Haberleşme ağlarında bilgi yaşı farkında çizelgeleme

In next-generation communication networks, various types of data traffic with different requirements will coexist. This type of coexistence requires new techniques based on new metrics to distinguish the various types of data traffic based on their value. The emerging metric Age of Information (AoI) quantifying the timeliness of the communication flow is a promising metric to prepare future networks for new technologies such as the Internet of Things and Autonomous Driving. In this thesis, we studied scheduling problems with the objective of minimizing AoI on different network models. With the help of the results obtained from the theoretical study of LCFS queues using the Stochastic Hybrid System method, we propose a prominent scheduling algorithm called Maximum Age Difference, which is useful for optimizing the network in terms of AoI. Additionally, we approached the age-minimizing scheduling problem with Reinforcement Learning-based methods, which is advantageous when the network statistics are unknown.