  Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of
the pervasive smartphones to efficiently collect data, enabling numerous novel
applications. To achieve good service quality for a MCS application, incentive
mechanisms are necessary to attract more user participation. Most of existing
mechanisms apply only for the offline scenario where all users' information are
known a priori. On the contrary, we focus on a more real scenario where users
arrive one by one online in a random order. We model the problem as an online
auction in which the users submit their private types to the crowdsourcer over
time, and the crowdsourcer aims to select a subset of users before a specified
deadline for maximizing the total value of the services provided by selected
users under a budget constraint. We design two online mechanisms, OMZ and OMG,
satisfying the computational efficiency, individual rationality, budget
feasibility, truthfulness, consumer sovereignty and constant competitiveness
under the zero arrival-departure interval case and a more general case,
respectively. Through extensive simulations, we evaluate the performance and
validate the theoretical properties of our online mechanisms.
