K‐submodular function‐based service‐limit incentive mechanisms for crowd heterogeneous sensing

Abstract

SummaryRecently, crowd sensing, as a new paradigm, uses mobile devices from users to efficiently fulfill allocated tasks, enabling many novel applications such as location sensing and air pollution monitoring. To achieve high‐quality service, extensive user participation is crucial. Most of existing works only apply for the homogeneous task scene where the requester has a budget limit and types of sensors used are homogeneous. On the contrary, we investigate a different scenario where the platform has a service limit and types of sensors are heterogeneous. Specially, we design the two service‐limit incentive mechanisms, called SCH and SWH, respectively, by minimizing the total cost for a more general case where the value function is monotone ‐submodular for participatory users so that given services can be fulfilled. The two mechanisms make it possible to extend current small‐scale crowd sensing to large‐scale crowd sensing, which have the following properties: individual rationality, task feasibility, computational efficiency, truthfulness, and constant frugality. Finally, we use extensive simulations to validate theoretical properties of our mechanisms.