基于智能优化算法的物联网异构数据融合方法
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摘要
针对当前物联网数据融合方法速度慢、融合精度低等问题,以改善物联网异构数据融合效果为目标,提出一种基于智能优化算法的物联网异构数据融合方法.首先采用多个节点采集监测对象状态数据,并对每个节点采集的数据噪声进行过滤,初步减少数据规模,提高物联网异构数据质量;然后引入聚类分析算法处理簇首数据,消除簇内数据间的冗余;最后在汇聚节点采用智能优化算法对簇首数据进行加权融合,并在相同环境下与其他融合方法进行对比实验.实验结果表明,该方法能对物联网异构数据进行有效融合,获得了较高精度的物联网异构数据融合结果,物联网异构数据融合错误少、速度快,提高了物联网数据融合的效率.
Aiming at the problems of slow speed and low accuracy of data fusion methods in the Internet of Things,in order to improve the effect of heterogeneous data fusion in the Internet of Things,we proposed a heterogeneous data fusion method of the Internet of Things based on intelligent optimization algorithm.Firstly,multi-nodes were used to collect the status data of monitoring objects,and the noise of data collected by each node was filtered to reduce the size of data initially and improve the quality of heterogeneous data of the Internet of Things.Secondly,clustering analysis algorithm was introduced to process the data of cluster head and eliminate the redundancy of data in the cluster.Finally,intelligent optimization algorithm was used to weigh the cluster head data in the aggregation node,and the contrast experiment was compared with other fusion methods under the same environment.The experimental results show that this method can effectively fuse heterogeneous data of the Internet of Things,and obtains high-precision results of heterogeneous data fusion of the Internet of Things.The heterogeneous data fusion of the Internet of Things has fewer errors and faster speed,which improves the efficiency of data fusion of the Internet of Things.
Aiming at the problems of slow speed and low accuracy of data fusion methods in the Internet of Things,in order to improve the effect of heterogeneous data fusion in the Internet of Things,we proposed a heterogeneous data fusion method of the Internet of Things based on intelligent optimization algorithm.Firstly,multi-nodes were used to collect the status data of monitoring objects,and the noise of data collected by each node was filtered to reduce the size of data initially and improve the quality of heterogeneous data of the Internet of Things.Secondly,clustering analysis algorithm was introduced to process the data of cluster head and eliminate the redundancy of data in the cluster.Finally,intelligent optimization algorithm was used to weigh the cluster head data in the aggregation node,and the contrast experiment was compared with other fusion methods under the same environment.The experimental results show that this method can effectively fuse heterogeneous data of the Internet of Things,and obtains high-precision results of heterogeneous data fusion of the Internet of Things.The heterogeneous data fusion of the Internet of Things has fewer errors and faster speed,which improves the efficiency of data fusion of the Internet of Things.
引文
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[2]潘继强,冯永政.改进LEACH的传感器网络分簇路由算法[J].吉林大学学报(理学版),2018,56(6):1476-1482.(PAN Jiqiang,FENG Yongzheng.Improved LEACH Clustering Routing Algorithm of Sensor Network[J].Journal of Jilin University(Science Edition),2018,56(6):1476-1482.)
[3]刘雄飞,周小军,肖振国.基于能量和距离分簇的主动式RFID防碰撞算法[J].计算机工程与设计,2015,36(2):379-383.(LIU Xiongfei,ZHOU Xiaojun,XIAO Zhenguo.Active RFID Anti-collision Algorithm Based on Energy and Distance Clustering[J].Computer Engineering and Design,2015,36(2):379-383.)
[4]MARTINEZ B,VALSTAR M F,BINEFA X,et al.Local Evidence Aggregation for Regression-Based Facial Point Detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(5):1149-1163.
[5]陈毅红,冯全源.基于捕获效应的预约时隙分配RFID防碰撞协议研究[J].计算机学报,2015,38(12):2375-2390.(CHEN Yihong,FENG Quanyuan.A Reservation Slot Assignment Anti-collision Protocol for the RFID System Considering the Capture Effect[J].Chinese Journal of Computers,2015,38(12):2375-2390.)
[6]董真杰,郑琛瑶,张国龙.不同精度数据融合的自适应加权平均法研究[J].舰船电子工程,2014,34(10):31-33.(DONG Zhenjie,ZHENG Chenyao,ZHANG Guolong.Self-adaption of Weighted Average Research for Data Fusion with Different Precision[J].Ship Electronic Engineering,2014,34(10):31-33.)
[7]陈俊杰,倪培洲,许广富,等.基于递推最小二乘和改进支持度的WSN数据融合算法[J].测控技术,2019,38(2):65-70.(CHEN Junjie,NI Peizhou,XU Guangfu,et al.Data Fusion Method Based on Recursive Least Square and Improved Support for WSN[J].Measurement and Control Technology,2019,38(2):65-70.)
[8]陈英,胡艳霞,刘元宁,等.多传感器数据的处理及融合[J].吉林大学学报(理学版),2018,56(5):1170-1178.(CHEN Ying,HU Yanxia,LIU Yuanning,et al.Processing and Fusion for Multi-sensor Data[J].Journal of Jilin University(Science Edition),2018,56(5):1170-1178.)
[9]余修武,张可,周利兴,等.基于无偏灰色马尔科夫预测WSN数据融合算法[J].传感技术学报,2018,31(8):1266-1269.(YU Xiuwu,ZHANG Ke,ZHOU Lixing,et al.WSN Data Fusion Algorithm Based on Unbiased Grey Markov Prediction[J].Chinese Journal of Sensors and Actuators,2018,31(8):1266-1269.)
[10]胡向东,李秋实.基于人工鱼群算法优化神经网络的WSN数据融合[J].重庆邮电大学学报(自然科学版),2018,30(5):614-619.(HU Xiangdong,LI Qiushi.WSN Data Fusion Based on Neural Network Optimized by Artificial Fish Swarm Algorithm[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2018,30(5):614-619.)
[11]邱立达,刘天键,傅平.基于深度学习的无线传感器网络数据融合[J].计算机应用研究,2016,33(1):185-188.(QIU Lida,LIU Tianjian,FU Ping.Data Aggregation in Wireless Sensor Networks Based on Deep Learning[J].Application Research of Computers,2016,33(1):185-188.)
[12]郑毅,胡祥培,尹进.基于多任务支持向量机的健康数据融合方法[J].系统工程理论与实践,2019,39(2):418-428.(ZHENG Yi,HU Xiangpei,YIN Jin.Health Data Fusion Method Based on Multi-task Support Vector Machine[J].Systems Engineering Theory&Practice,2019,39(2):418-428.)
[13]余修武,范飞生,周利兴,等.无线传感器网络自适应预测加权数据融合算法[J].传感技术学报,2017,30(5):772-776.(YU Xiuwu,FAN Feisheng,ZHOU Lixing,et al.Adaptive Forecast Weighting Data Fusion Algorithm for Wireless Sensor Network[J].Chinese Journal of Sensors and Actuators,2017,30(5):772-776.)
[14]高红菊,刘艳哲,陈莎.基于改进K-means算法的WSN簇头传感器数据融合[J].农业机械学报,2015,46(1):162-167.(GAO Hongju,LIU Yanzhe,CHEN Sha.Fusion of WSN Cluster Head Data Based on Improved K-Means Clustering Algorithm[J].Journal of Agricultural Machinery,2015,46(1):162-167.)
[15]陈毅红,冯全源.物联网中标签持续到达的数据融合[J].计算机集成制造系统,2012,18(9):2076-2081.(CHEN Yihong,FENG Quanyuan.RFID Anti-collision Algorithms for Tags Continuous Arrival in Internet of Things[J].Computer Integrated Manufacturing Systems,2012,18(9):2076-2081.)