Fairness-based multi-task reward allocation in mobile crowdsourcing system

Mobile crowdsourcing-based applications, widely popular, exploit the sensing data crowdsourced from smartphone users without putting any burden on the extra cost of data sensing and collection. However, user participation in crowdsourcing incurs resource cost, such as battery, bandwidth, thus it is critical to design incentive mechanisms for propelling user's participation. Previous diverse incentive mechanisms designed for crowdsourcing applications only focus on users' contribution for reward allocation, while ignore another important property, i.e. fairness, users' reward should be corresponding with their cost. In this study, the authors first introduce a new concept called rate of return (RoR), defined as the ratio of received reward and incurred cost for each user, to demonstrate the property of fairness. With the goal of guarantee, the fairness of reward allocation for each user in a multiple-task system, three algorithms, consensus-based reward allocation, consensus-based balanced topology reward allocation and Gossip-based reward allocation are proposed for the demands of various scenarios, in which the RoR values are synchronised by optimising the fairness function in either centralised or decentralised manner. Through rigorous theoretical analysis and extensive simulations, it is finally demonstrated that the proposed reward allocation algorithms have the good property of fairness with quick convergence.