LAUSR

Mobile crowd sensing has emerged as a novel data collection paradigm by leveraging pervasive mobile sensing devices to enable various applications. To obtain good quality of service, incentive mechanisms are indispensable for attracting enough users. Most of the existing mechanisms focus on the offline scenario in which all users submit profiles in advance. However, the online scenario often appears in the real world wherein users arrive one by one in random order. In this paper, we investigate the frugal online incentive problem based on an online auction model, where users report their strategic profiles to the crowdsourcer in an online mode, and the crowdsourcer selects users before a deadline to complete a specific number of tasks while minimizing the total payment. We design two online mechanisms, namely, Frugal-OMZ and Frugal-OMG, satisfying computational efficiency, individual rationality, truthfulness, consumer sovereignty, and constant frugality under the zero arrival–departure interval model and the general interval model, respectively. Extensive simulations verify the desirable properties of our mechanisms.