Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Abstract

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

“…As IoT technology develops, various types of data are being collected. In the IoT environment, various sensors can typically collect traffic information [17,18] or obtain biometric information for healthcare services. Meng et al [19] proposed a privacy-aware factorization-based hybrid method for healthcare service recommendation.…”

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

“…Considering the above challenges, we propose a novel traffic prediction method named AS M V P distr−L S H , which is based on the principle of distributed locality-sensitive hashing (LSH) [24][25][26] to protect privacy and fill the missing traffic data [27]. LSH has a favorable feature that is to retain similarity, i.e., two adjacent points are likely to be given the same exponent [28][29][30][31].…”