Incentive Mechanisms for Mobile Crowdsensing With Heterogeneous Sensing Costs

The emerging mobile crowdsensing applications are able to facilitate people's life in various aspects. A key factor to ensure that these applications can provide high-quality service is the sufficient participation of normal smartphone users. Therefore, a lot of effort has been made to design incentive mechanisms to motivate users to participate. Most of these works assume that users are associated with homogeneous costs across the whole sensing area, based on which many utility optimization models are proposed. In this paper, we consider the scenario where smartphone users have heterogeneous costs across the sensing area, i.e., users in different regions have different cost distributions. In this scenario, traditional mechanisms may generate sensing holes and recruit insufficient users in some regions with higher costs, which may lead to unsatisfactory service. To accommodate this issue, we propose two optimization models, which aim at maximizing the user cardinality and the sensing utility function for each region of the whole sensing area, respectively. We then design effective incentive mechanisms, which possess the desirable properties, including computational efficiency, individual rationality, budget feasibility, truthfulness, and good competitiveness. By conducting extensive experiments on the real-world geographical dataset, we demonstrate the effectiveness of the proposed mechanisms in achieving the good quality of service.