Edge Computing-Based Adaptable Trajectory Transmission Policy for Vessels Monitoring Systems of Marine Fishery

Abstract

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.

Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment

“…To evaluate the effectiveness of RADM in the edge computing framework [32], in this section we compare the anomaly detection accuracy of VAD-MFC, VAD-SNS, and RADM. Compared with other anomaly detection indicators, the ROC curve has greater tolerance to the imbalance of positive and negative samples, and thus we use the ROC curve as the evaluation standard for anomaly detection [44].…”

“…e study sea area was divided into 0.01 °(latitude) × 0.01 °(longitude) grids for a total of 1000 × 1000 grid squares. We installed edge computing nodes on vessels using the Zhoushan fishing VMS [32]. Using fishing vessel trajectory data collected using the Zhoushan fishing VMS from May 2016 to November 2018, the frequent regions were constructed.…”

“…However, the above methods are based on traditional central computing architectures, and they are not optimized for edge nodes, so they are difficult to apply directly in edge computing scenarios. Moreover, in the field of marine fishing vessel management, only a few researchers have studied edge computing models of VMSs and established edge computing frameworks [31,32], but there is no research on trajectory anomaly detection on this basis. In this paper, we present a real-time anomaly detection model for fishing vessels based on edge computing, and our models are experimentally verified to be more effective than traditional methods.…”

“…is study builds on a framework for edge computing from previous research [32], in which fishing vessels collect positioning information in real-time through an on-board terminal device during navigation. For the positioning information TRcollected in the current timeseg T c , it can be formally expressed as a series of trajectory points in chronological order, that is, TR � (p 1 , t 1 ), (p 2 , t 2 ), .…”