Outlier Trajectory Detection : A Trajectory

Abstract

With the booming of the Internet of Things, tremendous amount of sensors have been installed in different geographic locations, generating massive sensory data with both time-stamps and geo-tags. Such type of data usually have shown complex spatio-temporal correla- tion and are easily missing in practice due to communication failure or data corruption. In this paper, we aim to tackle the challenge – how to accurately and efficiently recover the missing values for corrupted spatio- temporal sensory data. Specifically, we first formulate such sensor data as a high-dimensional tensor that can naturally preserve sensors’ both geographical and time information, thus we call spatio-temporal Ten- sor. Then we model the sensor data recovery as a low-rank robust ten- sor completion problem by exploiting its latent low-rank structure and sparse noise property. To solve this optimization problem, we design a highly efficient optimization method that combines the alternating direc- tion method of multipliers and accelerated proximal gradient to minimize the tensor’s convex surrogate and noise’s ?1-norm. In addition to testing our method by a synthetic dataset, we also use passive RFID (radio- frequency identification) sensors to build a real-world sensor-array test- bed, which generates overall 115,200 sensor readings for model evalua- tion. The experimental results demonstrate the accuracy and robustness of our approach. 1