Incentive mechanism design for crowdsourcing-based indoor localization

Wi-Fi-based indoor localization system needs to construct a radio map by site surveys. The process of site surveys is time-consuming and crowdsourcing is one feasible option to tackle this issue. Meanwhile, privacy protection has drawn concerns from both industry and academia. In this paper, we propose two incentive mechanisms to stimulate mobile users (MUs) to contribute indoor trajectory data for crowdsourcing-based indoor localization with differential privacy to prevent MUs' privacy leakage. The first mechanism considers fixed reward for MUs and incomplete information, where each MU's sensitivity level of the data privacy is unknown to the crowdsourcing platform (CP). The interaction between MUs and CP is formulated into a two-stage Stackelberg game to maximize MUs' utility and CP's profit. The second mechanism jointly considers the variable reward for MUs and assumes CP knows each MUs' sensitivity level of the data privacy. A demand function is used to model the relationship among CP, MUs, and service customer. The optimization problem of maximizing CP's profit is studied to show the impact of the price fluctuation. Comprehensive simulations are presented to evaluate the performance of the proposed mechanisms and show some insights of the crowdsourced indoor localization incentive mechanism with privacy protection.