Truthful Mobile Crowdsensing for Strategic Users With Private Data Quality

Abstract

Mobile crowdsensing has found a variety of applications (e.g., spectrum sensing, environmental monitoring) by leveraging the “wisdom” of a potentially large crowd of mobile users. An important metric of a crowdsensing task is data accuracy, which relies on the data quality of the participating users' data (e.g., users' received SNRs for measuring a transmitter's transmit signal strength). However, the quality of a user can be its private information (which, e.g., may depend on the user's location) that it can manipulate to its own advantage, which can mislead the crowdsensing requester about the knowledge of the data's accuracy. This issue is exacerbated by the fact that the user can also manipulate its effort made in the crowdsensing task, which is a hidden action that could result in the requester having incorrect knowledge of the data's accuracy. In this paper, we devise truthful crowdsensing mechanisms for Quality and Effort Elicitation (QEE), which incentivize strategic users to truthfully reveal their private quality and truthfully make efforts as desired by the requester. The QEE mechanisms achieve the truthful design by overcoming the intricate dependency of a user's data on its private quality and hidden effort. Under the QEE mechanisms, we show that the crowdsensing requester's optimal (RO) effort assignment assigns effort only to the best user that has the smallest “virtual valuation”, which depends on the user's quality and the quality's distribution. We also show that, as the number of users increases, the performance gap between the RO effort assignment and the socially optimal effort assignment decreases, and converges to 0 asymptotically. We further discuss some extensions of the QEE mechanisms. Simulation results demonstrate the truthfulness of the QEE mechanisms and the system efficiency of the RO effort assignment.