基于特征向量的物联网大数据压缩算法

IoT Big Data Compression Algorithm based on Characteristic Vector

随着物联网技术的快速发展,物联网大数据问题开始凸显,而物联网中标签海量数量和信息问题尤其突出。为方便有效地处理物联网数据,提出了一种基于特征向量的物联网大数据压缩算法。该算法按照每个标签的读取实际间隔、在该段时间内读取次数以及标签读取时的平均信号强度作为无线射频标签的特征,组成三元特征向量空间,并在此基础上对海量标签数据进行压缩。仿真结果与算法分析表明,随着数据量的增加,提出的压缩算法的压缩效果越来越好。

With the rapid development of IoT （Internet of Things） technology, the big data problem of IoT begins to highlight, and the mass quantity and information problem of the RFID tags in IoT also become especially prominent. For easy and effective processing of IoT data, the IoT big-data compression algorithm based on characteristic vector is proposed. This algorithm, with the actual read interval of each tag, and label reading times within that period of time, and the average signal strength during reading as characteristics of the RFID tag constitutes the ternary feature vector space, and based on this, exercises compression of the massive electronic tag data. Simulation results and algorithm analysis indicate that the compression algorithm proposed in this paper is more obvious in effect with the increase of the data amount.