GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.

Deep Reinforcement Learning Based Joint Edge Resource Management in Maritime Network

Due to the rapid development of the maritime networks, there has been a growing demand for computation-intensive applications which have various energy consumption, transmission bandwidth and computing latency requirements. Mobile edge computing(MEC) can efficiently minimize computational latency by offloading computation tasks by the terrestrial access network. In this work, we introduce a space-air-ground-sea integrated network architecture with edge and cloud computing components to provide flexible hybrid computing service for maritime service. In the integrated network, satellites and unmanned aerial vehicles(UAVs) provide the users with edge computing services and network access. Based on the architecture, the joint communication and computation resource allocation problem is modelled as a complex decision process, and a deep reinforcement learning based solution is designed to solve the complex optimization problem. Finally, numerical results verify that the proposed approach can improve the communication and computing efficiency greatly.

LanePost:Lane-Based Optimal Routing Protocol for Delay-Tolerant Maritime Networks

Ship.to.ship, ship.to.shore radio links empowered by Wi Fi, Wi MAX etc have been recently exploited to build maritime multi.hop mesh networks to provide internet services to on.ship users. However, because of the mobility of the vessels/ships and the large inter.ship distances, nodes in the maritime network are frequently disconnected, forcing data communication in the maritime mesh networks to be opportunistic and delay.tolerant. In this paper, we present Lane Post, an optimization approach for maritime delay.tolerant routing protocol. We exploit the shipping lane information to predict the rendezvous opportunities of the ships to optimize the route selection in delay.tolerant routing. In particular, we show that when the shipping lane information is available, an opportunistic routing graph(ORG) for each ship can be constructed to predict its multi.hop data routing opportunities to the other ships or to the shore. Based on the ORG, we develop an optimal route protocol(i.e., Lane Post) for each ship to minimize its delay of multi.hop packet delivery via dynamic programming. We discussed the ways of collecting shipping lane information by centralized method or distributed method.The proposed Lane Post protocol was evaluated by ONE, an open.source delay.tolerant network simulator, which shows its dramatic performance improvement in terms of delay reduction compared to the state.of.the.art opportunistic routing protocols.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

Hybrid Satellite-UAV-Terrestrial Maritime Networks:Network Selection for Users on A Vessel Optimized with Transmit Power and UAV Position

The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea.The existing works focused on vessels collaboratively served by UAV-enabled aerial base station(ABSs)and terrestrial base stations(TBSs)deployed along the coast,and proved that data rate could be improved by optimizing transmit power and ABS’s position.In practice,users on a vessel can be collaboratively served by an ABS and a vesselenabled base station(VBS)in different networks.In this case,how to select the network for users on a vessel is still an open issue.In this paper,a TBS and a satellite respectively provide wireless backhaul for the ABS and the VBS.The network selection is jointly optimized with transmit power of ABS and VBS,and ABS’s position for improving data rate of all users.We solve it by finding candidates for network selection and iteratively solving transmit power and ABS’s position for each candidate.Simulation results demonstrate that data rate can be improved by collaborative coverage for users on a vessel.

Spatial-Modulated Physical-Layer Network Coding Based on Block Markov Superposition Transmission for Maritime Relay Communications

As an alternative to satellite communications,multi-hop relay networks can be deployed for maritime long-distance communications.Distinct from terrestrial environment,marine radio signals are affected by many factors,e.g.,weather conditions,evaporation ducting,and ship rocking caused by waves.To ensure the data transmission reliability,the block Markov superposition transmission(BMST)codes,which are easily configurable and have predictable performance,are applied in this study.Meanwhile,the physical-layer network coding(PNC)scheme with spatial modulation(SM)is adopted to improve the spectrum utilization.For the BMST-SMPNC system,we propose an iterative algorithm,which utilizes the channel observations and the a priori information from BMST decoder,to compute the soft information corresponding to the XORed bits constructed by the relay node.The results indicate that the proposed scheme outperforms the convolutional coded SM-PNC over fast-fading Rician channels.Especially,the performance can be easily improved in high spatial correlation maritime channel by increasing the memory m.

Enabling Technologies for Agile Maritime Communication Networks

With the rapid development of maritime activities,there has been a growing demand for high data rate and ultra-reliable maritime communications.Traditionally,this is provided by maritime satellites.Besides,shore and island-based base stations(BSs)can be built to extend the coverage of terrestrial networks providing the fourth-generation(4G)or even the fifth-generation(5G)services.Unmanned aerial vehicles(UAVs)-aided and ship-borne BSs can also be exploited to serve as relaying nodes in maritime mesh/ad-hoc networks.Despite all these approaches,there are still open issues towards the establishment of an agile maritime communication network(MCN).

Enabling Technologies for Agile Maritime Communication Networks

With the rapid development of maritime activities, there has been a growing demand for high data rate and ultra-reliable maritime communications. Traditionally, this is provided by maritime satellites. Besides, shore and island-based base stations (BSs) can be built to extend the coverage of terrestrial networks providing the fourth-generation (4G) or even the fifth-generation (5G) services. Unmanned aerial vehicles (UAVs)-aided and ship-borne BSs can also be exploited to serve as relaying nodes in maritime mesh/ad-hoc networks. Despite all these approaches, there are still open issues towards the establishment of an agile maritime communication network (MCN). Different from terrestrial communications for urban or suburban coverage, the MCN faces several challenges due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications. To address all these challenges, conventional communications and networking theories and methods need to be tailored for maritime application scenarios or new ones should be explored. The goal of this feature topic is to present the state-of-the-art original research, and the latest advances and innovations in key theories, technologies, and innovative applications for agile MCNs, as well as identify emerging research topics and point out the future research directions. Extended versions of papers published in conferences, symposiums, or workshop proceedings are encouraged for consideration.

Enabling Technologies for Agile Maritime Communication Networks

With the rapid development of maritime activities, there has been a growing demand for high data rate and ultra-reliable maritime communications. Traditionally,this is provided by maritime satellites. Besides, shore and island-based base stations (BSs) can be built to extend the coverage of terrestrial networks providing the fourth-generation (4G) or even the fifth-generation (5G) services.

Packet Transport for Maritime Communications:A Streaming Coded UDP Approach

The maritime communication network(MCN)plays an important role in the 6th generation(6G)system development.In MCNs,packet transport over long-distance lossy links will be ubiquitous.Transmission control protocol(TCP),the dominant transport protocol in the past decades,have had performance issues in such links.In this paper,we propose a novel transport approach which uses user datagram protocol(UDP)along with a simple yet effective bandwidth estimator for congestion control,and with a proactive packet-level forward erasure correction(FEC)code called streaming code to provide low-delay loss recovery without data retransmissions at all.We show that the approach can effectively address two issues of the state-of-the-art TCP variants in the long-distance lossy links,namely 1)the low bandwidth utilization caused by the slow increase of the congestion window(CWND)due to long roundtrip time(RTT)and the frequent CWND drop due to random and congestion losses,and 2)the high endto-end in-order delivery delay when re-transmissions are incurred to recover lost packets.In addition,we show that the scheme’s goodput has good smoothness and short-term intra-protocol fairness properties,which are beneficial for multimedia streaming and interactive applications that are prominent parts of today’s wireless traffic.

Location privacy protection of maritime mobile terminals

Mobile Edge Computing(MEC)can support various high-reliability and low-delay applications in Maritime Networks(MNs).However,security risks in computing task offloading exist.In this study,the location privacy leakage risk of Maritime Mobile Terminals(MMTs)is quantified during task offloading and relevant Location Privacy Protection(LPP)schemes of MMT are considered under two kinds of task offloading scenarios.In single-MMT and single-time offloading scenario,a dynamic cache and spatial cloaking-based LPP(DS-CLP)algorithm is proposed;and under the multi-MMTs and multi-time offloading scenario,a pseudonym and alterable silent period-based LPP(PA-SLP)strategy is proposed.Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy.Meanwhile,extending the alterable silent period,increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP.Furthermore,the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.

Trajectory Design for UAV-Enabled Maritime Secure Communications:A Reinforcement Learning Approach

This paper investigates an unmanned aerial vehicle(UAV)-enabled maritime secure communication network,where the UAV aims to provide the communication service to a legitimate mobile vessel in the presence of multiple eavesdroppers.In this maritime communication networks(MCNs),it is challenging for the UAV to determine its trajectory on the ocean,since it cannot land or replenish energy on the sea surface,the trajectory should be pre-designed before the UAV takes off.Furthermore,the take-off location of the UAV and the sea lane of the vessel may be random,which leads to a highly dynamic environment.To address these issues,we propose two reinforcement learning schemes,Q-learning and deep deterministic policy gradient(DDPG)algorithms,to solve the discrete and continuous UAV trajectory design problem,respectively.Simulation results are provided to validate the effectiveness and superior performance of the proposed reinforcement learning schemes versus the existing schemes in the literature.Additionally,the proposed DDPG algorithm converges faster and achieves higher utilities for the UAV,compared to the Q-learning algorithm.

STUDY ON DEVELOPMENT STRATEGY OF SHIPPING CENTERS AND TRANSPORTATION NETWORKS IN THE YELLOW SEA RIM

It is very important to establish cooperative mechanism to guarantee allmembers to develop their e-conomies in the Yellow Sea Rim. In this paper, the development strategiesof shipping centers and transportation network are discussed based on economic globalizationtendency. The results argue that a united transportation network should be built in order to promotethe economic competition of Northeast Asia in the world. As a key component of the economiccooperation, a hierarchical shipping centers network should be established with Hong Kong, Shanghai,Pusan, Kobe, and Tokyo as cores. The authorities of China, Japan, R. 0. Korea and D. P. R. Koreashould make more efforts to build a set of cooperation institutions based on raising thetransportation efficiency.

Ship Trajectory Prediction Based on BP Neural Network

In recent years,with the prosperity of world trade,the water transport industry has developed rapidly,the number of ships has surged,and ship safety accidents in busy waters and complex waterways have become more frequent.Predicting the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the ship.Aiming at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction,a ship navigation trajectory prediction method combining ship automatic identification system information and Back Propagation(BP)neural network are proposed.According to the basic principle of BP neural network structure,the BP neural network is trained by taking the characteristic values of ship navigation behavior at three consecutive moments as input and the characteristic values of ship navigation behavior at the fourth moment as output to predict the future ship navigation trajectory.Based on the Automatic Identification System(AIS)information of the waters near the Nanpu Bridge in Pudong New Area,Shanghai,the results show that the method is used to predict the ship's navigational behavior eigenvalues accurately and in real time.Compared with the traditional kinematics prediction trajectory method,the model can effectively predict ship navigation.The trajectory improves the accuracy of the ship's motion situation prediction,and has the advantages of high computational efficiency and strong versatility,and the error is within an acceptable range.

Economic Viability of Mega-size Containership in Different Service Networks

The mega-size containership viability was analyzed by considering different service networks for different ship sizes:hub-and-spoke and multi-port-calling (MPC) networks for mega-size containerships and conventional ships.A model was proposed,which quantifies the economies of scale in operating large con- tainerships and constructs models for ship routing under different service networks.A sensitivity analysis was conducted to test the effect of feeder costs and the results analyzed to determine optimal containership size with respect to different operational scenarios.Throughout model applications for Asia-Europe and Asia-North America trades,the mega-size containership is competitive in all scenarios for Asia-Europe,while it is viable for Asia-North America only when the feeder costs are low.

俄乌冲突背景下全球海运网络的结构特征分析

在俄乌冲突背景下,基于AIS轨迹数据和复杂网络分析法,分析全球海运网络的整体和局部结构特征,探究海上通道的枢纽作用以及区域海运网络的抗毁性。结果表明:俄乌冲突加剧了全球海运网络的动态演变,干散货海运网络的稳定性强于集装箱海运网络和液化天然气(LNG)海运网络。全球枢纽港口呈现明显的地域性分布和结构性差异特征,中国仅少数核心港口在全球海运网络中具有优势地位。在俄乌冲突紧张局势下,俄罗斯港口在LNG海运网络中保持了一定的竞争力。欧洲地区的直布罗陀海峡和英吉利海峡在三类海运网络中起到关键的中介枢纽作用,同时海运网络对于亚洲地区的琉球群岛、新加坡海峡、朝鲜海峡等也具有较高依赖性。中国与“一带一路”沿线国家之间的集装箱海运网络和干散货海运网络、与金砖国家之间的干散货海运网络均表现出较高的发展水平和较强的抗毁性。

海浪上下文信息补偿小目标检测算法

针对海上搜救图像中遇难人员在水面露出的面积小并且容易受到海浪反光、雨雾天气等恶劣环境影响,导致特征提取困难的问题,提出一种海浪上下文信息补偿小目标检测算法。首先,通过基于滑动窗口的图像预处理模块将图像进行裁剪,把关注点集中在目标物体周围,并减少图像中的无关区域,降低了计算量并提高了准确率;其次,提出一种海浪上下文模块,首次通过分析海浪的运动方向和强度,提取海浪上下文信息来辅助海上搜救小目标检测,提高检测精度。在SeaDronesSeev1和SeaDronesSeev2数据集上的实验结果表明,所提算法平均精度分别达到了73.29%和87.81%,相比YOLOv7-tiny算法,平均精度分别提高了21.84%和6.5%。所提算法提高了海上搜救小目标的检测精度,提高了海上搜救的效率。

基于改进DS证据理论和贝叶斯网络的海运供应链风险评估

作为一个复杂的网络组织,海运供应链为全球产业链提供运输服务,在全球化大背景下,海运供应链面临众多风险与挑战,影响供应链的安全平稳运行。因此,研究其风险至关重要。针对海运供应链风险评估体系中影响因素的不确定性和专家知识推断中存在的主观性,文章提出了基于改进DS证据理论和贝叶斯网络的海运供应链风险评估模型。结果表明,海运供应链处于较高风险等级状态,其中“信息系统风险”和“外部环境风险”概率较高,需要重点关注。该模型可以从系统的角度评估海运供应链风险,切实增强风险评估工作的科学性、准确性;评估结果可为供应链上的参与者进行风险管理提供有效参考。

21世纪海上丝绸之路共建国家水产品贸易网络特征及影响因素研究

21世纪海上丝绸之路共建国家是全球重要的水产品贸易市场。选取2011-2020年海上丝绸之路共建国家水产品贸易数据,运用社会网络分析法对共建国家水产品贸易网络特征及影响因素进行分析。结果显示:共建国家间贸易联系日益紧密,水产品贸易网络密度和互惠度均处于较高水平,且呈增长态势,冻鱼和甲壳类动物贸易态势较好;水产品贸易网络具有“核心—半边缘—边缘”的结构特征,位于核心区的中国、新加坡等国与沿线其他国家水产品贸易联系活跃,对共建国家水产品贸易具有明显促进作用;中国、新加坡和印度尼西亚等国居于水产品贸易网络的核心位置,且占据较多的结构洞,对沿线其他国家的影响力和控制力逐步增强;经济发展水平差异、人口规模差异以及陆地是否相邻对共建国家水产品贸易联系具有正向影响,地理距离对共建国家水产品贸易具有明显负向影响。

基于深度Q网络的海上环境智能路径规划

深入研究了融合航海优先级(NP)和优先级经验回放(PER)策略的深度Q网络(DQN)算法在海上环境智能路径规划问题上的应用。不同于传统路径规划算法,本优化算法能够自主探索并学习海上环境的规律,无需依赖人工构建的海洋环境全局信息。本研究开发了基于Gym框架的海上仿真环境,用以模拟和验证改进的DQN模型。该模型融合了航海优先级和优先级经验回放机制,通过调整学习过程中经验样本的利用频率,提升了算法对重要决策的学习效率。此外,引入新的奖赏函数,进一步增强了模型对路径规划问题的适应能力和稳定性。仿真实验结果证明,该模型在避免障碍物及寻找最佳路径方面相较于基准方法有显著提升,展现了一定的泛化性和优秀的稳定性。