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

Jie Huang,Zejun Huang,Yongjian Ren,Fengwei Zhu,Jian Wan,Xiaoxian Yang

doi:10.1155/2021/5598988

Abstract

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.

Highlights

At present, fishing vessel monitoring systems (VMSs) are widely used in fishing vessel safety management
To solve the above-stated problems, in this paper we propose a real-time anomaly detection model based on an edge computing framework
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

Summary

Introduction

At present, fishing vessel monitoring systems (VMSs) are widely used in fishing vessel safety management. In VMSs, the position and status information of each fishing vessel is collected and recorded by shipborne sensors at a certain time interval and sent back to a monitoring center, and a series of spatiotemporal data points will form a trajectory data set. In the process of sailing, fishing vessels may face unpredictable and/or abnormal conditions. In cases of equipment failure (radar, positioning equipment, etc.), bad weather (typhoon, etc.), or even terrorist events (such as hijacking by pirates), the monitoring center can timely identify the abnormal conditions using the trajectory data and extract the information necessary to take measures to maintain and guarantee the safety of the fishing vessel. There are the following shortcomings in the anomaly detection of marine fishing vessels through a monitoring center using transmitted trajectory data:

Objectives

Methods

Results

Conclusion