Online Truthful Incentives for Heterogeneous and $K$-Submodular Crowdsensing
Jiajun Sun, Dianliang Wu · IEEE Systems Journal · 2022
Recently, crowdsensing receives extensive attentions to solve complex sensing tasks. However, these existing works mainly focus on the incentive issues of traditional bidding model. In practice, solutions of the incentive issues of the posted-pricing model, are more important for some realistic mobile crowdsensing (MCS) applications. However, only few works focus on the homogeneous MCS applications (each user can perform only a single task). To this end, in this article we propose a posted-pricing incentive mechanism for heterogeneous MCS applications (different users have different limits of the number of assignments). Furthermore, considering more general MCS applications (satisfying$K$-submodular function: the$K$-set functions that are submodular in every type$k\in [K]$), we define the weighted marginal utilities and explore two posted-pricing incentive mechanisms for heterogeneous and$K$-submodular MCS applications, respectively. Moreover, both of the two mechanisms are based not only on heterogeneous or$K$-submodular sensing tasks, but also on heterogeneous sensing types. Finally, performance analysis shows that our mechanisms satisfy the desired properties, such as the truthfulness of acceptance decision, the individual rationality,budget feasibility,computational efficiency, and the regret minimization. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our proposed mechanism.