Maritime Transportation System Research Articles

Blockchain-enabled authentication framework for Maritime Transportation System empowered by 6G-IoT

In recent years, Maritime Transportation Systems (MTS) have experienced significant advancements through the adoption of Internet of Things (IoT) technology. The introduction of 6G mobile networks has further expanded possibilities by offering enhanced communication services and additional functionalities such as processing, caching, sensing, and control for a wide array of IoT devices. Despite these technological advancements, the integration of IoT and 6G has introduced new risks and challenges in terms of safety and reliability. The involvement of various maritime stakeholders in scheduling and managing marine transportation exacerbates these challenges. Moreover, the MTS has encountered escalating security and privacy concerns with unauthorized data access and message tampering pose significant vulnerabilities in the 6G-IoT-enabled MTS environment. Therefore, a shared and controlled access mechanism that cannot be manipulated or tampered with by unauthorized parties is also an essential requirement in MTS. To address these issues, this paper presents an authentication framework leveraging blockchain technology, specifically designed for 6G-IoT-enabled MTS. The objective is to handle real-time data using decentralized peer-to-peer cloud servers with minimal latency, all while addressing security and privacy concerns specific to MTS. This integration also serves to mitigate various security threats. The protocol’s security and privacy are verified through rigorous evaluations, including the ROR model, Scyther tool, and informal security analysis. A simulation of the proposed protocol using MIRACL is conducted, offering a comprehensive assessment of its computational costs and security features when compared to existing protocols, demonstrating its superior security and efficiency.

YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI

Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

Preventing Catastrophic Cyber–Physical Attacks on the Global Maritime Transportation System: A Case Study of Hybrid Maritime Security in the Straits of Malacca and Singapore

This paper examines hybrid threats to maritime transportation systems and their governance responses; focusing on the congested Straits of Malacca and Singapore (SOMS) as an illustrative case study. The methodology combines secondary sources with primary data from 42 expert interviews, a 28 respondent survey, and two maritime security roundtables. Key findings were that ships’ critical systems are increasingly interconnected, yet aging IT infrastructure and minimal cybersecurity awareness among crews heighten risks. Meanwhile, regional terrorist groups have previously targeted shipping and shown considerable skill in exploiting online tools, aligning with broader calls for jihadist violence. Furthermore, opportunistic piracy persists in the SOMS with the potential to disrupt shipping. Experts confirmed that maritime cybersecurity lags behind other critical infrastructure sectors and needs updated governance. Initial International Maritime Organization (IMO) guidelines lack specificity but revisions and updated IMO guidance are in process, while Port state implementation of maritime cybersecurity standards varies. Crucially, information sharing remains inadequate, even as recorded attacks increase. Findings underscore that although major hybrid incidents have not occurred, simulations and threat actors’ capabilities demonstrate potential for catastrophic collisions or cascading disruption in congested waterways. Mitigating factors like redundancy and crew training are deficient currently. Some alignment between SOMS states on maritime security cooperation exists, but not on cyber threats specifically. Key recommendations include an anonymous cyber attack reporting system, reinforced training and shipboard systems, and consolidated regional frameworks. Until these priorities are addressed, the analysis concludes that hybrid vulnerabilities in this vital global chokepoint remain a serious concern.

A novel object-oriented Bayesian network on risk assessment of sea lanes of communication

The maritime transportation system heavily depends on sea lanes of communication. Unfortunately, the surge in shipping activities has also resulted in an increase in maritime incidents, encompassing both conventional accidents and piracy-related events. This paper presents a novel object-oriented Bayesian network framework for assessing the risk of traditional maritime accidents and piracy-related incidents. Furthermore, an enhanced machine-learning-based Tree augmented naïve learning approach is proposed to address the correlation relationships among the influential factors and the distinctions between mathematical and practical meanings. The paper also introduces mutual information theory and an improved scenario construction approach to identify the key influential factors on maritime risks and unveil the non-linear relationships among them. The findings indicate that both the degraded natural environment and social environment contribute to the risk levels of maritime accidents. Furthermore, the type of ship is recognized as the most significant factor contributing to unsafe conditions. The research results will offer valuable insights to the maritime industry, specifically aiding ship owners, ship crew, and shipping companies in understanding the key influential factors in multiple types of maritime accidents and assessing potential risks.

A survey on computational intelligence approaches for intelligent marine terminal operations

AbstractMarine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well‐known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in‐depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area.

Enhancing target detection capability of the vessel traffic service (VTS) system on the Cai Mep - Thi Vai channel

This study addresses the limitations of the current Vessel Traffic Service (VTS) system in monitoring and ensuring navigational safety on the Cai Mep-Thi Vai channel, a crucial waterway in Vietnam's maritime transport system. This research aims to propose solutions to improve the target detection capability of the VTS system in this area. Through surveys and analysis of the current status, this study identifies the system's shortcomings and proposes solutions such as installing additional radar stations and cameras. The proposed solution offers a systematic approach to enhance the effectiveness of the VTS system in managing and monitoring maritime traffic on the Cai Mep-Thi Vai channel, thereby contributing to navigational safety and security. This study provides feasible solutions that can be implemented in practice to address the limitations of the current VTS system.

A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU

With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.

Leverage rule-based Bayesian network on assessing straits/canals resilience performance on a risk coupling analysis perspective

ABSTRACT The maritime transportation system is comprised of several crucial nodes, including straits and canals, which are becoming increasingly susceptible to external emergencies and disturbances. The intricate interactive relationships and non-linear influences among the risk factors pose significant challenges in assessing the resilience performance of these straits and canals. To address these challenges, a four-stage resilience assessment framework was developed to analyze the resilience performance of the straits and canals under uncertain risk coupling scenarios. The framework leverages the advantages of fuzzy logic and Bayesian network, enabling the construction of conditional probability tables and reducing expert judgement inconsistencies. The findings indicate that the Strait of Malacca exhibits higher resilience performance under multi-dimensional risk coupling scenarios. This framework has significant potential in providing valuable insights for risk and resilience management and can be extended to similar industries.

Empowering 6G maritime communications with distributed intelligence and over-the-air model sharing

Introduction: Shipping and maritime transportation have gradually gained a key role in worldwide economical strategies and modern business models. The realization of Smart Shipping (SMS) powered by advanced 6G communication networks, as well as innovative Machine Learning (ML) solutions, has recently become the focal point in the maritime sector. However, conventional centralized learning schemes are unsuitable in the maritime domain, due to considerable data communication overhead, stringent energy constraints, increased transmission failures in the harsh propagation environment, as well as data privacy concerns.Methods: To overcome these challenges, we propose the joint adoption of Federated Learning (FL) principles and the utilization of the Over-the-Air computation (AirComp) wireless transmission framework. Thus, this paper initially describes the mathematical considerations of a 6G maritime communication system, focusing on the heterogeneity of the relevant nodes and the channel models, including an Unmanned Aerial Vehicle (UAV)-aided relaying model that is usually required in maritime communications. The communication network, enhanced with the AirComp technique for efficiency purposes, forms the technical basis for the collaborative learning across multiple Internet of Maritime Things (IoMT) nodes in FL tasks. The workflow of the FL/AirComp scheme is illustrated and proposed as a communication-efficient and privacy-aware SMS framework, considering spectrum and energy efficiency aspects under a sum transmitting power constraint.Results: Then, the performance of the proposed methodology is assessed in an important ML task, related to intelligent maritime transportation systems, namely, the prediction of the Cargo Ship Propulsion Power using real data originating from six cargo ships and utilizing long-short-term-memory (LSTM) neural networks. Upon extensive experimentation, FL showed higher prediction accuracy relative to the typical Ensemble Learning technique by a factor of 3.04. The AirComp system performance was evaluated under varying noise conditions and number of IoMT nodes, using simulation data for the channel state information by regulating the power of the transmitting IoMT entities and the scaling factor at the shore base station.Discussion: The results clearly indicate the efficiency of the proposed FL/AirComp scheme in achieving low computation error, collaborative learning, spectrum efficiency and privacy protection in wireless maritime communications, while providing adequate accuracy levels with respect to the optimization objective.

A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system

A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system

Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

Pricing competition in maritime transportation with blockchain technology and empty container repositioning

A duopoly maritime transportation system with two ocean carriers providing cargo transport services between two sea ports is investigated in this paper. Pricing game models are developed to describe the competitive relationship between the two ocean carriers with/without blockchain adoption. Empty container repositioning is characterized in the models. Through model analysis, the pricing equilibria are derived. Using theoretical analysis and numerical studies, we find that (1) only when the fixed cost of blockchain is low do carriers adopt blockchain technology; (2) regardless of whether or not the competitor adopts blockchain technology, the carrier is better off by adopting it; (3) when both participants choose to adopt blockchain technology, the impact of blockchain on actual demand is not always positive unless the original operational costs are relatively high; and (4) sometimes, empty containers caused by demand imbalance in the two transportation directions are helpful in increasing the carrier’s profit.

Особливості сучасного управління судноплавною компанією

Methods of planning and management in the maritime transport system require a reflection of the peculiarities of the activities of subsystems of the maritime transport industry. In this regard, planning of investment activities should be considered as a guarantee of maintaining or expanding the company's position in the global trade market. An important aspect in the development of maritime transport enterprises is the establishment of effective partnerships with other companies and the development of intermodal transportation systems. This requires the introduction of innovative technologies that increase the efficiency of logistics processes and reduce the time and cost of cargo transportation. The creation of strategic alliances between companies is also important for building competitive advantages and expanding the geographical coverage of cargo transportation.

A Systematic Literature Review of Maritime Transportation Safety Management

Maritime transportation plays a critical role in global trade, and studies on maritime transportation safety management are of great significance to the sustainable development of the maritime industry. Consequently, there has been an increasing trend recently in studies on maritime transportation safety management, especially in terms of safety risk analysis and emergency management. Therefore, the general idea of this article is to provide a detailed literature review of maritime transportation safety management based on 186 articles in the Web of Science (WOS) database published from 2011 to 2022. The purposes of this article are as follows: (1) to provide a statistics-based description and conduct a network-based bibliometric analysis on the basis of the collected articles; (2) to summarize the methodologies/technologies employed in maritime transportation safety management spatiotemporally; and (3) to propose four potential research perspectives in terms of maritime transportation safety management. Based on the findings and insights obtained from the bibliometric and systematic review, the development of a resilient maritime transportation system could be facilitated by means of data- or intelligence-driven technologies, such as scenario representation, digital twinning, and data simulation. In addition, the issues facing intelligent maritime shipping greatly challenge the current maritime safety management system due to the co-existence of intelligent and non-intelligent maritime operation.

Joint Design of Channel Estimation and Flocking Control for Multi-AUV-Based Maritime Transportation Systems

Joint Design of Channel Estimation and Flocking Control for Multi-AUV-Based Maritime Transportation Systems

Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

Design and Manufacture Ballast Management System Model for Reduce Ship Rolling Motion

The safety of maritime transportation is a critical aspect that must be addressed to ensure the well-being of ships and their crew. Frequent ship accidents highlight the need for improvements in the maritime transportation system. One of the causes of ship accidents is ship instability, leading to a loss of balance and even sinking. Ship stability is influenced by both internal and external factors, including human negligence in observing and addressing ship instability. This research aims to design and create a management ballast system model that can be operated automatically as a solution to reduce ship rolling motion. This system enables the ship to maintain balance using automatic side ballast tank management by utilizing two wing tanks on either side of the ship. The ballast management system will be equipped with an accurate ship roll angle detector, the Initial Measurement Unit sensor, a microcontroller, and a series of actuators, including relays as voltage control switches for the pump motor. This research involves simulation and testing/experiments at various angles, namely 5, 10, and 15 degrees. Simulations are conducted under conditions with and without the ballast management system, which is then confirmed through experiments under the same conditions. The expected outcome of this research is that the created ballast management system can be used to reduce ship rolling.

Building multi-system nexuses in low-carbon transitions: Conflicts and asymmetric adjustments in Norwegian ferry electrification.

While nexus research in sustainability science has investigated the consequences of connected systems, it has paid less attention to the processes of building nexuses which is becoming increasingly important in low-carbon transitions because these often require the creation of new connections between multiple consumption-production systems. Building on multi-system research in the sustainability transitions literature, this paper introduces a conceptual system interface perspective on nexus-building which considers four dimensions (technology, actors, institutions, and resources) that are useful for analyzing nexus-building dynamics. We apply our framework to the case of electrification of ferries in Norway which requires the building of a new interface between the electricity system and the maritime transport system. The case study shows that the system interface was initially characterized by conflicts and tensions in all dimensions, which actors then attempted to resolve through cross-system intermediation and adjustment activities. These activities were asymmetrical because of differences in external pressures, urgency, unequal power relationships, and different degrees of interest in cross-system nexus building. Because important tensions remained unresolved, ferry actors started implementing sub-optimal workaround solutions in the diffusion phase.

Neighborhood Rough Residual Network–Based Outlier Detection Method in IoT-Enabled Maritime Transportation Systems

Outlier detection can identify anomalies in large-scale data. To provide reliability and security for Internet of Things (IoT)-enabled maritime transportation systems (MTSs), in this paper we propose an outlier detection method based on the neighborhood rough residual network (NRRN). We calculate the neighborhood approximation accuracy and neighborhood conditional entropy to obtain the neighborhood combined entropy describing the discrimination ability of the condition attribute subset to the information system. We then delete the redundant attributes according to the attribute combination importance derived from the neighborhood combined entropy. The data after attribute reduction are used to train the convolutional neural network, and the residual network (ResNet50) is used to avoid the degradation of model performance caused by the increase in the number of network layers. The proposed method is compared with mainstream outlier detection algorithms on a fishing vessel operation dataset. Experiments show that the proposed method can greatly improve the accuracy of outlier detection while taking into account interpretability and computational efficiency, thereby ensuring the data integrity of IoT-enabled MTSs.

Comprehensive cyber security for port and harbor ecosystems

Global maritime transportation and logistics systems are essential parts of critical infrastructures in every society, and a crucial part of maritime logistics processes are seaports. In the coming years, digitalization and increased levels of autonomy in logistic transport chains are expected to take leaps forward. This development can help create safer, more efficient, more sustainable, and more reliable service chains to meet the requirements for a better quality of life and global prosperity. Port and harbor operations connect the maritime transport to other modes of transportation and enable multimodal transportation. Smart ports play a central role in future transport logistics and supply chains. Digitalization helps improve the efficiency of terminal systems in the processes of these ports. In the best cases, digitalization can also promote the reduction of emissions by optimizing port operations and enhancing cargo and people flows while improving the experience for all stakeholders. The improvement of port processes relies on the development of information and communication technology (ICT) as well as on industrial control systems (ICS) and operation technologies (OT). At the same time, the cyber security of maritime logistics also needs to be addressed. This article presents our findings related to the Sea4Value research goal on cyber security, which is a comprehensive cyber security architecture for port services at the system level. The article emphasizes the importance of a system of systems approach in terms of a comprehensive cyber security management process for port ecosystems. The description and recognition of management steps of every stakeholder are the key elements in this kind of process.