Achieving Information Freshness With Selfish and Rational Users in Mobile Crowd-Learning
Achieving Information Freshness With Selfish and Rational Users in Mobile Crowd-Learning
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DOI：
10.1109/jsac.2021.3065092
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发表时间：
2021-05
影响因子：
16.4
通讯作者：
Bin Li;Jia Liu
中科院分区：
文献类型：
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作者：
Bin Li;Jia Liu
智能移动设备的普及刺激了移动群体学习服务的爆炸式增长，服务提供商依靠用户社区自愿收集、报告和共享分散的兴趣点（PoI）的实时信息。影响此类移动群体学习应用未来大规模采用的一个关键因素是群体学习信息的新鲜度，这可以通过称为“信息年龄”（age-of-information, AoI）的度量来衡量。然而，我们表明，如果系统设计不当，在自私和理性的用户行为下，移动人群学习的AoI可能会任意差。这促使我们设计有效的奖励机制，激励移动用户及时报告信息，目标是保持每个PoI的AoI和拥塞水平较低。为此，我们考虑了一个简单的基于AoI的线性奖励机制，并从无政府状态价格（PoA）的角度分析了其AoI和拥塞性能，PoA表征了由于用户的自私和理性行为而导致的系统效率下降。在本文中，我们考虑了平均最大年龄和平均加权年龄之和。值得注意的是，我们表明，在确定性场景下，该机制在平均最大年龄方面渐近地实现了最优的AoI性能，即相应的PoA渐近地减小到0。此外，在我们提出的机制下，平均总年龄的PoA可以渐近上界为1/2。进一步证明了该机制在一般随机情况下实现有界PoA，且该界仅与系统参数有关。特别地，当poi的服务率在随机情况下对称时，所得到的PoA渐近上界为1/2。总的来说，这项工作促进了我们对移动人群学习系统中信息新鲜度的理解。
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 and rational 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 to keep 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 and rational behavior of users. In this paper, we consider both average maximum age and average weighted sum of age. Remarkably, we show that the proposed mechanism achieves the optimal AoI performance in terms of average maximum age asymptotically in a deterministic scenario, i.e., the corresponding PoA decreases to 0 asymptotically. Moreover, the PoA in terms of average total age under our proposed mechanism can be upper-bounded by 1/2 asymptotically. 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.