Steering Crowdsourced Signal Map Construction via Bayesian Compressive Sensing

Mobile crowdsensing with increasing pervasiveness of smartphones has enabled a myriad of applications, including urban-scale signal map monitoring and revision. Despite the importance of its quality, due to the large size of a site to cover, dense crowdsourcing is neither cost-effective nor convenient for crowdsourcing participants, making it critical and challenging to balance between signal quality and crowdsourcing cost. To address this problem, we propose a novel incentive mechanism, BCCS, based on Bayesian Compressive Crowdsensing (BCS). BCCS iteratively determines the spatial grids for crowd-sourcing quality and predicts the remaining unexplored grids for deployment efficiency. BCS returns not only the predicted signal values, but also the confidence intervals for convergence and incentive control. A probabilistic user participation and measurement model is applied for incentive design, which is flexible for crowdsensing deployment. Our extensive evaluation based on two different data sets shows that BCCS achieves much higher prediction accuracy (often by more than 20%) with lower payments to the participants and fewer iterations (often by 30%) than existing solutions.