基于信道冲击成型自适应机制的 WSN大数据采集算法

为解决无线传感网采集算法普遍存在的节点故障率高、链路抖动严重及抗噪性能较低等不足,提出了一种基于信道冲击成型自适应机制的无线传感网大数据采集算法。首先,根据无线传感网节点信号具有的分区及偏移特性设计了信道冲击成型方法,并基于编码率-信噪比-信号发射强度增益等参量构建能量密度与信道衰落匹配过滤方式,提高信号发射过程中能量密度;随后,通过传输信号与信道噪声具有的正交特性建立自适应抽样机制,根据垂直信号与噪声信号互相干涉中产生的正交效应进行匹配滤波,并针对节点采取了载波过滤的方式进行遍历过滤,降低了网络信号拥塞现象的发生。仿真实验表明:与当前常见的拉普拉斯抗噪传输算法(Laplasse Anti-Noise Transmission Algorithm,ANT-L机制)以及傅氏信号解析抗噪传输算法(Fourier Noise Analysis Algorithm for Noise Immunity,NI-FNA机制)相比,本文算法具有更高的数据吞吐性能,有效地降低了网络链路抖动现象,取得更低的传输误码率。

LARGE DATA ACQUISITION ALGORITHM OF WSN THAT BASED ON ADAPTIVE MECHANISM OF CHANNEL IMPACT SHAPING

In order to solve the shortcomings of wireless sensor network acquisition algorithms, such as high rate of node failure, severe link jitter and low level of anti-noise performance, a large data acquisition algorithm based on the adaptive mechanism of channel impact shaping is proposed. Firstly, according to the signal partition and offset characteristics of nodes, a channel impact shaping method is designed.Based on the parameters of coding rate, signal-to-noise ratio and signal transmission intensity gain, a matching filtering method of energy density and channel fading is constructed to improve the energy density in signal transmitting. The orthogonal characteristic of signal and channel noise establishes an adaptive sampling mechanism, which combines the orthogonal effect of signal interference that carry out matched filtering and adopts traversal filtering to reduce network signal congestion and improves network throughput performance. The simulation results show that the proposed algorithm has better data throughput performance than Laplasse Anti-Noise Transmission Algorithms and Fourier Noise Analysis Algorithms for Noise Immunity, which effectively reduce link jitter and has lower transmission error rate.