ApproxECIoT:一种基于自适应分层采样的边缘计算新架构  

ApproxECIoT: New Edge Computing Architecture Based on Adaptive Stratified Sampling

作　　者：张德干 颜浩然[1,2] 张捷 张婷 王嘉旭 ZHANG De-Gan;YAN Hao-Ran;ZHANG Jie;ZHANG Ting;WANG Jia-Xu(School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384,China;Key Laboratory of Computer Vision and System,Ministry of Education(Tianjin University of Technology),Tianjin 300384,China;School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]天津理工大学计算机科学与工程学院,天津300384 [2]计算机视觉与系统教育部重点实验室(天津理工大学),天津300384 [3]北京交通大学电子信息工程学院,北京100044

出　　处：《软件学报》2022年第9期3437-3452,共16页Journal of Software

基　　金：国家自然科学基金(61571328);天津市自然科学基金(18JCZDJC96800);天津市重大科技专项(17YFZCGX00360)。

摘　　要：随着物联网技术的发展,目前的物联网系统产生的数据量越来越多,这些数据持续不断的传输到数据中心,传统的物联网数据处理分析系统效率低下且无法处理数量如此庞大的数据流.另外,物联网智能设备存在资源受限的特性,在分析数据时这一特性是不可忽略的.提出一种适用于物联网实时数据流处理的新架构ApproxECIoT(approximate edge computing Internet of Things),实现了一种自调整分层采样算法,用于处理物联网系统中产生的实时数据流.该算法在维持已给出的资源预算不变的情况下,根据每层方差的大小进行样本层内大小的调整,这对于资源有限的情况下提高计算结果准确度是非常有益的.最后使用模拟数据流和真实数据流进行实验分析,结果表明ApproxECIoT在边缘节点资源有限的情况下,仍能获得具有较高准确度的计算结果.With the development of the Internet of Things(IoT) technology, the current amount of data generated by the IoT system is increasing, and the data is continuously transmitted to the data center. The traditional IoT data processing and analysis system is inefficient and cannot handle such a large number of data streams. In addition, IoT smart devices have a resource-limited feature, which cannot be ignored during data analysis. This study proposes a new architecture ApproxECIoT(approximate edge computing IoT) suitable for realtime data stream processing of the IoT. It realizes a self-adjusting stratified sampling algorithm to process real-time data streams. The method adjusts the size of the sample strata according to the variance of each stratum while maintaining the given memory budget. This is beneficial to improving the accuracy of the calculation results when resources are limited. Finally, the experimental analysis is performed using simulated datasets and real-world datasets. The results show that ApproxECIoT can still obtain high-accuracy calculation results even with limited resources of the edge nodes.

关 键 词：物联网 边缘计算 近似计算 数据分析 实时数据流处理 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]