基于边缘智能计算的城市交通感知数据自适应恢复

Intelligent Edge Computing-Empowered Adaptive Urban Traffic Sensing Data Recovery

智能交通系统(intelligent transportation systems,ITSs)被广泛用于智慧城市中,却普遍存在感知数据缺失问题.而交通感知站点有限的存储计算能力严重制约感知数据的恢复,极大影响ITSs的正常使用.虽然可以利用边缘节点强大的存储计算能力解决这个困境,但边缘节点部署的高复杂性和感知数据时空相关性的高动态性对数据精确恢复提出挑战.为了解决上述挑战,提出基于边缘智能计算的城市交通感知数据自适应恢复系统.具体地,首先利用子模优化理论,提出具有理论下界的边缘节点次优部署分配算法.然后,基于低秩理论恢复感知数据,并基于恢复结果估计非缺失下限,通过反馈自适应调整感知站点的数据上传比例,从而保证数据精确恢复.最后,基于澳大利亚600个交通站点1年的感知数据构建原型系统,对所提算法进行评估.实验结果表明,所提算法的边缘节点部署性能达到最优性能的90%以上,缺失数据恢复精度比3种对比方法提高43.8%以上.同时,自适应数据恢复能够平均提高精度40.3%.

Intelligent transportation systems(ITSs)have been widely used in smart cities with a widespread problem of missing sensing data.The limited storage computing capability of traffic stations also severely restricts the recovery of sensing data and greatly affects the normal use of ITSs.Although the powerful computing capacity of edge nodes can be used to alleviate this issue,the high complexity and dynamics of the temporal and spatial correlation of sensing data still pose a serious challenge to the recovery process,making the result of data recovery,based on static edge nodes deployment and distribution,unsatisfactory.In order to effectively solve this series of problems,we propose an adaptive urban traffic sensing data recovery system based on intelligent edge computing.The system mainly consists of two parts:Firstly,the submodular optimization theory is used to design a suboptimal deployment and allocation scheme for edge nodes with a theoretical performance lower bound.Secondly,we address a data recovery method based on the low-rank theory.At the same time,the recovery results are used to calculate the corresponding nonmissing theoretical lower bound,feed back to the edge nodes,and then update the data distribution scheme to ensure an accurate recovery of subsequent sensing data.The experiments based on large-scale ITSs traces of Australia illustrate that our method can achieve 90%of the optimal performance for the edge node deployment,and improve the data recovery accuracy by 43.8%in comparison with three baselines.Furthermore,the adaptive data recovery scheme can improve the accuracy by 40.3%on average.