An Incentive Mechanism for Crowdsensing Markets With Multiple Crowdsourcers
Alireza Chakeri, Luis G. Jaimes · IEEE Internet of Things Journal · 2017
In this paper, we design an incentive mechanism for data collection in smart cities. We propose an incentive mechanism for crowdsensing with multiple crowdsourcers. We model the incentive mechanism as a noncooperative game. We consider two different pricing mechanisms when the crowdsourcers fixed the rewards in advance, and when the crowdsourcers dynamically set the rewards in order to maximize their own utilities. A discrete time dynamic inspired by the well known best response dynamic, called elite strategy dynamics, is proposed to compute a Nash equilibrium of the modeled game. Comprehensive simulations were presented to evaluate the performance of the proposed incentive mechanism.