Universities(NS2024021)the Science and Technology Development Fund of Macao SAR(0145/2023/RIA3,0093/2023/RIA2,0050/2020/A1)the National Natural Science Foundation of China(62103411).
文摘There has been significant recent research on secure control problems that arise from the open and complex realworld industrial environments.This paper focuses on addressing the issue of secure consensus control in multi-agent systems(MASs)under malicious attacks,utilizing the practical Byzantine fault tolerance(PBFT)and Raft consensus algorithm in blockchain.Unlike existing secure consensus control algorithms that have strict requirements for topology and high communication costs,our approach introduces a node grouping methodology based on system topology.Additionally,we utilize the PBFT consensus algorithm for intergroup leader identity verification,effectively reducing the communication complexity of PBFT in large-scale networks.Furthermore,we enhance the Raft algorithm through cryptographic validation during followers’log replication,which enhances the security of the system.Our proposed consensus process not only identifies the identities of malicious agents but also ensures consensus among normal agents.Through extensive simulations,we demonstrate robust convergence,particularly in scenarios with the relaxed topological requirements.Comparative experiments also validate the algorithm’s lower consensus latency and improved efficiency compared to direct PBFT utilization for identity verification and classical secure consensus control method mean subsequence reduced(MSR)algorithm.
文摘针对物联网场景下实用拜占庭容错(Practical Byzantine Fault Tolerance,PBFT)算法在点数量大时协商效率低、主节点恶意行为导致协商失败等问题,提出一种基于节点评估模型的多层PBFT(Multi-layer PBFT,M-PBFT)算法。构建结合层次分析法(Analytic Hierarchy Process,AHP)、逼近理想解排序(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)法与波达(Borda)的节点评价模型,该模型以节点行为为评价指标,根据节点偏好获得综合得分;根据节点总数与动态分组策略,将共识过程分为多层结构,次一层组内主节点担任高一层的成员节点。设置节点模型优化策略与异常检测机制,针对不同场景调整模型,在节点出现异常时及时处理。在每层PBFT共识后,依据已达成共识的节点表现反馈至评估模型,对节点进行再次评估,层层共识递进,最终完成全局共识。实验结果表明,M-PBFT算法提高了节点的可扩展性和容错性,在物联网各类大节点数场景下显著降低了通信复杂度与视图切换频率;还验证了该算法在拜占庭节点占比达总数20%的情况下,仍能达到98.2%的共识成功率。因此,该算法可高效应用于各类场景的联盟区块链系统。
