Autonomous surveillance vessels are enhancing coastal security through remote operation, reduced crewing requirements and improved mission flexibility. This study combines hull design, stability, resistance, powering and seakeeping analysis to develop a cost-effective unmanned craft capable of operating in challenging conditions while delivering precise manoeuvrability in constrained coastal and canal environments

Fourth Industrial Revolution technologies are reshaping ship design, challenging established engineering practices while unlocking value across the vessel lifecycle. By evaluating practical applications and explicit use cases, new opportunities emerge for smarter decision-making, enhanced efficiency, and digital innovation. The findings reveal where disruptive technologies can deliver meaningful competitive advantage.

Advanced cloud-based analytics are transforming high-speed catamaran monitoring, delivering near real-time insight into wave loads, slamming events and passenger comfort. Combining AWS infrastructure, parallel computing and LSTM machine learning enables scalable hull monitoring as a service. The approach highlights new opportunities for predictive operations, safety and performance at sea today.

Structural digital twins are advancing ship health assessment by combining operational, environmental and condition data with strength and fatigue modelling. Using AIS and weather data extends capability beyond sensor-equipped vessels, enabling virtual hull monitoring, smarter inspections and lifecycle planning. The framework offers scalable decision support for fleet performance, reliability and asset longevity.

Smart Ship Technology is unlocking smarter, more connected maritime operations by integrating expanding IoT networks across the supply chain. A secure approach to linking intelligent vessels, transport systems and infrastructure promises improved efficiency, stronger environmental performance and better data-driven decision-making, while addressing the enduring challenge of reliable system integration.

Artificial intelligence is accelerating autonomous navigation by turning vessel data into accurate trajectory predictions. Leveraging AIS information and LSTM neural networks, the approach strengthens route forecasting, situational awareness and operational efficiency. The results demonstrate how advanced machine learning can support safer, more reliable decision-making for next-generation autonomous and remotely operated vessels.

Autonomous underwater transport is advancing through coordinated AUV systems capable of carrying payloads with flexible connections. By combining nonlinear dynamic modelling with PID and optimal pole placement control, engineers addressed buoyancy, stability and motion challenges. The findings reveal critical trade-offs between power efficiency and control performance in complex underwater operations.

Advancing navigation in ice-covered waters requires more than a single sensor. By combining diverse sensing technologies into an integrated ice sensor suite, operators and autonomous systems can build real-time ice severity maps, calibrate ship-ice interactions and improve route decisions. The approach addresses a key barrier to safer, more capable autonomous Arctic operations.

A proposed data aggregation framework is strengthening navigation in ice-covered waters by combining forecasts, ice sensing technologies and vessel-specific risk models. By transforming diverse data into regional ice severity maps, the approach supports safer route optimization, informed decision-making and future autonomous operations in challenging polar environments.

Digital twins are emerging as a cornerstone of maritime digital transformation, turning real-time sensor data, models and simulations into actionable operational insight. By clarifying their structure, applications and future evolution, the approach highlights how data-driven learning systems can optimize performance, support decision-making and unlock new opportunities across complex assets.

Artificial intelligence is enabling smarter, energy-efficient ship operations through real-time decision support. Using sea-trial data and optimized neural network models, the system accurately predicts fuel consumption across voyage scenarios, supporting weather routing, trim and ballast optimization. The approach demonstrates how data-driven forecasting can improve operational efficiency, sustainability and voyage planning.

Artificial intelligence is enabling smarter, energy-efficient ship operations through real-time decision support. Using sea-trial data and optimized neural network models, the system accurately predicts fuel consumption across voyage scenarios, supporting weather routing, trim and ballast optimization. The approach demonstrates how data-driven forecasting can improve operational efficiency, sustainability and voyage planning.