Optimal Sampling and Scheduling for Timely Status Updates in Multi-Source Networks

We consider a joint sampling and scheduling problem for optimizing data freshness in multi-source systems. Data is measured by non-decreasing penalty function of age information, where all sources have the same age-penalty function. Sources take turns to generate update packets, forward them their destinations one-by-one through shared channel with random delay. There scheduler, that chooses order sources, sampler, determines when source should new packet its turn. aim find optimal scheduler-sampler pairs minimize total-average at delivery times (Ta-APD) (Ta-AP). prove Maximum Age First (MAF) scheduler zero-wait sampler are jointly minimizing Ta-APD. Meanwhile, MAF relative value iteration reduced complexity (RVI-RC) Ta-AP. The RVI-RC based on algorithm whose exploiting threshold property sampler. Finally, low-complexity threshold-type devised via an approximate analysis Bellman's equation. This reduces simple water-filling linear