高能量无线传感网络隐藏异常结构数据识别

High Energy Wireless Sensor Networks Hidden Anomaly Data Identification

为了保障高能量无线传感网络的稳定运行,需要对高能量无线传感网络中隐藏的异常结构数据进行识别,采用当前数据识别方法对高能量无线传感网络中隐藏的异常结构数据进行识别时,存在识别效率差、抗干扰性能低的问题。基于高能量无线传感网络,提出一种异常结构数据识别方法。构建高能量无线传感网络数据传输的模型,通过高能量无线传感网络隐藏异常结构数据信号解析模型对异常结构数据信号进行解析,提高异常结构数据的识别效率。对高能量无线传感网络中的数据进行预处理,去除数据中存在的噪声,提高方法的抗干扰性能,采用小波分解的方法对流量序列中存在的细节信号进行展示,完成高能量无线传感网络中隐藏的异常结构数据识别。仿真结果表明,上述方法的抗干扰性能强、识别效率高。

In order to ensure the stable running of high - energy wireless sensor networks, we need to recognize the hidden abnormal structure data. A recognition method for abnormal structure data was proposed. The model of data transmission was constructed, and the signal analytic model of hidden abnormal structure data was used to analyze the signal of abnormal structure data, so as to improve the recognition efficiency of abnormal structure data. Then, the data in high - energy wireless sensor networks were preprocessed to remove the noise of data and improve the anti - interference performance of method. Finally, the wavelet decomposition method was used to display the detailed signal in the flow sequence. Thus, the recognition of hidden abnormal structure data in high - energy wireless sensor network was completed. Simulation results show that the proposed method has strong anti - interference performanee and high recognition efficiency.