Contextual Budget-limited Online Incentives for Lipschitz Sensing Tasks

Recently, mobile crowdsensing (MCS) has received extensive attentions from the single-request scenes to multi-request scenes to solve complex sensing tasks. However, these existing works mainly focus on the issues of traditional pricing incentives, no matter the bidding incentives or the posted-pricing incentives. In practice, users,contextual features have an impor-tant impact on the incentive issues, such as participatory user's personal information, different past sensing demands of allocated tasks, mobile devices,costs used to participating sensing, etc. Moreover, these personal information, sensing demands and costs may vary with time progress. To the end, in this paper we propose two contextual budget-limited pricing mechanisms for crowdsensing applications, where the sensing tasks satisfy the Lipschitz cost function for the contextual features of arrival users. What's more, performance analysis show that our mechanisms satisfy basic properties of crowdsensing. Through extensive simulations, we also evaluate the performance of our proposed mechanisms.