Intelligent Anomaly Detection of Trajectories for IoT Empowered Maritime Transportation Systems
Jia Hu, Kuljeet Kaur, Hui Lin, Xiaoding Wang, Mohammad Mehedi Hassan, Imran Razzak, Mohammad Hammoudeh
Abstract
The convergence of Maritime Transportation Systems (MTS) and Internet of Things (IoT) has led to the promising IoT-empowered MTS (IoT-MTS). However, abnormal trajectories of maritime transportation ships can have highly negative impacts on the management of IoT-MTS. Therefore, anomaly detection of trajectories is important for the successful deployment of IoT-MTS. In this paper, we propose a Transfer Learning based Trajectory Anomaly Detection strategy, named TLTAD, for IoT-MTS. Specifically, a variational autoencoder is used to discover the potential connections between each dimension of the normal trajectory, while a graph variational autoencoder is used to explore the spatial similarity between normal trajectories. Based on internal connection of trajectories, a deep reinforcement learning algorithm, Twin Delayed Deep Deterministic policy gradient (TD3), is employed to train the trajectory anomaly detection model. To reduce the model training time, transfer learning is used to migrate the trained anomaly detection model between different regions of an ocean area or between similar ocean areas. Moreover, an efficient data transformation module is designed to improve the efficiency of model transfer. The experiments were conducted on a real-world automatic identification system (AIS) dataset. The results indicate that the proposed TLTAD can provide accurate anomaly detection on ships’ trajectories in IoT-MTS with reduced model training times.