Age-of-Information-Aware Mobile Crowdsensing for Uncertain Event Capture

Mobile CrowdSensing (MCS) is a crowdsourcing-based paradigm that leverages mobile users to collect data from Points of Interest (PoIs) using their smart devices. As data freshness has become a crucial concern, Age of Information (AoI) is employed to measure data freshness for MCS systems. Existing AoI-aware MCS works mainly focus on direct data collection scenarios, where data in PoIs is always available. Unlike these works, this paper explores the AoI-aware MCS system for uncertain event capture applications, where events may occur frequently yet with uncertainty, making the AoI update hard to be estimated. First, we model this uncertain event capture problem as a constrained episodic restless bandit problem with unknown transition probability. Next, we propose a belief-DPP bandit policy by extending the Drift-Plus-Penalty (DPP) policy. By combining belief-DPP and the Thompson Sampling (TS) technique, we further propose the TS-DPP algorithm, so as to minimize the cumulative weighted AoI values of all events under a given budget constraint. We analyze the theoretical performance of the TS-DPP algorithm, and derive a sublinear Bayesian regret bound ${\mathcal{O}}\left( {\sqrt {T\log T} } \right)$, where T is the size of time horizon. Additionally, we conduct extensive simulations to demonstrate the significant performance of the TS-DPP algorithm.