选择性转发攻击是对无线传感网络(wireless sensor networks,WSNs)最危险的攻击,特别是在移动环境的WSNs下。为此,针对基于IPv6的移动WSNs,对选择性转发攻击进行研究,并提出基于序贯概率比测试(sequential probability ratio test,SPRT...选择性转发攻击是对无线传感网络(wireless sensor networks,WSNs)最危险的攻击,特别是在移动环境的WSNs下。为此,针对基于IPv6的移动WSNs,对选择性转发攻击进行研究,并提出基于序贯概率比测试(sequential probability ratio test,SPRT)的检测算法(SPRT-DA),该算法通过计算接受与丢失的数据包数识别恶意节点,并采用自适应的阈值机制排除恶意节点。实验数据表明,提出的SPRT-DA算法的检测率逼近100%。展开更多
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural...Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.展开更多
文摘选择性转发攻击是对无线传感网络(wireless sensor networks,WSNs)最危险的攻击,特别是在移动环境的WSNs下。为此,针对基于IPv6的移动WSNs,对选择性转发攻击进行研究,并提出基于序贯概率比测试(sequential probability ratio test,SPRT)的检测算法(SPRT-DA),该算法通过计算接受与丢失的数据包数识别恶意节点,并采用自适应的阈值机制排除恶意节点。实验数据表明,提出的SPRT-DA算法的检测率逼近100%。
基金Project(61201028)supported by the National Natural Science Foundation of ChinaProject(YWF-12-JFGF-060)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2011ZD51048)supported by Aviation Science Foundation of China
文摘Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.
