  Crowd sensing is a new paradigm which leverages the ubiquity of
sensor-equipped mobile devices to collect data. To achieve good quality for
crowd sensing, incentive mechanisms are indispensable to attract more
participants. Most of existing mechanisms focus on the expected utility prior
to sensing, ignoring the risk of low quality solution and privacy leakage.
Traditional incentive mechanisms such as the Vickrey-Clarke-Groves (VCG)
mechanism and its variants are not applicable here. In this paper, to address
these challenges, we propose a behavior based incentive mechanism for crowd
sensing applications with budget constraints by applying sequential all-pay
auctions in mobile social networks (MSNs), not only to consider the effects of
extensive user participation, but also to maximize high quality of the context
based sensing content submission for crowd sensing platform under the budget
constraints, where users arrive in a sequential order. Through an extensive
simulation, results indicate that incentive mechanisms in our proposed
framework outperform the best existing solution.

Computer Science and Game Theory;Networking and Internet Architecture