The proliferation of smart mobile devices has spurred an explosive growth of mobile crowd-learning services, where service providers rely on the user community to voluntarily collect, report, and share real-time information for a collection of scattered points of interest (PoI). A critical factor affecting the future large-scale adoption of such mobile crowd-learning applications is the freshness of the crowd-learned information, which can be measured by a metric termed ``age-of-information'' (AoI). However, we show that the AoI of mobile crowd-learning could be arbitrarily bad under selfish users' behaviors if the system is poorly designed. This motivates us to design efficient reward mechanisms to incentivize mobile users to report information in time, with the goal of keeping the AoI and congestion level of each PoI low. Toward this end, we consider a simple linear AoI-based reward mechanism and analyze its AoI and congestion performances in terms of price of anarchy (PoA), which characterizes the degradation of the system efficiency due to selfish behavior of users. Remarkably, we show that the proposed mechanism achieves the optimal AoI performance asymptotically in a deterministic scenario. Further, we prove that the proposed mechanism achieves a bounded PoA in general stochastic cases, and the bound only depends on system parameters. Particularly, when the service rates of PoIs are symmetric in stochastic cases, the achieved PoA is upper-bounded by 1/2 asymptotically. Collectively, this work advances our understanding of information freshness in mobile crowd-learning systems.

Jia (Kevin) Liu is currently an Assistant Professor in the Dept. of Computer Science and Dept. of Electrical and Computer Engineering (by courtesy) at Iowa State University, where he joined in Aug. 2017. He received his Ph.D. degree from the Bradley Dept. of Electrical and Computer Engineering at Virginia Tech in 2010. He was a Postdoctoral Researcher and subsequently a Research Assistant Professor from Feb. 2010 to Jul. 2017, both in the Dept. of Electrical and Computer Engineering at The Ohio State University. His research areas include theoretical foundations of control and optimization for stochastic networked systems, distributed algorithms design, optimization of cyber-physical systems, data analytics infrastructure, and machine learning. Dr. Liu is a senior member of IEEE and a member of ACM. His work has received numerous awards at top venues, including IEEE INFOCOM'19 Best Paper Award, IEEE INFOCOM'16 Best Paper Award, IEEE INFOCOM'13 Best Paper Runner-up Award, IEEE INFOCOM'11 Best Paper Runner-up Award, and IEEE ICC'08 Best Paper Award. He is a recipient of Bell Labs President Gold Award in 2001. His research has been supported by NSF, AFOSR, AFRL, and ONR.