This research develops machine learning techniques to improve fibre-optic access networks, enabling faster, more reliable, and lower-cost internet connections. By allowing transmitters to adapt using receiver feedback, it reduces signal distortion, equipment complexity, and power consumption, helping build resilient communication networks capable of supporting future digital societies.

This research develops privacy-preserving, decentralised AI systems where devices learn collaboratively without sharing raw data. Inspired by natural systems like bee colonies, it enables adaptive, self-organising cooperation among devices. The approach improves performance in heterogeneous environments, such as smart cities, while complying with data protection constraints like GDPR.

This study developed a real-time IoT-based system to optimize fishway performance in fragmented rivers. Using sensors, PIT-tag tracking, and machine-learning models, it links climate triggers with hydraulic controls. Adaptive sluice-gate regulation improved fish passage efficiency by 166% without reducing hydropower output, offering scalable, sustainable river management.

This research explores how to secure low-power Internet of Things devices using physical-layer security. Instead of relying on computational cryptography, it harnesses randomness in wireless communication channels to achieve strong or even perfect security. As 5G expands device numbers, understanding these mathematical limits is essential for protecting future networks.