Real-Time Crowdsourcing Incentive for Radio Environment Maps: A Dynamic Pricing Approach

To effectively utilize/harvest short-lived whitespace that accounts for more than 30% of the cellular bands, it is critical to build a real-time radio environment map. Note that existing radio spectrum maps/databases (e.g., Google Spectrum Database) are updated on a daily or weekly basis. In this paper, we introduce a novel real-time crowdsourcing incentive solution that rewards mobile users who contribute their qualified spectrum sensing data to a radio environment map. First, we develop a feature-based model based on advanced machine learning techniques in order to estimate model parameters of the radio environment map. Based on the prediction model, we then propose a smart dynamic pricing strategy including prepaid and postpaid pricing schemes. The prepaid scheme is to guarantee the minimum payment for participants, and the postpaid scheme is to reward the participants according to their contributions. Importantly, in our model, the postpaid scheme will be adjusted iteratively in a real-time manner based on the contributions of participants to the spectrum map. After that we carry out real experiments through a mobile application and a cloud spectrum database. The experiment results show that our proposed solution can achieve not only better users' utilities, but also a lower overall system cost compared with those of some existing works.