The next generation of shipping industry, namely Shipping 4.0 will integrate advanced automation and digitization technologies towards revolutionizing the maritime industry. As conventional maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL), a decentralized ML approach, enables collaborative model training across multiple distributed edge devices, such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More specifically, we present the main FL principles, the proposed workflow and then, we evaluate and compare various FL algorithms in three maritime use cases, i.e. regression to predict the naval propulsion gas turbine (GT) measures, classification to predict the ship engine condition, and time-series regression to predict ship fuel consumption. The efficiency of the proposed FL-based PdM highlights its ability to improve maintenance decision-making, reduce downtime in the shipping industry, and enhance the operational efficiency of shipping fleets. The findings of this study support the advancement of PdM methodologies in Shipping 4.0, providing valuable insights for maritime stakeholders to adopt FL, as a viable and privacy-preserving solution, facilitating model sharing in the shipping industry and fostering collaboration opportunities among them.

Sixth generation (6G) networks must adopt spectral-efficient communication techniques to ensure massive connectivity for coexisting humans and machines. However, the impact of various practical issues must be analyzed and addressed, including imperfect channel state information (CSI), stemming by the channel estimation error (CEE) and feedback delay (F-D) with time-variant channels. This paper focuses on these issues in the context of uplink networks, relying on power-domain non-orthogonal multiple access (NOMA). Moreover, the degrading effect of imperfect successive interference cancellation (SIC), when randomly deployed multiple mobile terminals communicate with a single base station (BS) is considered. The system performance is measured by means of outage probability, error probability, ergodic rate, throughput, energy efficiency, and spectral efficiency. Analytical, asymptotic, and computer simulation results demonstrate that CEE causes system coding gain losses for low signal-to-noise ratio (SNR) while the disruptive effects of CEE become negligible in the high SNR. Results also show that F-D does not degrade the system performance in the low SNR but it causes system coding gain losses for high SNR. Also, imperfect SIC does not have any detrimental effect on the system performance for low SNR but results in reduced coding gain for high SNR.Â

A document by Volkan Ozduran . Click on the document to view its contents.

In this study, we focus on an MCN where the direct links towards a shore BS are not available, due to excessive fading conditions. For this case, we use a UAV swarm to provide improved wireless connectivity, adopting non-orthogonal multiple access (NOMA) for high resource efficiency. In downlink communication, UAVs take into consideration the desired service rate and the channel quality of their links towards the maritime nodes. In the uplink, UAVs employ dynamic decoding ordering to enhance the performance of successive interference cancellation, avoiding fixed ordering of the maritime nodesâ\euro™ signals. Moreover, to ensure highly flexible UAV selection, UAVs have buffers to store data. Performance comparisons show that the UAV swarm-aided MCN enjoys increased average sum-rate by relying on multi-criteria-based interference cancellation and buffer-aided UAVs, over other benchmark schemes in the downlink and uplink.

Maritime transportation is vital for economic growth, since it is responsible for the vast majority of global trade. However, optimizing maritime transportation, focusing on certain performance metrics may lead to non-convex problems due to the large number and heterogeneity of network nodes and vessels. Furthermore, the harsh propagation environment, and the long propagation distances might be prohibitive for the implementation of conventional optimization. Machine learning (ML) represents a viable way towards complexity minimization but still, it might not be feasible to fully exploit its potential, since error-free feedback channels are usually assumed while the overall centralized processing delay from numerous distributed sources might render real-time deployment infeasible, due to stringent latency requirements. Meanwhile, security and privacy concerns constitute key driving factors for decentralized ML solutions, since data locality is vital to protect sensitive information. Taking into consideration all the above, this paper discusses feasibility issues, regarding the deployment of federated learning (FL) solutions in maritime environments, via the presentation and analysis of various use cases. Moreover, experimental results using datasets from an enterprise specializing in the maritime industry are provided, showing the superiority of FL over traditional ML approaches, in terms of accuracy and complexity. Finally, open issues that must be addressed to pave the way for the wide adoption of FL in maritime applications are discussed.