Vessel Monitoring Systems (VMS) are extensively used in the world to provide information on the vessel's spatiotemporal distribution, monitor the fishing activities of marine fishery and manage the safety of vessel navigation. In the traditional VMS, there are some deficiencies in the interaction and real-time communication between the land system and marine vessel. We present an edge computing-based adaptable trajectory transmission policy (EC-ATT) for VMS to improve the communication efficiency in this paper. Firstly, a novel VMS framework named EC-VMS is proposed which composed of four layers. Each vessel has an edge computing intelligent node to collect data, process and transmit data. Meanwhile, the edge computing server is set up to enhance collaborative computing between the cloud and the edge, that transmits data through the Beidou navigation satellite system. Secondly, the EC-ATT utilizes the computing power of edge nodes to establish an adaptive data transmission mechanism, which reduces redundant data and satellite communication frequency. Besides, the packet loss feedback mechanism and error checking strategy are used to ensure the reliability of data transmission. The experimental results show that EC-ATT has better performance in typical cases, which not only reduces the average communication time but also strengthens the real-time availability of the VMS. INDEX TERMS Vessels monitoring systems, edge computing, marine communication, vessel trajectory.

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Fishing vessel monitoring systems (VMSs) play an important role in ensuring the safety of fishing vessel operations. Traditional VMSs use a cloud centralized computing model, and the storage, processing, and visualization of all fishing vessel data are completed in the monitoring center. Due to the limitation of maritime communications, the data generated by fishing vessels cannot be fully utilized, and communication delays lead to inadequate warnings in cases of fishing vessel abnormalities. In this paper, we present a real-time anomaly detection model (RADM) for fishing vessels based on edge computing. The model runs in the edge layer, making full use of the information of moving edge nodes and nearby nodes, and combines a historical trajectory extraction detection model with an online anomaly detection model to detect anomalies. The detection model of historical trajectory extraction mines frequent patterns in historical trajectories through multifeature clustering and identifies trajectories that are different from the frequent patterns as anomalies. Online anomaly detection algorithms detect anomalous behavior in specific scenarios based on the spatiotemporal neighborhood similarity and reduce the impact of anomaly evolution. Experiments show that RADM was more effective than traditional methods in real-time anomaly detection of fishing vessels, which provides a new method for upgrading the technology of traditional VMS.