In recent years, the adoption of innovative technologies in maritime transport and logistics systems has become a key aspect towards their development and growth, especially due to the complex and heterogeneous nature of the maritime environment. On the other hand, Internet of Things (IoT) solutions are gaining importance in the shipping industry thanks to the huge number of distributed cameras and sensors in modern ships, cargoes and sea ports, which can be exploited to improve safety, costs and productivity. This paper presents an experimental evaluation of a maritime platform, which enables a wide range of 5G-based services in the context of logistics and maritime transportation. Its core is a Narrow Band (NB)-IoT framework used to run massive IoT services on top of a hybrid terrestrial–satellite network and feed a OneM2M platform with significant data on maritime transport to develop high-level and value-added logistic applications on top. Among the many different services that could be provided by the maritime platform, we focus on the cargo-ship container tracking use case through the Global Tracking System, which allows for continuous container monitoring all over the seas in a port-to-port service scenario. The results of the experimental tests illustrate the capacity of the platform in managing the high number of messages transmitted by the container tracking devices (i.e., more than 3000) and its efficiency in limiting the average maximum latency and packet loss below $5.5$ s and $0.9$%, respectively.

Network slicing plays a key role in the 5G ecosystem for verticals to introduce new use cases in the industrial sector, i.e., Industry 4.0. However, a widely recognized challenge of network slicing is to provide traffic isolation and concurrently satisfy diverse performance requirements, e.g., bandwidth and latency. Such challenge becomes even more important when serving a large number of network traffic flows under a resource-limited condition between distributed sites, e.g., factory floor and remote office. In this work, we present the capability to retain these two goals at the same time, by applying the virtual queue notion over a priority queuing based pipeline in P4 switch over software-defined networks. To examine the effectiveness of our approach, a proof-of-concept is setup to serve different requests of Industry 4.0 use cases over a mixed data path, including P4 switch and Open vSwitch, for a large number of network flows.

This paper investigates the most prominent lines of optical network control evolution, focusing on software-defined networking (SDN), NETCONF/YANG protocols, telemetry techniques, advancements in packet/optical networking, and the integration of artificial intelligence (AI) within optical networks. We show how the integration of SDN with open modeling frameworks allows to devise hierarchical control models where we trade-off between the segregation of proprietary hardware and the creation of open interfaces like in the OpenSDK scenario. In addition, we depict the convergence of packet and optical layers with advancements in coherent technologies and pervasive telemetry techniques to create new flexible scenarios for controlling optical networks. On top of these approaches, the intent-based networking allows to implement configuration solutions using natural primitives. Finally, key applications of AI, mainly machine learning (ML), including quality-of-transmission estimation, failure prediction, and resource optimization, are analyzed to improve optical network control efficiency alongside their challenges, such as energy efficiency and data scarcity. By addressing advances in the aforementioned areas of research, this work outlines the transformative potential of combining programmability, real-time telemetry, and AI to build resilient, adaptive, and sustainable optical infrastructures for the future.