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Truthful Quality-Aware Data Crowdsensing for Machine Learning

Crowdsensing has found a broad range of applications (e.g., spectrum sensing, environmental monitoring) by leveraging the "wisdom" of a potentially large crowd of "workers" (i.e., mobile users). One important class of applications use the data collected from crowdsensing for data analytics via machine learning (e.g., for wireless indoor localization). To exploit the potential of crowdsensing for machine learning, it is beneficial for the crowdsensing requester to know and make use of the quality of worker’s data. In this paper, based on a general linear regression model of machine learning, we devise truthful quality-aware crowdsensing mechanisms for quality and effort elicitation, which incentivize workers to truthfully report their private worker quality to the requester, and make effort as desired by the requester. The truthful design of the mechanisms overcomes the differences of ground truths of workers’ tasks, and the coupling in the joint elicitation of workers’ quality, effort, and data. Under the mechanisms, we investigated the socially optimal and the requester’s optimal effort assignments, and analyze their performance. We show that the requester’s optimal assignment is determined by the "virtual quality" rather than the highest quality among workers, which depends on the worker’s quality and the quality’s distribution. Simulation results are provided which demonstrate the truthfulness of the mechanisms and the performance of the optimal effort assignments.