Data-Driven Pricing for Sensing Effort Elicitation in Mobile Crowd Sensing Systems
IEEE/ACM Transactions on Networking(2019)
摘要
The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers’ noisy or even conflicting sensory data,
truth discovery algorithms
, which jointly estimate workers’ data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers’
strategic reduction of their sensing effort
is properly tackled. To address this issue, in this paper, we propose a
payment mechanism
, named Theseus, that deals with workers’ such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the
Bayesian Nash Equilibrium
of the
non-cooperative game
induced by Theseus, all participating workers will spend their
maximum possible effort
on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers’ data will be highly accurate. Additionally, Theseus bears other desirable properties, including
individual rationality
and
budget feasibility
. We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations.
更多查看译文
关键词
Sensors,Task analysis,Games,Reliability,Noise measurement,Data integrity,Data models
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要