LSH-based missing value prediction for abnormal traffic sensors with privacy protection in edge computing

Abstract

Traffic flow prediction is an important part of intelligent transportation systems (ITS). However, sensor failures or the transmission distortion often occur in the process of data acquisition, which will inevitably cause the loss or abnormality of traffic flow data transmitted to the edge server. In this situation, it is necessary to share traffic flow data among different platforms. However, existing traffic flow prediction methods are facing two challenges in the process of traffic flow data sharing. First, user privacy is often leaked in the process of sharing traffic data on various platforms. Moreover, with the continuous updating of data, the efficiency and scalability of data sharing between different platforms will become lower and lower. In view of the above challenges, in this paper, we propose a novel prediction method for the missing traffic flow data caused by abnormal sensors, named $$ASMVP_{distr-LSH}$$ A S M V P d i s t r - L S H based on distributed locality-sensitive hashing (LSH) technique. At last, a case study is presented to illustrate the feasibility and effectiveness of our approach $$ASMVP_{distr-LSH}$$ A S M V P d i s t r - L S H .