Extending the existing near-shore terrestrial infrastructure with non-terrestrial network capabilities helps maritime operators alleviate the high costs of communication and meet the requirements imposed by time-sensitive applications. Recognizing that the deployment of terrestrial and non-terrestrial networks necessitates selecting from the available wireless backhaul solutions, which have dissimilar data transmission costs and communication link qualities, it is essential to propose an appropriate backhaul selection policy. Specifically, in this letter, we coin a backhaul selection policy that manages the inherent trade-off between data transmission expenses and timely throughput guarantees for maritime communications. We formulate the backhaul selection problem as a Markov decision process and show that the proposed solution is not only more cost-efficient, but also satisfies the timely throughput requirements in contrast to currently used greedy strategies.

Considering the dynamically changing nature of the radio propagation environment, the envisioned battery lifetime of the end device (ED) for massive machine-type communication (mMTC) stands for a critical challenge. As the selected radio technology bounds the battery lifetime, the possibility of choosing among several low-power wide-area (LPWAN) technologies integrated at a single ED may dramatically improve its lifetime. In this paper, we propose a novel approach of battery lifetime extension utilizing reinforcement learning (RL) policies. Notably, the system assesses the radio environment conditions and assigns the appropriate rewards to minimize the overall power consumption and increase reliability. To this aim, we carry out extensive propagation and power measurements campaigns at the city-scale level and then utilize these results for composing real-life use-cases for static and mobile deployments. Our numerical results show that RL-based techniques allow for a noticeable increase in EDs’ battery lifetime when operating in multi-RAT mode. Furthermore, out of all considered schemes, the performance of the weighted average policy shows the most consistent results for both considered deployments. Specifically, all RL policies can achieve 90 % of their maximum gain during the initialization phase for the stationary EDs while utilizing less than 50 messages. Considering the mobile deployment, the improvements in battery lifetime could reach 200 %.

The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient, and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.