改进型支持度函数的WSN水质监测数据融合方法  

Data fusion method for water quality monitoring using WSN based on improved support function

作　　者：匡亮[1,2] 施珮 季云峰 平震宇[1] 陈北京[2] Kuang Liang;Shi Pei;Ji Yunfeng;Ping Zhenyu;Chen Beijing(School of IoT Engineering,Jiangsu Vocational College of Information Technology,Wuxi 214153,China;School of Computer and Software,Nanjing University of Information Science&Technology,Nanjing 210044,China;Tilapia Industrial Research Center of China Agricultural Research System,Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization,Ministry of Agriculture Freshwater Fisheries Research Center,Chinese Academy of Fishery Sciences,Wuxi 214081,China;School of IoT Engineering,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江苏信息职业技术学院物联网工程学院,无锡214153 [2]南京信息工程大学计算机与软件学院,南京210044 [3]中国水产科学研究院淡水渔业研究中心,农业部淡水渔业和种质资源利用重点实验室,无锡214081 [4]江南大学物联网工程学院,无锡214122

出　　处：《农业工程学报》2020年第16期192-200,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中央级公益性科研院所专项基金(2019JBFM10);江苏省高等职业教育产教融合集成平台建设计划项目(苏教职函〔2019〕26号);江苏省自然科学基金(BK20131097);江苏信息职业技术学院重点课题(JSITKY201803)。

摘　　要：无线传感网络已被广泛应用到水质监测领域中,针对水质监测中对传感器数据高精度的要求,该研究提出一种基于支持度函数的数据融合算法。首先,对各传感器采集的数据进行一致性检测,保证数据的准确性;其次,采用改进的动态时间弯曲距离(Improved Dynamic Time Warping Distance,IDTW)对支持度函数(Support Function,SF)进行优化,实现水质参数时间序列数据间的互支持度计算;最后,通过加权算法完成数据的融合过程,实现错误数据的校正,获得高质量融合数据。该算法在水质监测平台中进行了试验,结果表明,IDTW-SF融合算法的平均绝对误差值为0.2792%,融合精度较其他对比算法更高,且运行速度较快。IDTW-SF支持度融合算法能够有效弥补现有水质监测系统数据采集准确率低、效率低等缺陷,基于此方法的水质监测系统提高了溶解氧数据准确率,在降低水产养殖风险,提高养殖效率等方面发挥重要作用。Wireless sensor network has been widely used in various types of industries,such as water quality detection.Due to all kinds of device faults and transmission faults,there are some outliers during water quality monitoring.In a large monitoring area,the difference between monitoring parameter values exists due to uneven distribution.The monitoring data in a single location is unsuitable to represent the real situation of the whole monitoring area.The data fusion method is used to fuse data in multiple locations.Traditional methods in water quality data fusion have problems of low accuracy and efficiency for limited to poor generalization and complex calculations.In response to requirements of high-precision for sensor data in water quality detection,a novel data fusion method based on a new support function IDTW-SF(Improved Dynamic Time Warping Distance Optimized Support Function)was proposed in this study.Based on the importance of dissolved oxygen in various water quality parameters,it was used as an example to study this research in this study.The purpose of data fusion was for correcting outliers to obtain high-quality data.Firstly,the consistency detection of sensor data improved the quality of the fusion data.With high computing complexity,the traditional Gaussian support function was a defective method in data fusion.The dissolved oxygen content was used for example to study the new data fusion method.An improved dynamic time warping distance IDTW(Improved Dynamic Time Warping Distance)was used to optimize a new support function SF(Support Function),thus calculated the support degree value between water quality time series data.Unlike the Gaussian support function,the SF function obtained mutual support degree of sensors without the exponent calculation.The weighted algorithm was used to complete the data fusion process.Based on Grey correlation analysis,the IDTW-SF combined the dynamic time warping distance.time segment strategy and Mahalanobis distance together.DTW algorithm was applied to replace the Euc

关 键 词：水质 监测 数据融合 无线传感器网络 支持度函数 动态时间弯曲距离 

分 类 号：S969.33[农业科学—水产养殖]