Walrasian Equilibrium-Based Incentive Scheme for Mobile Crowdsourcing Fingerprint Localization

Abstract

Mobile crowdsourcing has been exploited to collect enough fingerprints for fingerprinting-based localization. Since the construction of a fingerprint database is time consuming, mobile users should be well motivated to participate in fingerprint collection task. To this end, a Walrasian equilibrium-based incentive mechanism is proposed in this paper to motivate mobile users. The proposed mechanism can eliminate the monopoly of the crowdsourcer, balance the supply and demand of fingerprint data, and maximize the benefit of all participators. In order to reach the Walrasian equilibrium, firstly, the social welfare maximization problem is constructed. To solve the original optimization problem, a dual decomposition method is employed. The maximization of social welfare is decomposed into the triple benefit optimization among the crowdsourcer, mobile users, and the whole system. Accordingly, a distributed iterative algorithm is designed. Through the simulation, the performance of the proposed incentive scheme is verified and analyzed. Simulation results demonstrated that the proposed iterative algorithm satisfies the convergence and optimality. Moreover, the self-reconstruction ability of the proposed incentive scheme was also verified, indicating that the system has strong robustness and scalability.

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“…Indeed, improvements related to post-collection processes are crucial to achieving better database quality and online localization accuracy; however, the factors that facilitate the data collection process are essential to ensure the applicability and widespread use of such ubiquitous methods. To arouse the interest of smartphone users in participating in the data collection task, Yu, et al [9] proposed an incentive mechanism by leveraging appropriate motivation measures. Attracting users through incentive schemes is vital for the adoption of crowdsourcing approaches.…”