TPR‐DTVN: A Routing Algorithm in Delay Tolerant Vessel Network Based on Long‐Term Trajectory Prediction

Abstract

An efficient and low-cost communication system has great significance in maritime communication, but it faces enormous challenges because of high communication costs, incomplete communication infrastructure, and inefficient routing algorithms. Delay Tolerant Vessel Networks (DTVNs), which can create low-cost communication opportunities among vessels, have recently attracted considerable attention in the academic community. Most existing maritime ad hoc routing algorithms focus on predicting vessels’ future contacts by mining coarse-grained social relations or spatial distribution, which has led to poor performance. In this paper, we analyze 3-year trajectory data of 5123 fishery vessels in the China East Sea. Using entropy theory, we observe that the trajectory of the vessel has strongly spatial-temporal distribution regularity, especially when previous states were given. To predict accurate future trajectories, we develop a long-term accurate trajectory prediction model by improving the Bidirectional Long-Short Term Memory (Bi-LSTM) model. Based on predicted trajectories and the confident degree of each prediction step, we propose a series of routing algorithms called TPR-DTVN to achieve efficient communication performance. Finally, we carry out simulation experiments with extensive real data. Compared with existing algorithms, the simulation results show that TPR-DTVN can achieve a higher delivery ratio with lower cost and transmission delay.

Predicting Vessel Tracks in Waterways for Maritime Anomaly Detection

“…A Bi-LSTM model can also be found in the work of Liu et al (2021). They developed a series of routing algorithms, where a Bi-LSTM was augmented with an 'attention mechanism' to predict the next position of a vessel along the trajectory.…”

Construction of a Real-Time Ship Trajectory Prediction Model Based on Ship Automatic Identification System Data

“…Gao et al [29] studied a bi-directional LSTM (Bi-LSTM) network, aiming to enhance the memory ability of historical data and the correlation between future time series data. Liu et al [19] integrated convolutional transformations into a Bi-LSTM based on an attention mechanism in order to achieve long-term prediction. In another study, ship trajectory sequence features extracted by CNN are input into the LSTM model for prediction [22].…”

A deep learning method for the prediction of 6-DoF ship motions in real conditions

“…Machine learning methods involve Gaussian process regression models, 24 support vector machines, 25 and k-nearest neighbours. 26 Finally, examples of deep learning methods are convolutional neural networks, 27 LSTM, 28 bidirectional Gated Recurrent Units (GRU) 29 and transformer 30,31 algorithms. The latter can train historical AIS data to capture limited ship motion features and predict ship motion trajectories in advance.…”