Smart Edge Services for the Port Continuum
This project focuses on developing a cloud-edge-IoT continuum platform to support the digital transformation of modern container terminals in line with the Port 4.0 vision. Seaports are critical components of global supply chains, but increasing trade volumes require improved operational efficiency, reduced equipment downtime, and faster handling processes. The project addresses current challenges in port operations, including limited visibility into asset conditions, unexpected equipment failures, and time-consuming manual inspection procedures, by integrating intelligent edge services and advanced data-driven technologies.
A key research direction for the project is the development of predictive maintenance solutions for container-handling equipment (CHE). Machine learning models are developed to analyze equipment health indicators and predict potential failures before they impact terminal operations. The proposed approaches combine statistical health monitoring with advanced ML techniques, including artificial neural networks, decision trees, random forests, XGBoost, and Gaussian Naive Bayes, achieving highly accurate prediction of critical equipment faults and enabling more proactive maintenance strategies.
The project also investigates the use of artificial intelligence for automated container inspection and operational monitoring. Deep learning-based computer vision models are developed for real-time container tracking, container ID detection, damage detection, and seal detection during crane operations using video streams from container terminals. By leveraging YOLO-based object detection models trained on real-world terminal data, the developed system enables accurate container identification and automated detection of structural damage, reducing manual inspection effort and improving safety.
Overall, the project aims to deliver intelligent edge services that enhance the efficiency, reliability, and sustainability of port operations. By combining IoT sensing, edge computing, cloud analytics, predictive maintenance, and AI-based inspection, the developed platform contributes to the evolution of smart ports capable of autonomous decision-making and optimized resource utilization.
