Truthful mobile crowdsensing for strategic users with private qualities

Abstract

Mobile crowd sensing 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 crowd sensing task is data accuracy, which relies on the qualities 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 crowd sensing 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 crowd sensing 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 crowd sensing mechanisms for Quality and Effort Elicitation (QEE), which incentivize strategic users to truthfully reveal their private qualities 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 crowd sensing requester's optimal (CO) 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 CO effort assignment and the socially optimal effort assignment decreases, and converges to 0 asymptotically. We further show that while the requester's payoff and the social welfare attained by the CO effort assignment both increase as the number of users increases, interestingly, the corresponding users' payoffs can decrease. Simulation results demonstrate the truthfulness of the QEE mechanisms and the system efficiency of the CO effort assignment.