Paper Info
Title
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.
Year
DOI
Venue
2019
10.3390/s19122693
SENSORS
Keywords
Field
DocType
localization,fingerprinting,crowdsourcing,equilibrium
Mathematical optimization,Incentive,Iterative method,Crowdsourcing,Electronic engineering,Fingerprint,Robustness (computer science),Engineering,Optimization problem,Maximization,Scalability
Journal
Volume
Issue
ISSN
19
12
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Tao Yu12315.40
Linqing Gui2649.34
Tianxin Yu300.34
Jilong Wang45719.88