Efficient and Privacy-preserving Worker Selection in Mobile Crowdsensing Over Tentative Future Trajectories

Mobile Crowdsourcing (MCS) is a newly-emerged sensing paradigm where a group of workers is selected to collect and share real-time data for a particular task. With the recent advances of Internet of Things (IoTs), cloud computing, and 5G network, MCS has drawn great attention in recent years. Worker selection is one of the most fundamental problems in MCS, as the selected workers’ qualifications play a significant role in the service quality. In this paper, by extending the research scope of previous literature, we formulate a novel worker selection problem in MCS that incorporates spatial-temporal constraints over workers’ tentative future trajectories. Specifically, each worker is required to submit a tentative future trajectory in advance and the MCS platform only selects qualified workers who meet both the spatial and temporal constraints. To increase the efficiency of worker selection, we propose a hybrid indexing approach to efficiently index workers’ spatial-temporal information by combining MX-CIF quadtree and Interval tree. Besides, we design a greedy algorithm, which considers both the reliability of the selected workers and the overall budget at the same time. Furthermore, to protect workers’ sensitive spatial-temporal information from being disclosed to untrusted parties, we design a privacy-preserving technique by transferring workers’ real spatial-temporal information to the approximate data with restricted information. Security analysis shows that the proposed solution is privacy-preserving. Extensive experiments are conducted, and the results demonstrate that our scheme outperforms the baseline methods.