工业物联网(Industrial Internet of Things,IIoT)中通信故障具有突发性与结构依赖性,提升节点异常状态的预测精度对保障IIoT系统的稳定运行具有重要意义。文章构建了基于图神经网络(Graph Neural Network,GNN)的通信故障预测模型,提出...工业物联网(Industrial Internet of Things,IIoT)中通信故障具有突发性与结构依赖性,提升节点异常状态的预测精度对保障IIoT系统的稳定运行具有重要意义。文章构建了基于图神经网络(Graph Neural Network,GNN)的通信故障预测模型,提出了通信图建模方法。该方法设计了多层图卷积结构与注意力机制,构建了多类别故障标签并实现动态图序列预测。实验结果表明,该方法在预测准确率、故障识别能力与推理效率方面优于传统模型,可以有效感知节点间拓扑与状态演化关系,在工业通信网络中具备部署价值与应用前景。展开更多
针对区块链边缘节点的部署环境开放、安全措施薄弱、易受到安全攻击,以及计算和网络资源不足等问题,提出一种基于可信执行环境(TEE)的区块链安全架构P-Dledger。该架构通过构建两阶段的信任链,在满足软件便捷迭代的基础上,确保加载部件...针对区块链边缘节点的部署环境开放、安全措施薄弱、易受到安全攻击,以及计算和网络资源不足等问题,提出一种基于可信执行环境(TEE)的区块链安全架构P-Dledger。该架构通过构建两阶段的信任链,在满足软件便捷迭代的基础上,确保加载部件的可信;通过实现智能合约可信执行框架以及基于串行外设接口或非门存储器(SPI NOR Flash)的数据可信存储,保证智能合约的可信计算与数据的可信存储;同时,为共识提案赋予单调递增的唯一标识,限制拜占庭节点的行为。实验与分析结果表明:所提架构确保了加载主体、账本数据与执行过程的安全可信;当网络延时大于60 ms或节点数大于8时,P-Dledger比采用拜占庭容错(PBFT)算法的区块链系统的吞吐量更高,且随着网络延时与节点数的增加,P-Dledger性能表现更稳定。展开更多
随着通信业务连续性要求逐步提升,不间断电源(Uninterruptible Power Supply,UPS)系统在电源故障保障中发挥着愈发重要的作用。围绕通信场景下UPS系统的运行特性展开分析,系统梳理电压波动、节点阻抗、冲击负载及环流干扰等常见故障影...随着通信业务连续性要求逐步提升,不间断电源(Uninterruptible Power Supply,UPS)系统在电源故障保障中发挥着愈发重要的作用。围绕通信场景下UPS系统的运行特性展开分析,系统梳理电压波动、节点阻抗、冲击负载及环流干扰等常见故障影响因素,并制定输出幅度动态范围限定、接触节点阻抗波动抑制、功率变动瞬时曲线缓释、地线通道干涉因子衰减4项应对策略,明确各类参数扰动条件下UPS系统的动态响应机制与供电稳定性控制路径,形成面向通信负载特性的故障抑制体系。展开更多
The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important...The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.展开更多
Wireless sensor networks are often used to monitor physical and environmental conditions in various regions where human access is limited. Due to limited resources and deployment in hostile environment, they are vulne...Wireless sensor networks are often used to monitor physical and environmental conditions in various regions where human access is limited. Due to limited resources and deployment in hostile environment, they are vulnerable to faults and malicious attacks. The sensor nodes affected or compromised can send erroneous data or misleading reports to base station. Hence identifying malicious and faulty nodes in an accurate and timely manner is important to provide reliable functioning of the networks. In this paper, we present a malicious and malfunctioning node detection scheme using dual-weighted trust evaluation in a hierarchical sensor network. Malicious nodes are effectively detected in the presence of natural faults and noise without sacrificing fault-free nodes. Simulation results show that the proposed scheme outperforms some existing schemes in terms of mis-detection rate and event detection accuracy, while maintaining comparable performance in malicious node detection rate and false alarm rate.展开更多
On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average ...On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.展开更多
The fast growth of datacenter networks,in terms of both scale and structural complexity,has led to an increase of network failure and hence brings new challenges to network management systems.As network failure such a...The fast growth of datacenter networks,in terms of both scale and structural complexity,has led to an increase of network failure and hence brings new challenges to network management systems.As network failure such as node failure is inevitable,how to find fault detection and diagnosis approaches that can effectively restore the network communication function and reduce the loss due to failure has been recognized as an important research problem in both academia and industry.This research focuses on exploring issues of node failure,and presents a proactive fault diagnosis algorithm called heuristic breadth-first detection(HBFD),through dynamically searching the spanning tree,analyzing the dial-test data and choosing a reasonable threshold to locate fault nodes.Both theoretical analysis and simulation results demonstrate that HBFD can diagnose node failures effectively,and take a smaller number of detection and a lower false rate without sacrificing accuracy.展开更多
文摘工业物联网(Industrial Internet of Things,IIoT)中通信故障具有突发性与结构依赖性,提升节点异常状态的预测精度对保障IIoT系统的稳定运行具有重要意义。文章构建了基于图神经网络(Graph Neural Network,GNN)的通信故障预测模型,提出了通信图建模方法。该方法设计了多层图卷积结构与注意力机制,构建了多类别故障标签并实现动态图序列预测。实验结果表明,该方法在预测准确率、故障识别能力与推理效率方面优于传统模型,可以有效感知节点间拓扑与状态演化关系,在工业通信网络中具备部署价值与应用前景。
文摘针对区块链边缘节点的部署环境开放、安全措施薄弱、易受到安全攻击,以及计算和网络资源不足等问题,提出一种基于可信执行环境(TEE)的区块链安全架构P-Dledger。该架构通过构建两阶段的信任链,在满足软件便捷迭代的基础上,确保加载部件的可信;通过实现智能合约可信执行框架以及基于串行外设接口或非门存储器(SPI NOR Flash)的数据可信存储,保证智能合约的可信计算与数据的可信存储;同时,为共识提案赋予单调递增的唯一标识,限制拜占庭节点的行为。实验与分析结果表明:所提架构确保了加载主体、账本数据与执行过程的安全可信;当网络延时大于60 ms或节点数大于8时,P-Dledger比采用拜占庭容错(PBFT)算法的区块链系统的吞吐量更高,且随着网络延时与节点数的增加,P-Dledger性能表现更稳定。
文摘随着通信业务连续性要求逐步提升,不间断电源(Uninterruptible Power Supply,UPS)系统在电源故障保障中发挥着愈发重要的作用。围绕通信场景下UPS系统的运行特性展开分析,系统梳理电压波动、节点阻抗、冲击负载及环流干扰等常见故障影响因素,并制定输出幅度动态范围限定、接触节点阻抗波动抑制、功率变动瞬时曲线缓释、地线通道干涉因子衰减4项应对策略,明确各类参数扰动条件下UPS系统的动态响应机制与供电稳定性控制路径,形成面向通信负载特性的故障抑制体系。
文摘The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.
文摘Wireless sensor networks are often used to monitor physical and environmental conditions in various regions where human access is limited. Due to limited resources and deployment in hostile environment, they are vulnerable to faults and malicious attacks. The sensor nodes affected or compromised can send erroneous data or misleading reports to base station. Hence identifying malicious and faulty nodes in an accurate and timely manner is important to provide reliable functioning of the networks. In this paper, we present a malicious and malfunctioning node detection scheme using dual-weighted trust evaluation in a hierarchical sensor network. Malicious nodes are effectively detected in the presence of natural faults and noise without sacrificing fault-free nodes. Simulation results show that the proposed scheme outperforms some existing schemes in terms of mis-detection rate and event detection accuracy, while maintaining comparable performance in malicious node detection rate and false alarm rate.
基金Supported by the National High Technology Research and Development Program of China(No.2008AA01A201)the National Natural Science Foundation of China(No.60503015)
文摘On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.
基金supported by the National Natural Science Foundation of China(61877067 61572435)+3 种基金the joint fund project of the Ministry of Education–the China Mobile(MCM20170103)Xi’an Science and Technology Innovation Project(201805029YD7CG13-6)Ningbo Natural Science Foundation(2016A610035 2017A610119)
文摘The fast growth of datacenter networks,in terms of both scale and structural complexity,has led to an increase of network failure and hence brings new challenges to network management systems.As network failure such as node failure is inevitable,how to find fault detection and diagnosis approaches that can effectively restore the network communication function and reduce the loss due to failure has been recognized as an important research problem in both academia and industry.This research focuses on exploring issues of node failure,and presents a proactive fault diagnosis algorithm called heuristic breadth-first detection(HBFD),through dynamically searching the spanning tree,analyzing the dial-test data and choosing a reasonable threshold to locate fault nodes.Both theoretical analysis and simulation results demonstrate that HBFD can diagnose node failures effectively,and take a smaller number of detection and a lower false rate without sacrificing accuracy.
