An incentive mechanism for continuous crowd sensing based symmetric encryption and double truth discovery

Aimed at the problems in continuous crowd sensing, such as privacy protection, unreliable perceptual data, and a small amount of collected perceptual data, an incentive mechanism based on symmetric encryption and double-layer truth discovery is proposed. First, the symmetric encryption algorithm is used to protect the privacy of the perceived data. When the privacy requirements are high and the number of perceptions involved in computing is large, the computing overhead and reward computing time are greatly reduced. Second, based on a double-layer truth discovery model, an incentive mechanism for data reliability evaluation is proposed, so as to simultaneously realize the real-time reward of continuous crowd sensing and improve the fairness of reward when the participants have malicious behavior. Numerical simulation results show that the proposed method can effectively calculate the true value and the reward according to the data reliability. Notably, it outperforms those related incentive models in the less reward calculating time and the fair reward distribution when the participants have malicious behavior.