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 require...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.展开更多
Investigating the influence of international events on global maritime networks is a challenging task that must comprehensively incorporate geographical, political, and maritime sciences. Understanding global maritime...Investigating the influence of international events on global maritime networks is a challenging task that must comprehensively incorporate geographical, political, and maritime sciences. Understanding global maritime network dynamics is an initial and critical step in this investigation. This study proposes an automatic identification system(AIS)-based approach to understanding maritime network dynamics before and after international events. In this approach, a spatiotemporal modeling method is introduced to measure the similarity in shipping trends before and after international events. Then, a spatiotemporal analytic framework is proposed to understand the maritime network dynamics by grouping similar situation, and assessing possible indirect effects within a network. Finally, three case studies of international events, military conflict, lifted economic sanctions, and government elections, were used to investigate the observed network dynamics possibly affected by international events. The results indicate that container, tanker, and bulk shipping between India and its connected countries all declined more than 69% after military conflicts between India and Pakistan in August 2015. Tanker shipping between Iran and the United Arab Emirates increased 51% after economic sanctions on Iran were lifted. Container shipping between Sri Lanka and Singapore, Malaysia, and India increased more than 74% after the general election in Sri Lanka. These investigations demonstrate the feasibility of the proposed approach in assessing the possible effects of international events on maritime network dynamics.展开更多
With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Marit...With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios.展开更多
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 sch...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.展开更多
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 ...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.展开更多
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 e...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.展开更多
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 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.展开更多
Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence netw...Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence networks.However,the resource constraints of devices,the delay-sensitive nature of tasks,and the dynamic environmental conditions present significant challenges.While Multi-Armed Bandit(MAB)algorithms have been explored for task offloading,their performance is often constrained in highly dynamic scenarios with complex,nonlinear utility dependencies.To address these challenges,we propose a Group Neural MAB(GN-MAB)approach that jointly optimizes idle device selection(i.e.,arm groups)and DNN partitioning decisions(i.e.,arms)for efficient collaborative inference.Building upon the neural upper confidence bound algorithm,GN-MAB dynamically balances the exploration and exploitation,enabling continuous adaptation of offloading strategies across sequential inference tasks.Extensive experimental results show that GN-MAB outperforms baseline approaches,achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.展开更多
With the exponential growth of maritime wireless devices and the rapid development of maritime applications,traditional maritime communication networks encounter communication and computation limitations in supporting...With the exponential growth of maritime wireless devices and the rapid development of maritime applications,traditional maritime communication networks encounter communication and computation limitations in supporting computation-intensive and latency-critical tasks.Edge computing and Intelligent Reflecting Surface(IRS)have emerged as promising techniques to improve communication and computation services for maritime devices with limited computation capabilities and battery capacity.This paper studies an IRS Mounted on Unmanned Aerial Vehicle(UIRS)assisted maritime edge computing network,in which the UIRS is deployed to assist the transmission from Unmanned Surface Vehicles(USVs)to the edge server via Non-Orthogonal Multiple Access(NOMA)protocol.We propose a resource management and trajectory optimization scheme by jointly optimizing subslot duration,offloading ratios,transmit power,edge computation capability allocation,UIRS phase shifts and UIRS trajectory,aiming at minimizing the overall energy consumption.Since the non-convex nature of the optimization problem,we propose a two-layered method by decomposing the original problem into two subproblems.The top-layered subproblem is solved by the Semi-Definite Relaxation(SDR)method and the underlying-layered subproblem is solved by the Deep Deterministic Policy Gradient(DDPG)algorithm.Numerical results demonstrate that our proposed scheme can effectively and efficiently reduce overall energy consumption.展开更多
China's global shipping connectivity had been somewhat overlooked as the bulk of related studies predominantly focused on the throughput volume of its own port cities. This article tackles such lacunae by providin...China's global shipping connectivity had been somewhat overlooked as the bulk of related studies predominantly focused on the throughput volume of its own port cities. This article tackles such lacunae by providing a relational perspective based on the extraction of vessel movement archives from the Lloyd's List corpus. Two complementary analyses are proposed: long-term dynamics with all ships included(1890–2008) and medium-term dynamics focusing on container flows(1978–2016). Each analysis examines China's maritime connectivity in various ways and on different spatial scales, from the global to the local, in terms of concentration, vulnerability, and expansion. The main results underline the influence of technological, economic, and political factors on the changing distribution of connectivity internally and externally. In particular, China has managed to reduce its dependence upon external transit hubs, to increase the internal connectivity of its own port system, and to strengthen its dominance towards an increasing number of foreign nodes and trade partners through the maritime network.展开更多
Recently,the fifth-generation(5G)of wireless networks mainly focuses on the terrestrial applications.However,the well-developed emerging technologies in 5G are hardly applied to the maritime communications,resulting f...Recently,the fifth-generation(5G)of wireless networks mainly focuses on the terrestrial applications.However,the well-developed emerging technologies in 5G are hardly applied to the maritime communications,resulting from the lack of communication infrastructure deployed on the vast ocean,as well as different characteristics of wireless propagation environment over the sea and maritime user distribution.To satisfy the expected plethora of broadband communications and multimedia applications on the ocean,a brand-new maritime information network with a comprehensive coverage capacity in terms of all-hour,all-weather,and all-sea-area has been expected as a revolutionary paradigm to extend the terrestrial capacity of enhanced broadband,massive access,ultra-reliable,and low-latency to the vast ocean.Further considering the limited available resource of maritime communication infrastructure,the convergence of broadband and broadcast/multicast can be regarded as a possible yet practical solution for realizing an efficient and flexible resource configuration with high quality of services.Moreover,according to such multi-functionality and all-coverage maritime information network,the monitoring and sensing of vast ocean area relying on massive Ocean of Things and advanced radar techniques can be also supported.Concerning these issues above,this study proposes a Software Defined Networking(SDN)based Maritime Giant Cellular Network(MagicNet)architecture for broadband and multimedia services.Based on this network,the convergence techniques of broadband and broadcast/multicast,and their supporting for maritime monitoring and marine sensing are also introduced and surveyed.展开更多
Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy d...Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy detector has attracted much attention for its low computational complexity and superior performance. Motivated by this, a cooperative spectrum sensing scheme is proposed in this paper based on centralized double threshold in the maritime communication networks(MCN), where the energy value of received signal in each cognitive node is forwarded to the fusion center for final decision based on double thresholds. Additionally, the proposed scheme is further optimized for the decisions that the energy is within the scope of maximum threshold and minimum threshold. Simulation experiments verify the performance of the proposed method.展开更多
As supply chains in today’s world become more complex and fragile,enhancing the resilience of maritime transport is increasingly imperative.The COVID-19 epidemic in 2020 exposed the vulnerability of existing supply c...As supply chains in today’s world become more complex and fragile,enhancing the resilience of maritime transport is increasingly imperative.The COVID-19 epidemic in 2020 exposed the vulnerability of existing supply chains,causing substantial impacts such as supply shortages,procurement constraints,logistics delays and port congestion,highlighting the need to build resilient maritime transportation networks(MTNs)and reigniting research on the resilience of maritime transport.Based on science mapping,we quantitatively analysed the domain of resilience of MTNs.We mainly study the resilience of MTNs from the following aspects:the construction of MTNs and their topological characterization,vulnerability-orientated resilience analysis of MTNs,recovery-orientated resilience analysis of MTNs,investment decision-orientated resilience analysis of MTNs,climate change-orientated resilience analysis of MTNs and pandemic-orientated resilience analysis of MTNs.This study reviews recent advances in MTN resilience research,highlighting research topics,shortcomings and future research agenda.展开更多
基金the National Natural Science Foundation of China under Grant No. U1805262
文摘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.
基金Key Project of the Chinese Academy of Sciences,No.ZDRW-ZS-2016-6-3The National Key Research and Development Program of China,No.2017YFB0503802+1 种基金National Natural Science Foundation of China,No.40971233,No.41771473LIESMARS Special Research Funding
文摘Investigating the influence of international events on global maritime networks is a challenging task that must comprehensively incorporate geographical, political, and maritime sciences. Understanding global maritime network dynamics is an initial and critical step in this investigation. This study proposes an automatic identification system(AIS)-based approach to understanding maritime network dynamics before and after international events. In this approach, a spatiotemporal modeling method is introduced to measure the similarity in shipping trends before and after international events. Then, a spatiotemporal analytic framework is proposed to understand the maritime network dynamics by grouping similar situation, and assessing possible indirect effects within a network. Finally, three case studies of international events, military conflict, lifted economic sanctions, and government elections, were used to investigate the observed network dynamics possibly affected by international events. The results indicate that container, tanker, and bulk shipping between India and its connected countries all declined more than 69% after military conflicts between India and Pakistan in August 2015. Tanker shipping between Iran and the United Arab Emirates increased 51% after economic sanctions on Iran were lifted. Container shipping between Sri Lanka and Singapore, Malaysia, and India increased more than 74% after the general election in Sri Lanka. These investigations demonstrate the feasibility of the proposed approach in assessing the possible effects of international events on maritime network dynamics.
基金supported by the National Natural Science Foundation of China under Grant 62371181the Project on Excellent Postgraduate Dissertation of Hohai University (422003482)the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029。
文摘With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios.
基金supported in part by the National Natural Science Foundation of China under Grant 62001056, 61925101, U21A20444in part by the Fundamental Research Funds for the Central Universities under Grant 500421336 and Grant 505021163。
文摘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.
基金supported by the National Key Research and Development Program of China (2021YFE0105500)the National Natural Science Foundation of China (61801166).
文摘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.
基金supported by the Six Categories Talent Peak of Jiangsu Province(No.KTHY-039)the Future Network Scientific Research Fund Project(No.FNSRFP-2021-YB-42)+1 种基金the Science and Technology Program of Nantong(No.JC2021016)the Key Research and Development Program of Jiangsu Province of China(No.BE2021013-1)。
文摘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.
基金supported by Natural Science Foundation of China(NSFC)under Grant no.61801248,62171240by the Key Research and Development Program of Jiangsu Province of China under Grant BE2021013-1+2 种基金by Science and Technology Program of Nantong under JC2021121by State Key Laboratory of Advanced Optical Communication Systems and Networks under Grant 2021GZKF006by the Postgraduate Research&Practice Innovation Program of Jiangsu Province KYCX223346。
文摘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.
基金supported by the Fundamental Research Funds for the Central Universities,South-Central MinZu University(No.CZQ25005)the Young Innovative Talents Project of Department of Education of Guangdong Province(Nos.2025KQNCX261,2025KQNCX262)+1 种基金the National Natural Science Foundation of China(No.62201621)the Young Scientific and Technological Talents Training Program of Hubei Province(No.2025DJA072).
文摘Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence networks.However,the resource constraints of devices,the delay-sensitive nature of tasks,and the dynamic environmental conditions present significant challenges.While Multi-Armed Bandit(MAB)algorithms have been explored for task offloading,their performance is often constrained in highly dynamic scenarios with complex,nonlinear utility dependencies.To address these challenges,we propose a Group Neural MAB(GN-MAB)approach that jointly optimizes idle device selection(i.e.,arm groups)and DNN partitioning decisions(i.e.,arms)for efficient collaborative inference.Building upon the neural upper confidence bound algorithm,GN-MAB dynamically balances the exploration and exploitation,enabling continuous adaptation of offloading strategies across sequential inference tasks.Extensive experimental results show that GN-MAB outperforms baseline approaches,achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.
基金supported in part by the National Natural Science Foundation of China(Nos.62371085,51939001,62122069,and 62071431)in part by the Fundamental Research Funds for the Central Universities(No.3132023514).
文摘With the exponential growth of maritime wireless devices and the rapid development of maritime applications,traditional maritime communication networks encounter communication and computation limitations in supporting computation-intensive and latency-critical tasks.Edge computing and Intelligent Reflecting Surface(IRS)have emerged as promising techniques to improve communication and computation services for maritime devices with limited computation capabilities and battery capacity.This paper studies an IRS Mounted on Unmanned Aerial Vehicle(UIRS)assisted maritime edge computing network,in which the UIRS is deployed to assist the transmission from Unmanned Surface Vehicles(USVs)to the edge server via Non-Orthogonal Multiple Access(NOMA)protocol.We propose a resource management and trajectory optimization scheme by jointly optimizing subslot duration,offloading ratios,transmit power,edge computation capability allocation,UIRS phase shifts and UIRS trajectory,aiming at minimizing the overall energy consumption.Since the non-convex nature of the optimization problem,we propose a two-layered method by decomposing the original problem into two subproblems.The top-layered subproblem is solved by the Semi-Definite Relaxation(SDR)method and the underlying-layered subproblem is solved by the Deep Deterministic Policy Gradient(DDPG)algorithm.Numerical results demonstrate that our proposed scheme can effectively and efficiently reduce overall energy consumption.
基金Under the auspice of European Research Council under the European Union’s Seventh Framework Programme(FP/2007-2013)/ERC(313847)‘World Seastems’National Natural Science Foundation of China(No.41371140)National Social Science Foundation of China(No.16ZDA016)
文摘China's global shipping connectivity had been somewhat overlooked as the bulk of related studies predominantly focused on the throughput volume of its own port cities. This article tackles such lacunae by providing a relational perspective based on the extraction of vessel movement archives from the Lloyd's List corpus. Two complementary analyses are proposed: long-term dynamics with all ships included(1890–2008) and medium-term dynamics focusing on container flows(1978–2016). Each analysis examines China's maritime connectivity in various ways and on different spatial scales, from the global to the local, in terms of concentration, vulnerability, and expansion. The main results underline the influence of technological, economic, and political factors on the changing distribution of connectivity internally and externally. In particular, China has managed to reduce its dependence upon external transit hubs, to increase the internal connectivity of its own port system, and to strengthen its dominance towards an increasing number of foreign nodes and trade partners through the maritime network.
基金supported by the National Natural Science Foundation China(Nos.61931015 and 61971257)the National Key R&D Program of China(Nos.2020YFD0901000 and 2017YFE0112300)+2 种基金Beijing National Research Center for Information Science and Technology(Nos.BNR2019RC01014 and BNR2019TD01001)the project of Peng Cheng Laboratory(No.LZC0020)the China Postdoctoral Science Foundation(Nos.2019T120091 and 2018M640130)。
文摘Recently,the fifth-generation(5G)of wireless networks mainly focuses on the terrestrial applications.However,the well-developed emerging technologies in 5G are hardly applied to the maritime communications,resulting from the lack of communication infrastructure deployed on the vast ocean,as well as different characteristics of wireless propagation environment over the sea and maritime user distribution.To satisfy the expected plethora of broadband communications and multimedia applications on the ocean,a brand-new maritime information network with a comprehensive coverage capacity in terms of all-hour,all-weather,and all-sea-area has been expected as a revolutionary paradigm to extend the terrestrial capacity of enhanced broadband,massive access,ultra-reliable,and low-latency to the vast ocean.Further considering the limited available resource of maritime communication infrastructure,the convergence of broadband and broadcast/multicast can be regarded as a possible yet practical solution for realizing an efficient and flexible resource configuration with high quality of services.Moreover,according to such multi-functionality and all-coverage maritime information network,the monitoring and sensing of vast ocean area relying on massive Ocean of Things and advanced radar techniques can be also supported.Concerning these issues above,this study proposes a Software Defined Networking(SDN)based Maritime Giant Cellular Network(MagicNet)architecture for broadband and multimedia services.Based on this network,the convergence techniques of broadband and broadcast/multicast,and their supporting for maritime monitoring and marine sensing are also introduced and surveyed.
文摘Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy detector has attracted much attention for its low computational complexity and superior performance. Motivated by this, a cooperative spectrum sensing scheme is proposed in this paper based on centralized double threshold in the maritime communication networks(MCN), where the energy value of received signal in each cognitive node is forwarded to the fusion center for final decision based on double thresholds. Additionally, the proposed scheme is further optimized for the decisions that the energy is within the scope of maximum threshold and minimum threshold. Simulation experiments verify the performance of the proposed method.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3107903)the National Natural Science Foundation of China(Grant No.51920105014 and 51909202)+2 种基金the Young Elite Scientist Sponsorship Program by CAST(Grant No.2021QNRC001)Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City(Grant No.2021JJLH0012)innovation and entrepreneurship team import project of Shaoguan City(Grant No.201208176230693).
文摘As supply chains in today’s world become more complex and fragile,enhancing the resilience of maritime transport is increasingly imperative.The COVID-19 epidemic in 2020 exposed the vulnerability of existing supply chains,causing substantial impacts such as supply shortages,procurement constraints,logistics delays and port congestion,highlighting the need to build resilient maritime transportation networks(MTNs)and reigniting research on the resilience of maritime transport.Based on science mapping,we quantitatively analysed the domain of resilience of MTNs.We mainly study the resilience of MTNs from the following aspects:the construction of MTNs and their topological characterization,vulnerability-orientated resilience analysis of MTNs,recovery-orientated resilience analysis of MTNs,investment decision-orientated resilience analysis of MTNs,climate change-orientated resilience analysis of MTNs and pandemic-orientated resilience analysis of MTNs.This study reviews recent advances in MTN resilience research,highlighting research topics,shortcomings and future research agenda.
