Incentive-Based Crowdsourcing of Hotspot Services

Abstract

We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy, time of day, and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute crowdsourced service providers to improve the crowdsourced coverage balance within an area. A novel participation probability model is also introduced to estimate the expected number of crowdsourced service providers’ movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.