基于智能优化算法的物联网异构数据融合方法

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.

Heterogeneous Data Fusion Method of Internet ofThings Based on Intelligent Optimization Agorithm

Aiming at the problems of slow speed and low accuracy of data fusion methods in the Internet of Things, in order to improve the effectof 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 ofthe 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 wascompared with other fusion methods under the same environment. The experimentalresults 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.