Raul Land

Protecting underwater infrastructure and ensuring marine environmental safety in the Baltic Sea are crucial due to rising threats such as subsea cable damage, anchor incidents, and shipwreck-related pollution. This research integrates autonomous underwater sensor networks, advanced acoustic sensing, and artificial intelligence for enhanced maritime security and environmental monitoring. Acoustic arrays assess shipwreck conditions, including hull integrity, corrosion, and fuel presence, using advanced signal processing. Sensor nodes equipped with hydrophones continuously monitor underwater acoustic signals, detecting and classifying anthropogenic and natural sources. Machine learning-driven signal processing enables real-time risk assessment, anomaly detection, and communication with surface units. Expected outcomes include improved hazard detection, more efficient pollution monitoring, and autonomous decision-making, strengthening marine conservation and security in the Baltic Sea.

A platform for monitoring energy usage and operational state properties of distribution networks is elaborated, enabling users of distribution networks to access relevant and better time-resolution data to support green energy usage, energy efficiency and the flexibility technologies. The platform being developed will provide extensive support for decision-making and calculation algorithms, enabling efficient availability and management of local area operational data.