DR-TSP: A Data Repairing Framework for Time Synchronization Problems in ERI Data

Abstract

Timestamps are often problematic in Internet-of-Things (IoT) systems due to time synchronization problems of distributed radio-frequency identification (RFID) readers or sensors. This issue may seriously affect the data quality in some fields, such as transportation. A typical IoT application in transportation is electronic registration identification of the motor vehicle (ERI), an emerging traffic data acquisition technology based on RFID. ERI data play a vital role in intelligent transportation. However, the data quality is often affected seriously by the inaccurate timestamps, which arise from the time-unsynchronized distributed ERI readers. To solve this issue, we propose a novel framework, data repairing of time synchronization problems (DR-TSP), which can detect the time-unsynchronized ERI readers and correct timestamp-deviated ERI data. Precisely, DR-TSP consists of three components. Problem reader discovery component employs a statistics-based method to detect the time-unsynchronized ERI readers and discovers the clock leaps of the problematic ERI reader through a smoothing-based method. Travel-time estimation component constructs a spatial correlative travel-time estimation model based on the neural network to infer timestamp deviation. The influence of clock deviation is considered in the model training. Data correction component utilizes the above results to correct the timestamp-deviated data. Experiments over large-scale ERI data collected from a big China city, Chongqing, show that our method can significantly improve data quality.