The recent developments in the research area highlight a significant push towards enhancing autonomy and efficiency in various sectors, including maritime operations, industrial networks, and communication technologies. A notable trend is the application of advanced data-driven methodologies and machine learning techniques to improve the autonomy of Unmanned Surface Vehicles (USVs) in complex maritime environments. These approaches aim to replicate expert maneuvers with high precision, despite environmental perturbations. In the realm of communication technologies, there's a growing emphasis on improving the performance and reliability of Delay- and Disruption-tolerant Networking (DTN) through the development of comprehensive evaluation frameworks. These frameworks aim to address the challenges of intermittent connectivity and ensure high performance in challenging environments. Additionally, the integration of 5G with Time-Sensitive Networking (TSN) is being explored to meet the stringent Quality of Service (QoS) requirements of industrial applications, particularly in indoor factory settings. This integration promises to deliver high data rates, ultra-low latency, and minimal jitter, essential for latency-sensitive applications. The research also underscores the importance of adopting a multi-stakeholder perspective in the development of self-managing networks to address the complexity and diverse needs of modern telecommunication networks. Furthermore, the maritime industry is focusing on the validation of physics-based ship motion prediction models to enhance operational efficiency and safety, crucial for the industry's sustainable future.

Noteworthy Papers

• Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles: Introduces a data-driven learning and control methodology for USVs, capable of learning complex trajectories and ensuring convergence towards targets with robust control.
• DTN-COMET: A Comprehensive Operational Metrics Evaluation Toolkit for DTN: Proposes a novel framework for reproducible performance evaluation of DTN implementations, validated through physical testbeds and comparative analysis.
• A Multi-Stakeholder Perspective on Self-Managing Networks: Advocates for a transition from a single-stakeholder to a multi-stakeholder perspective in the development of self-managing networks, enhancing performance metrics.
• Comparative Performance Evaluation of 5G-TSN Applications in Indoor Factory Environments: Evaluates the performance of 5G-TSN for indoor factory applications, demonstrating its effectiveness in addressing latency-sensitive scenarios.
• Scalability Analysis of 5G-TSN Applications in Indoor Factory Settings: Assesses the scalability of 5G-TSN for various indoor factory applications, showing potential for bounded delay in scalable settings.
• IoT Performance for Maritime Passenger Evacuation: Explores the impact of IoT and ICT systems on maritime passenger evacuation, highlighting the need for robust and fast systems.
• Towards Real-World Validation of a Physics-Based Ship Motion Prediction Model: Presents a physics-based 3D dynamics motion model for container-ships, validated against real-world voyages, aligning closely with actual trajectories.