基于大数据混沌耦合优化方式的WSN网络数据自递归加密机制

为解决当前WSN网络数据自递归加密机制难以实现混沌递归集有效收敛、元数据性能较差、致使出现加密性能下降、算法收敛程度较低等瓶颈现象，提出了一种基于大数据混沌耦合优化方式的WSN网络数据自递归加密机制。首先通过信息交互方式，基于瀑布流生成的思想对网络中混沌度较低的数据进行耦合优化，提高了混沌自归集的自适应收敛性能，降低了数据加密过程中的资源成本；随后基于元数据的阶数，采取微分方式对收敛过程进行二次整合，改善了加密过程中难以降低收敛复杂度的难题，极大的提高了本机制的弹性，降低了加密复杂度。仿真实验表明，与传统的宽带数据融合优化加密机制（optimal encryption scheme for ultra wideband data fusion，OEUW机制）、线性终端数据误差时延优化推断加密机制（linear terminal data error delay optimal inference encryption mechanism，LTED机制）相比，本算法能够有效的降低加密复杂度，减少加密时间，提升传输带宽质量，具有显著的实际部署价值。

Self recursive encryption mechanism of WSN network data based on chaotic coupling optimization of large data

in order to solve the current WSN network data encryption mechanism to realize chaotic self recursive set effective convergence, metadata poor performance, resulting in the emergence of encryption algorithm convergence performance decrease, the low degree of bottleneck phenomenon, proposes a self recursive encryption mechanism of big data chaotic optimization method based on WSN data network; first by information exchange. The waterfall flow generated by coupling optimization of network ideological chaos low based on data can greatly improve the convergence performance of adaptive chaotic self collection, reduce the data encryption in the process of resource cost; then based on the order of metadata, take differential form two integration of the convergence process, greatly improved to reduce the problem of convergence complexity of the encryption process, greatly improves the system flexibility, reduces the complexity of encryption. Simulation results show that the fusion and broadband data encryption mechanism, optimize the traditional linear terminal data error delay optimization inference (Linear terminal data error encryption mechanism delay optimal inference encryption mechanism LTED mechanism) compared to the algorithm in this paper can effectively reduce the complexity of encryption, reduce the encryption time, enhance the quality of transmission bandwidth, has significant practical deployment value.