基于线性回归的无线传感器网络链路质量估计方法研究  

Research on link quality estimation method for wireless sensor networkbased on linear regression

作　　者：张书毓 施伟斌[1] 王文龙 厉祚 Zhang Shuyu;Shi Weibin;Wang Wenlong;Li Zuo(School of Optoelectronic Information&Computer Engineering,University of Shanghai for Science&Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《计算机应用研究》2022年第7期2132-2136,2155,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61374040);国防基础研究资助项目;上海理工大学科技发展资助项目(2020KJFZ082)。

摘　　要：针对无线传感器网络链路质量估计模型中回归算法复杂度高、缺少统一分类标准和公开数据集等问题,提出了一种基于EWMA和线性回归的链路质量估计方法ELR-LQE。以物理层获取的RSSI、LQI和SNR,以及包接收率PRR作为度量参数,分别在多种实验环境中采用不同的发射功率、竞争条件和部署方式采集数据,建立了链路质量估计数据集。通过最小值填充和EWMA对数据进行预处理,明显提高了回归模型的输入特征与链路质量的相关性。与现有方法相比,提出方法易于和网络层协议适配,并且复杂度较低,适合在资源有限的无线传感器网络节点中实现。实验结果显示,ELR-LQE具有较高的精度,在多种实验条件下平均的ME为4.6×10^(-2),R^(2)为0.99。Aiming at the high complexity of regression algorithms,the lack of unified classification standards and public data sets in the link quality estimation model of wireless sensor network,this paper proposed a link quality estimation method ELR-LQE(EWMA and linear regression based link quality estimation)based on EWMA and linear regression.Taking RSSI,LQI and SNR acquired from the physical layer,as well as PRR(packet reception ratio)as input metrics,it collected data in various experimental environments with different transmission power,contending conditions and deployment methods,and established a link quality estimation data set.It preprocessed the data by minimum interpolation and EWMA,which significantly improved the correlation between the input features of the regression model and the link quality.Compared with the existing methods,ELR-LQE is more adaptable to the network layer protocols,with lower complexity,thereby more suitable for implementation in wireless sensor network nodes with limited resources.The experimental results show that ELR-LQE has a high accuracy.Under a variety of experimental conditions,the average ME is 4.6×10^(-2) and R^(2) is 0.99.

关 键 词：指数加权移动平均 线性回归 最小值填充 无线传感器网络 链路质量估计 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]