Classical network scheduling problems have primarily focused on optimizing metrics, such as delay, which pertain to the service provided to individual packets in the network. However, in modern applications like tele-robotics and networked cars, the emphasis is on metrics that capture the freshness of information, specifically, how up-to-date the information is at the receiver (monitor) compared to the transmitter (source). Thus, several metrics have been introduced to quantify information freshness, the most widely used one being the age of information (AoI). The AoI for a source at any given time is equal to the difference between the current time and the generation time of the most recent packet (update) received at the monitor. For modern applications, the scheduling objective is to minimize the AoI for the sources in an online environment, where only causal information is available at any time.
A critical feature of AoI scheduling problems is that not all updates generated at a source need to be transmitted. Depending on the network model, a scheduling algorithm (policy) must choose a subset of updates to transmit. This characteristic gives AoI scheduling problems a combinatorial flavour, making them fundamentally different and analytically challenging compared to classical scheduling problems. In the PhD dissertation, we have addressed some major challenges in AoI scheduling for generic network models, taking into consideration energy consumption, shared transmission link, and the type of scheduler (centralized/decentralized). We have analyzed the various trade-offs involved in decision-making and proposed novel causal algorithms (policies) that strategically handle these trade-offs. Additionally, using analytical techniques, we have derived theoretical performance guarantees, ensuring the efficiency and robustness of the proposed solutions. In this seminar, we will have a comprehensive overview of the above contributions, with all the relevant background.