Sybil-proof incentive mechanisms for crowdsensing
Jian Lin, Ming Li, Dejun Yang, Guoliang Xue, Jian Tang · 2017
The rapid growth of sensor-embedded smartphones has led to a new data sensing and collecting paradigm, known as crowdsensing. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in crowdsensing. However, none of them have taken into consideration the Sybil attack where a user illegitimately pretends multiple identities to gain benefits. This attack may undermine existing inventive mechanisms. To deter the Sybil attack, we design Sybil-proof auction-based incentive mechanisms for crowdsensing in this paper. We investigate both the single-minded and multi-minded cases and propose SPIM-S and SPIM-M, respectively. SPIM-S achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SPIM-M achieves individual rationality, truthfulness, and Sybil-proofness. We evaluate the performance and validate the desired properties of SPIM-S and SPIM-M through extensive simulations.