This project has completed the research and dissemination work planned for the first period. The research work has led to fruitful outcomes, dissemination events, and impactful results. Specifically,
WP1 focused on the project management and impact. The consortium has completed two annual plenary meetings and organized two focused workshops, as well as delivered numerous conference presentations, keynote speeches and exploitation events.
WP2 completed two use case studies focusing on landslide monitoring and oil pipeline monitoring, respectively. For monitoring landslide disasters and ensuring the safety of oil pipelines, IoT networks are highly effective and valuable for their abilities to provide comprehensive, efficient, and real-time data collection, transmission, processing, and analysis. WP2 designed a software-defined aerial-terrestrial network architecture and completed the definition of the functional components.
WP3 reviewed the energy needs in IoT networks for environmental monitoring, identified the key limitations and QoS requirements, and developed an effective machine learning algorithm to achieve dynamic power splitting, aimed at optimizing the power ratio for energy harvesting and traffic scheduling.
WP4 designed a lightweight AI model and online learning methods, including a streamlined object detector based on Convolutional Neural Networks (CNN) for embedded systems.
WP5 completed an exploratory analysis on IoT trust management techniques for security and reliability of interconnected devices. An effective approach for network management and anomaly detection was further developed for the IoT networks.
