TY - GEN
T1 - Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?
AU - Li, Bin
AU - Liu, Jia
N1 - Funding Information:
Authors are listed in alphabetical order. Both authors are primary authors. This work has been supported in part by NSF grants CNS-1717108, CNS-1815563, ECCS-1818791, CCF-1758736, CNS-1758757, CNS-1446582; ONR grant N00014-17-1-2417, AFRL grant FA8750-18-1-0107.
Publisher Copyright:
© 2019 IFIP.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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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U2 - 10.23919/WiOPT47501.2019.9144128
DO - 10.23919/WiOPT47501.2019.9144128
M3 - Conference contribution
AN - SCOPUS:85091537236
T3 - Proceedings - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
BT - Proceedings - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
A2 - de Pelligrini, Francesco
A2 - de Pelligrini, Francesco
A2 - Saad, Walid
A2 - Tan, Chee Wei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
Y2 - 3 June 2019 through 7 June 2019
ER -