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 upperbounded by 1/2 asymptotically. Collectively, this work advances
our understanding of information freshness in mobile crowd-learning systems.
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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 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.
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- NSF-PAR ID:
- 10192356
- Date Published:
- Journal Name:
- IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
- Page Range / eLocation ID:
- 702 to 709
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162913
