  Crowd sensing is a new paradigm which leverages the pervasive smartphones to
efficiently collect sensing data, enabling numerous novel applications. To
achieve good service quality for a crowd sensing application, incentive
mechanisms are indispensable to attract more user participation. Most of
existing mechanisms only apply for the offline scenario, where the system has
full information about the users' sensing profiles, i.e., a set of locations or
mobility as well as the type of smartphones used, and their true costs. On the
contrary, we focus on a more real scenario where users with their own privacy
concerns arrive one by one online in a random order. We model the problem as a
privacy-respecting online auction in which users are willing to negotiate
access to certain private information and submit their sensing profiles
satisfying privacy concerns to the platform (the provider of crowd sensing
applications) over time, and the platform aims to the total total value of the
services provided by selected users under a budget constraint. We then design
two online mechanisms for a budgeted crowd sensing application, satisfying the
computational efficiency, individual rationality, budget feasibility,
truthfulness, consumer sovereignty, constant competitiveness and privacy
concerns. Through extensive simulations, we evaluate the performance and
validate the theoretical properties of our online mechanisms.
