Collaborative Intelligence for Joint Optimization of Freshness and Security in RIS-Assisted NTN–TN Networks

In integrated terrestrial network (TN) and non-terrestrial network (NTN) systems, balancing information freshness and communication security is particularly challenging, especially with dynamic and potentially malicious nodes in the network. This work explores the trade-off between age of information (AoI) and age of leaked information (AoLI) in reconfigurable intelligent surface (RIS)-assisted wireless networks. To address this, a collaborative intelligence-based framework is developed, where hierarchical learning supports the joint optimization of RIS configuration and power allocation under changing network conditions. The proposed approach is evaluated against both conventional optimization methods and modern machine learning (ML)-based techniques, showing clear improvements in managing the AoI-AoLI trade-off. The results also point to the value of adaptive and distributed decision-making in handling the complexity of TN-NTN environments. Finally, the study discusses open challenges and future directions, including the use of integrated sensing to improve network awareness, which could further strengthen the design of secure and intelligent 6G communication systems.