Optimal and Quantized Mechanism Design for Fresh Data Acquisition

Abstract

The proliferation of real-time applications has spurred much interest in data freshness, captured by the {\it age-of-information} (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an information update system in which a destination acquires, and pays for, fresh data updates from multiple sources. The destination incurs an age-related cost, modeled as a general increasing function of the AoI. Each source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. The destination decides on the price of updates, when to get them, and who should generate them, based on the sources' reported sampling costs. We show that a benchmark that naively trusts the sources' reports can lead to an arbitrarily bad outcome compared to the case where sources truthfully report. To tackle this issue, we design an optimal (economic) mechanism for timely information acquisition following Myerson's seminal work. To this end, our proposed optimal mechanism minimizes the sum of the destination's age-related cost and its payment to the sources, while ensuring that the sources truthfully report their private information and will voluntarily participate in the mechanism. However, finding the optimal mechanisms may suffer from \textit{prohibitively expensive computational overheads} as it involves solving a nonlinear infinite-dimensional optimization problem. We further propose a quantized version of the optimal mechanism that achieves asymptotic optimality, maintains the other economic properties, and enables one to tradeoff between optimality and computational overheads.