ATWR-SMR: An Area-Constrained Truthful-Worker Recruitment-Based Sensing Map Recovery Scheme for Sparse MCS in Extreme-Environment Internet of Things

Sparse mobile crowdsensing (SMCS) is a prospective solution for large-scale data sensing through mobile devices of Internet of Things (IoT) systems where IoT systems cannot obtain the sensing data of the area under extreme environments. The unsensed area data can be obtained by the data inference algorithm trained by the sensed data of recruited workers. However, recruited workers may upload false data in exchange for payment, and the platform is unable to distinguish between true and false data. In this article, our goal is to maximize the SMCS platform’s total profit, where the platform cannot verify the authenticity of the sensed data, and the requester’s payment is based on the sensing task’s data quality. To meet the objective, we propose the area-constrained truthful worker recruitment-based sensing map recovery (ATWR-SMR) scheme, which includes the area constraint, the area-constrained truthful worker recruitment, and the sensing map recovery. 1) The area constraint establishes the importance of areas by history data differences in the sliding window. 2) The truthful worker recruitment identifies trustworthy workers by the truthful upper confidence bound algorithm and recruits low-cost trustworthy workers to sense high-importance areas. 3) The sensing map recovery infers the unsensed data by the deep matrix factorization algorithm trained by the history truthful data set. Finally, we verify the effectiveness of the ATWR-SMR scheme in improving the total profit of the platform through extensive comparison experiments based on the China air quality data set.