WSNs中基于双层抑制数据冗余算法

Two-Level-Based Suppressing Data Redundancy in Wireless Sensor Networks

针对无线传感网络的海量数据的处理问题,提出基于双层抑制数据冗余算法;在第一层,传感节点引用皮尔森相关算法压缩数据,减少数据量;在第二层,由融合节点引用K均值聚类算法消除邻居节点间的数据冗余,进行数据聚类,降低数据间的冗余;实验数据表明:提出的TSDR算法有效地降低数据冗余。

For the issue of analyzing the big data in Wireless Sensor Networks(WSNs),the two-level-based suppressing data redundancy(TSDR)algorithm was proposed.At the first level,the sensors use a data compressionmodel based on the Pearson coefficient in order to reducethe amount of data collected periodically in each sensor.The aim is to reduce the number of data.At the second level,the aggregator node had an objective to eliminate data redundancycollected by neighboring nodes by using an adapted version ofK-means clustering method.Simulation on real data sensorsshows the effectiveness of our technique in reducing the big datacollected in WSNs.