资源优化中无线传感网络覆盖率检测仿真研究

Research on Detection Simulation of Wireless Sensor Network Coverage in Resource Optimization

在网络资源的优化过程中,对无线传感器网络覆盖率的检测,能够有效提升网络资源利用效率。对无线传感网络覆盖率的检测,需要根据粒子群优化算法得到全局最优参数,对样本集进行分层次处理,完成传感器网络覆盖率的优化检测。传统方法对网络资源进行聚类,通过人工蜂群算法优化无线传感器网络资源参数,但忽略了对资源样本集进行层次化处理,导致检测精度偏低。提出无线传感器网络资源覆盖率检测方法。采用小波去噪方法去除无线传感器网络资源中存在的噪声信号。通过最小二乘支持向量机模型解决无线传感器网络资源的非线性问题,根据粒子群优化算法得到全局最优参数。采用小波多分辨率分析方法对无线传感器网络中的训练样本集进行分层次的处理,完成无线传感器网络中资源覆盖率的检测。实验结果表明,上述方法的资源覆盖率检测精度较高,且速度较快。

In the process of network resource optimization, to detect coverage rate of wireless sensor network can effectively improve the network resource utilization. Traditional methods ignore the hierarchical processing of resource sample set, which results in low detection accuracy. A method for detecting resource coverage rate in wireless sensor network was proposed. Firstly, the wavelet denoising method was used to remove the noise signal in wireless sensor network resources. Then, the model of least squares support vector machine was used to solve the nonlinear problem of wireless sensor network resource. Moreover, the global optimal parameter was obtained based on the particle swarm optimization algorithm. Finally, the wavelet multi -resolution analysis method was used to hierarchically process the training sample set in wireless sensor network. Thus, we completed the detection of resource coverage rate in wireless sensor networks. Simulation resuhs show that the detection precision of resource coverage in proposed method is high and the speed is fast.