No abstract

No abstract

No abstract

With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel’s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories’ sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty results to overcome this problem. Furthermore, the different mobility models of the same vessel during the day and the night are also exploited to improve the prediction accuracy. Privacy issues have been taken into consideration in this paper. A quantitative model considering Markov order, training metadata and privacy leak degree is proposed to help the participant make the trade-off based on their customized requirements. We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels. The experiment results demonstrate that L-VTP can realize fine-grained long-term trajectory prediction with the consideration of privacy issues. The average error of 4.5-hour fine-grained prediction is less than 500 m. In addition, the proposed method can be extended to 10-hour prediction with an average error of 2.16 km, which is also far less than the communication range of ocean vessel communication devices.

Due to lack of the coverage of 3G/4G network, satellite communication which costs excessively is the main approach used in ocean to provide network service. Ocean mobile delay tolerant network (OMDTN) can provide low-cost data transmission service in the network by utilizing the contact chances of moving vessels. Spatio-temporal contact pattern is one of the key metrics to improve the efficiency of the routing algorithm in OMDTN. Some researches have been carried out on human handheld device and vehicular ad hoc networks (VANETs). However, the vessel's trajectory data is distributed and stored disorderly, which makes traditional contact pattern detection algorithm cannot be directly applied. In this paper, we design a parallel algorithm named VSTP based on MapReduce to detect spatio-temporal contact pattern from trajectories of over 2000 vessels. Studying the vessels' trajectories and the contact records, we observe that the vessels' contact pattern including inter-contact time distribution and contact times distribution is in sharp contrast to the study on human handheld device and VANETs. Our results can provide the guidelines for the design of data routing protocols on OMDTN and give a new solution to overcome the difficulty of ocean network coverage.