基于两级预测的温室WSN系统数据传输方法

Data Transmission of WSN System in Greenhouse Based on Two-level Prediction

摘要:

为了减少温室WSN系统传感器节点数据传输次数，提出基于两级预测的温室WSN系统数据传输方法。首先，引入莱特准则进行序列异常值检测，研究并提出了便于节点实时计算的序列方差滑动递推计算方法。其次，分别在传感器节点和服务器建立一阶分段线性回归方程并结合自适应加权算法形成两级预测模型，设定传感器节点仅在预测误差超过设定阈值时上传实际采集值，其他时刻服务器自动触发线性回归模型预测填充该部分数据。同时，结合温室环境自动控制的特点，研究了一种基于抛物线的可变误差阈值确定方法。试验表明：分段一阶线性回归模型能够在规定误差阈值内逼近系统原始数据曲线，利用两级预测算法无线传感器节点数据发送次数可减少93%（误差阈值为0.9）。

Abstract:

In order to decrease the data transmission frequency of the sensor nodes in greenhouse WSN system, a method based on two-level prediction was presented. Firstly, Letts’ criterion was imported to detect the sequence of outliers and the calculation method of sliding recursive sequence variance was proposed to facilitate real-time calculation of the nodes. Secondly, a piecewise linear regression equation combined with weighted adaptive algorithm was established to form two-level prediction models in sensor node and server. When forecasting error exceeded the set threshold, the sensor nodes uploaded the actual collection value. At other times, the server automatically triggered the linear regression prediction model to fill the partial data. At the same time, a variable error threshold determination method based on parabola was presented according to the characteristics of the automatic control of facility environment. The tests proved that the first order linear regression model approximated the raw data curve in prescriptive error threshold and the number of sending data of WSN sensor nodes could be reduced 93% by using two-level prediction algorithm （error threshold is 0.9）.