基于收敛值动态混沌自旋机制的WSN网络安全收敛算法

针对当前WSN网络安全算法普遍存在收敛度分散,抗攻击能力较差,实现机理复杂等问题,提出了一种基于收敛值动态混沌自旋机制的的WSN网络安全收敛算法。首先,通过WSN网络指纹因子投影迁移的方式,引入模拟评估度量,实现对不同时间段内WSN网络安全收敛状态的精确评估,提高网络收敛值混沌度水平;随后,根据网络收敛状态,通过微分傅里叶准则,构建收益评估因子,实现了网络攻击指标的混沌自旋,有效加快WSN网络安全收敛速率。实验结果显示:与当前常用的超高频混沌安全收敛算法(Ultra High Frequency Chaotic Security Convergence Algorithm,UHFC-SC算法)、傅里叶混沌安全收敛算法(Fourier Chaotic Security Convergence Algorithm,FC-SC算法)等相比,所提算法具有更高的收敛速度,以及更高的网络冗余带宽剩余足与网络传输带宽,具有良好的实际部署价值。

THE WSN NETWORK CONVERGENCE ALGORITHM BASED ON CONVERGENCE DYNAMIC CHAOTIC SPIN MECHANISM

In order to solve the problems of decentralized convergence, poor anti-attack ability and complex implementation mechanism, a new WSN Network Security Convergence Algorithm Based on dynamic chaotic spin mechanism with convergence value is proposed. Firstly, the WSN network fingerprint factor is projected and migrated, and the simulation evaluation metrics are introduced to evaluate the WSN network security convergence state accurately in different time intervals, which greatly improve the chaos level of the network convergence value. Furthermore, the chaotic spin of the network attack index speeds up the WSN network convergence rate. Compared with UHF chaotic security convergence algorithm and Fourier chaotic security convergence algorithm, the proposed algorithm has so many advantages, such as short convergence time, fast convergence speed, sufficient redundant bandwidth, high redundancy transmission bandwidth and low false alarm rate. It has good practical deployment value.