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
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
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.
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.
