Crowdsourcing to smartphones

TLDR

Mobile phone sensing leverages ubiquitous smartphones to collect and analyze data at unprecedented scale, yet current applications lack effective incentive mechanisms to attract sufficient user participation. The study aims to design incentive mechanisms that encourage user participation in mobile phone sensing. We propose two incentive frameworks: a platform‑centric Stackelberg game that maximizes platform utility and a user‑centric auction that is computationally efficient, individually rational, profitable, and truthful, and we validate both through extensive simulations. We demonstrate that the Stackelberg equilibrium uniquely maximizes platform utility and is stable against unilateral user deviations.

Abstract

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.