Can We Improve Information Freshness With Predictions in Mobile Crowd-Learning?
The rapid growth of mobile devices has spurred the development of crowd-learning applications, which rely on users to collect, report and share real-time information. A critical factor of crowd-learning is information freshness, which can be measured by a metric called age-of-information (AoI). Moreover, recent advances in machine learning and an abundance of historical data have enabled crowd-learning service providers to make precise predictions on user arrivals, data trends, and other predictable information. These developments lead to a fundamental question: Can we improve information freshness with predictions in mobile crowd-learning?
In this paper, we show that the answer is affirmative. Specifically, motivated by the age-optimal Round-Robin policy, we propose the so-called "periodic equal spreading'' (PES) policy. Under the PES policy, we first reveal a counter-intuitive insight that the frequency of prediction should not be too often in terms of AoI improvement. Further, we analyze the AoI performances of the proposed PES policy and derive upper bounds for the average age under i.i.d. and Markovian arrivals, respectively. In order to evaluate the AoI performance gain of the PES policy, we also derive two closed-form expressions for the average age under uncontrolled i.i.d. and Markovian arrivals, which could be of independent interest. Our results in this paper serve as a first building block towards understanding the role of predictions in mobile crowd-learning.
Committee: Jia Liu (major professor), Hridesh Rajan (major professor), Yan-Bin Jia, and Wensheng Zhang