Age and Stability Trade-offs in Remote Monitoring Systems
For IoT system designers, this provides a theoretical framework and optimal scheduling policy for balancing timeliness and stability, though the solution is specific to the modeled MDP formulation.
This work models the trade-off between queue stability and data freshness in IoT systems as a Markov Decision Process, minimizing a weighted sum of average Age of Information and queue length. The optimal policy is a monotonic queue-dependent age threshold, with performance compared against heuristics.
Timely information is important in a wide variety of Internet of Things (IoT) services in which a shared server must manage two competing tasks: (i) processing a queue of jobs, and (ii) generating status updates to a remote monitor. This creates a fundamental trade-off between queue stability and data freshness. In this work, we model this scheduling decision as a Markov Decision Process (MDP) with the objective of minimizing a weighted sum of the average Age of Information (AoI) and the average queue length. We show that the optimal scheduling strategy is a queue-dependent age threshold which is monotonic. The shape of the switching curve differs according to different priority regimes. Finally, we compare the optimal MDP policy against heuristic policies.