  Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data
about the environment by mounting sensors on vehicles such as taxis. An
important problem in vehicular crowd sensing is to design payment mechanisms to
incentivize drivers (agents) to collect data, with the overall goal of
obtaining the maximum amount of data (across multiple vehicles) for a given
budget. Past works on this problem consider a setting where each agent operates
in isolation---an assumption which is frequently violated in practice. In this
paper, we design an incentive mechanism to incentivize agents who can engage in
arbitrary collusions. We then show that in a "homogeneous" setting, our
mechanism is optimal, and can do as well as any mechanism which knows the
agents' preferences a priori. Moreover, if the agents are non-colluding, then
our mechanism automatically does as well as any other non-colluding mechanism.
We also show that our proposed mechanism has strong (and asymptotically
optimal) guarantees for a more general "heterogeneous" setting. Experiments
based on synthesized data and real-world data reveal gains of over 30\%
attained by our mechanism compared to past literature.
