无线传感器网络中基于RSSI的节点距离预测

无线传感器网络大量应用在环境监测、目标跟踪、安全监控等领域,因此网络的自身定位是大多数应用的基础。常用的定位方法必须测量节点间的距离。为了预测距离值,根据实验获取的RSSI值与对应的距离值,先对实验数据进行滤波处理,建立面向Matlab神经网络工具箱的神经网络预测模型,利用神经网络的特性和Matlab工具箱的强大功能,通过实测数据对网络进行训练。预测结果表明,距离精度达到1 m之内。

Distance Prediction Based on RSSI in Wireless Sensor Networks

Wireless sensor networks are in the large-scale application for environment monitoring, target tracking, safety monitoring and other fields; therefore, network positioning itself is the basis of most applications. Some of the commonly used location methods must measure node spacing. In order to predict distance value, according to the experimental RSSI value and the corresponding distance value, this paper first carries out filtering processing to experiment data, and then establishes neural network predictive model based on Matlab neural network toolbox using neural network characteristics and the strong function of Matlab toolbox. Network training is done through measurement data. The prediction results show that the distance precision is within 1 m.