Recently, in IoT, wireless sensor network has become a critical technology with which many applications in industries and human life can achieve smart IoT control. However, for those daily applications using WSN technology, malicious users can capture the sensor nodes much easily since these wireless sensor nodes are usually deployed in easily touched places. Once this node captured attack occurs, wireless sensor network soon faces various security risks. In this paper, in order to resist node captured attack, we propose a novel authentication information exchange scheme in WSN, which is very different from the previous authentication researches. Our idea is to add an authentication information exchange scheme in previous authentication scheme but not propose new one. We develop this scheme based on the idea of the association scheme of Home GWN and local sensor nodes. In this study, HGWN should contact all local sensor nodes and meanwhile is responsible for performing an authentication information exchange scheme for resisting security risk. In order to prevent the attacker from guessing communication period between HGWN and the sensor, we also design a dynamic contacting mechanism. We give a detail discussion of this scheme and validate it by three ways, security evaluation, BAN logic and performance evaluation, which proves that our authentication information exchange scheme can achieve security features and goals.
In this paper, an automatic heartbeat Classification method based on discrete wavelet transform (DWT) and kernel principal component analysis (KPCA) is proposed. DWT is employed to extract time-frequency characteristics of heartbeats, and KPCA is utilized to extract a more complete nonlinear representation of the principal components. In addition, RR interval features are also adopted. A three-layer multilayer perceptron neural network (MLPNN) is used as a classifier. The MIT-BIH Arrhythmia Database was used as a test bench. In the "class-oriented" evaluation, the classification accuracy is 98.48%, which is comparable to previous works. In the "subject-oriented" evaluation, the classification accuracy is 92.34%. The Se (sensitivity) of class "S" and "V" is 62.0% and 84.4% respectively, and the P + (positive predictive rate) of class "S" and "V" is 70.6% and 77.7% respectively. The results show an improvement on previous works. The proposed method suggested a better performance than the state-of-art method in real situation.
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