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.