Incentivizing Fresh Information Acquisition via Age-Based Reward

Many Internet platforms are information-oriented and crowd-based. They collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer rewards to incentivize users and compensate their costs incurred from information acquisition. In practice, a user’s cost is his/her private information, thus both the user cost and its distribution are hidden to the platform, making it challenging to determine the optimal rewarding decision. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with bandit feedback. To overcome the challenge, we first design an age-based reward scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based reward scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived from the cost distribution). Simulation results show that the age-based reward scheme protects the platform’s OpEx from the influence of the user crowd characteristics, and also verify the asymptotic optimality of the EDAL policy.