Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?

TL;DR

This paper investigates how to maintain fresh information in mobile crowd-learning systems with selfish users, proposing a reward mechanism that optimizes age-of-information and system efficiency.

Contribution

It introduces a linear AoI-based reward mechanism and analyzes its effectiveness, achieving asymptotic optimality and bounded price of anarchy in stochastic scenarios.

Findings

Mechanism achieves asymptotic optimal AoI performance.

Bounded price of anarchy depending on system parameters.

PoA is at most 1/2 in symmetric stochastic cases.

Abstract

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. 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.