面向群智感知车联网的异常数据检测算法

摘要

群智感知车联网利用普通用户的手机或平板电脑等智能终端获得交通数据,解决了车联网以低成本获取足够数据的问题,但却凸显了数据"质"的问题.为此,在分析群智感知车联网的数据结构及数据异常特点的基础上,提出一种适用于群智感知车联网的异常数据检测算法,并依此剔除异常数据,提高数据质量.算法利用核密度估计理论对车联网数据的概率密度进行估计,进而构建信任函数计算被检数据的信任度,后根据统计学理论将信任度小于0的数据判定为异常数据.最后对该算法的可行性及性能进行了仿真,结果表明该算法的性能可满足实用需求,且对比传统的统计检测法在检测率和误检率上具有更好的性能.%Internet of Vehicles (IoV) based on crowdsensing technology,which gets traffic data by smartphone or panel PC from ordinary person,has solved the problem that getting sufficient data at low cost.However,it also makes a new problem that the data quality of the system is deteriorated.To solve this problem,by analyzing the structure of crowdsensing data and the characteristics of abnormal data in crowdsensing IoV,a data detection algorithm is put forward to eliminate the abnormal data in IoV system and consequently improve data quality.In the algorithm,kernel density estimation theory is used to estimate the probability density of traffic data,and a belief function is then constructed to derive the confidence value of every detected data.According to the statistical theory,the data whose confidence value is less than 0 is regarded as abnormal data.Finally,the feasibility and performance of the presented algorithm are simulated.The results show that the proposed algorithm can meet practical demands and achieve better performance than that of traditional statistical detection methods.