From Freshness to Effectiveness : Goal-Oriented Sampling for Remote Decision Making

Data freshness, measured by Age of Information (AoI), is highly relevant in networked applications such as Vehicle to Everything (V2X), smart health systems, and Industrial Internet of Things (IIoT). However, freshness alone does not always equate to utility in decision-making. In decision-critical settings, somestaledata may be more valuable thanfreshupdates. Motivated by this, we move beyond AoI-centric policies and investigate how datastalenessaffects remote decision-making effectiveness under random delay and limited communication resources. To this end, we propose AR-MDP, an Age-aware Remote Markov Decision Process framework, which co-designs optimal sampling and remote decision-making under a sampling frequency constraint and random delay. To efficiently solve this problem, we design a newtwo-stagehierarchical algorithm, namely Quick Bellman-Linear-Program (QUICKBLP), where the first stage involves solving the Dinkelbach root of a Bellman variant and the second stage involves solving a streamlined linear program (LP). For the tricky first stage, we propose a new One-layer Primal-Dinkelbach Synchronous Iteration (ONEPDSI) method, which overcomes there-convergenceandnon-expansive divergencepresent in existingper-samplemulti-layer algorithms. Through rigorous convergence analysis of our proposed algorithms, we establish that the worst-case optimality gap in ONEPDSI exhibits exponential decay with respect to iterationKat a rate ofO( 1/RK). Throughsensitivity analysis, we derive a threshold for the sampling frequency, beyond which additional sampling does not yield further gains in decision-making. Simulation results validate our analyses.