In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.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
To reduce excessive computing and communication loads of traditional fault detection methods,a neighbor-data analysis based node fault detection method is proposed.First,historical data is analyzed to confirm the conf...To reduce excessive computing and communication loads of traditional fault detection methods,a neighbor-data analysis based node fault detection method is proposed.First,historical data is analyzed to confirm the confidence level of sensor nodes.Then a node's reading data is compared with neighbor nodes' which are of good confidence level.Decision can be made whether this node is a failure or not.Simulation shows this method has good effect on fault detection accuracy and transient fault tolerance,and never transfers communication and computing overloading to sensor nodes.展开更多
为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键...为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键节点.首先,借助节点间的最短路径长度和数量,定义节点间的通信概率序列.其次,通过结合通信概率和相对熵(Communication Probability and Relative Entropy,CPR),将传统的二元邻接矩阵转化为网络归一化相关矩阵.再次,结合加权聚类系数和邻居节点的影响(Weighted Clustering Coefficients and Neighbor Influence,WCCN),得到改进的考虑邻居影响的局部加权密度.最后,为验证CPRWCCN算法的效果,在故意攻击和随机攻击下进行模拟实验,利用传播模型在4种实际网络上对CPR-WCCN与其他5种算法进行对比分析.实验结果表明:当网络遭受故意攻击,导致前15个关键节点失效时,网络的连通性、效率、最大连接子图以及自然连通性等关键指标较随机攻击出现了更显著的下降;相较于其他5种算法,CPR-WCCN算法表现出最优的整体性能,能够准确且高效地识别出网络中的关键节点.展开更多
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
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.
基金supported by the National Basic Research Program of China(2007CB310703)the High Technical Research and Development Program of China(2008AA01Z201)+1 种基金the National Natural Science Foundlation of China(60821001,60802035,60973108)Chinese Universities Science Fund(BUPT2009RC0504)
文摘To reduce excessive computing and communication loads of traditional fault detection methods,a neighbor-data analysis based node fault detection method is proposed.First,historical data is analyzed to confirm the confidence level of sensor nodes.Then a node's reading data is compared with neighbor nodes' which are of good confidence level.Decision can be made whether this node is a failure or not.Simulation shows this method has good effect on fault detection accuracy and transient fault tolerance,and never transfers communication and computing overloading to sensor nodes.
文摘为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键节点.首先,借助节点间的最短路径长度和数量,定义节点间的通信概率序列.其次,通过结合通信概率和相对熵(Communication Probability and Relative Entropy,CPR),将传统的二元邻接矩阵转化为网络归一化相关矩阵.再次,结合加权聚类系数和邻居节点的影响(Weighted Clustering Coefficients and Neighbor Influence,WCCN),得到改进的考虑邻居影响的局部加权密度.最后,为验证CPRWCCN算法的效果,在故意攻击和随机攻击下进行模拟实验,利用传播模型在4种实际网络上对CPR-WCCN与其他5种算法进行对比分析.实验结果表明:当网络遭受故意攻击,导致前15个关键节点失效时,网络的连通性、效率、最大连接子图以及自然连通性等关键指标较随机攻击出现了更显著的下降;相较于其他5种算法,CPR-WCCN算法表现出最优的整体性能,能够准确且高效地识别出网络中的关键节点.
