Yu Tao, Gui Linqing, Yu Tianxin, Wang Jilong
Institute of Network Science and Cyberspace, Tsinghua University, Beijing 100084, China.
Department of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2019 Jun 14;19(12):2693. doi: 10.3390/s19122693.
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.
移动众包已被用于收集足够的指纹以进行基于指纹的定位。由于构建指纹数据库耗时，因此应充分激励移动用户参与指纹收集任务。为此，本文提出了一种基于瓦尔拉斯均衡的激励机制来激励移动用户。所提出的机制可以消除众包商的垄断，平衡指纹数据的供需，并使所有参与者的利益最大化。为了达到瓦尔拉斯均衡，首先构建社会福利最大化问题。为了解决原始优化问题，采用了对偶分解方法。社会福利最大化被分解为众包商、移动用户和整个系统之间的三重效益优化。相应地，设计了一种分布式迭代算法。通过仿真，对所提出的激励方案的性能进行了验证和分析。仿真结果表明，所提出的迭代算法满足收敛性和最优性。此外，还验证了所提出的激励方案的自我重构能力，表明该系统具有很强的鲁棒性和可扩展性。
