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
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
A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associat...A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.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
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 PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to ...The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to support massive network nodes, the common participation over all nodes in the consensus mechanism would lead to increased communication complexity, and the arbitrary selection of master nodes would also lead to inefficient consensus. This paper offered a PBFT consensus method (Role Division-based Practical Byzantine Fault Tolerance, RD-PBFT) to address the above problems based on node role division. First, the nodes in the system voted with each other to divide the high reputation group and low reputation group, and determined the starting reputation value of the nodes. Then, the mobile node in the group was divided into roles according to the high reputation value, and a total of three roles were divided into consensus node, backup node, and supervisory node to reduce the number of nodes involved in the consensus process and reduced the complexity of communication. In addition, an adaptive method was used to select the master nodes in the consensus process, and an integer value was introduced to ensure the unpredictability and equality of the master node selection. Experimentally, it was verified that the algorithm has lower communication complexity and better decentralization characteristics compared with the PBFT consensus algorithm, which improved the efficiency of consensus.展开更多
In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid for...In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.展开更多
In this paper, it is proved that the direction of the node-voltage difference vector, which is the difference between the node-voltage vector at faulty state and the one at the nominal state, is determined only by the...In this paper, it is proved that the direction of the node-voltage difference vector, which is the difference between the node-voltage vector at faulty state and the one at the nominal state, is determined only by the location of the faulty clement in linear analog circuits. Considering that the direction of the node-voltage sensitivity vector is the same as the one of the node-voltage difference vector and also considering that the module of the node-voltage sensitivity vector presents the weight of the parameter of faulty element deviation relative to the voltage difference, fault dictionary is set up based on node-voltage sensitivity vectors. A decision algorithm is proposed concerned with both the location and the parameter difference of the faulty element. Single fault and multi-fault can be diagnosed while the circuit parameters deviate within the tolerance range of 10 %.展开更多
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
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
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.
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A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.
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.
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.
文摘The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to support massive network nodes, the common participation over all nodes in the consensus mechanism would lead to increased communication complexity, and the arbitrary selection of master nodes would also lead to inefficient consensus. This paper offered a PBFT consensus method (Role Division-based Practical Byzantine Fault Tolerance, RD-PBFT) to address the above problems based on node role division. First, the nodes in the system voted with each other to divide the high reputation group and low reputation group, and determined the starting reputation value of the nodes. Then, the mobile node in the group was divided into roles according to the high reputation value, and a total of three roles were divided into consensus node, backup node, and supervisory node to reduce the number of nodes involved in the consensus process and reduced the complexity of communication. In addition, an adaptive method was used to select the master nodes in the consensus process, and an integer value was introduced to ensure the unpredictability and equality of the master node selection. Experimentally, it was verified that the algorithm has lower communication complexity and better decentralization characteristics compared with the PBFT consensus algorithm, which improved the efficiency of consensus.
文摘In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.
基金supported by Program for New Century Excellent Talents in University under Grant No.NCET-05-0804
文摘In this paper, it is proved that the direction of the node-voltage difference vector, which is the difference between the node-voltage vector at faulty state and the one at the nominal state, is determined only by the location of the faulty clement in linear analog circuits. Considering that the direction of the node-voltage sensitivity vector is the same as the one of the node-voltage difference vector and also considering that the module of the node-voltage sensitivity vector presents the weight of the parameter of faulty element deviation relative to the voltage difference, fault dictionary is set up based on node-voltage sensitivity vectors. A decision algorithm is proposed concerned with both the location and the parameter difference of the faulty element. Single fault and multi-fault can be diagnosed while the circuit parameters deviate within the tolerance range of 10 %.
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。
