Privacy-Aware Online Task Assignment Framework for Mobile Crowdsensing

Mobile crowdsensing is a new sensing paradigm exploiting potential of crowds to collect data, which has various advantages over traditional sensor networks such as low cost, high coverage, and high mobility. Privacy preservation is a crucial issue in mobile crowdsensing because worker privacy might be exposed if workers share their location information to service platform or other workers. In this paper, we assume workers can determine their own privacy preservation levels and they do not need to upload their location information to the platform or share to other workers for sensing behavior coordination. Moreover, workers move to task locations to collect sensing data in a distributed manner. We accordingly propose a privacy-aware online task assignment framework to achieve high task coverage. In this framework, spatial task-application information in previous cycles is used to estimate worker density and an incentive pricing mechanism is designed to guide workers to collect sensing data in low-worker-density areas. We present detailed mechanism design. Extensive simulation results show that our proposed solution has much better performance than the baseline mechanism.