Repot: Real-time and privacy-preserving online task assignment for mobile crowdsensing

Recently, the development of Internet of Things and mobile sensor technology has facilitated mobile crowdsensing (MCS) as a popular data collection paradigm. In MCS, requesters publish tasks to the sensing platform, and then the sensing platform assigns tasks to workers who carry mobile sensing devices. In general, the sensing platform matches tasks and workers according to the location and time attributes of tasks and workers. However, most current studies rarely consider location and time attributes, simultaneously. In previous work, it is difficult to achieve real-time task assignment while protecting worker's location privacy. To tackle the above problem, in this article, we propose a real-time and privacy-preserving online task assignment scheme, named Repot. In Repot, the location privacy of workers is protected by using geoindistinguishability. And we design a probabilistic method to quantify the reachability between the obfuscated worker and the task. In addition, the worker-based distance comparison mechanism and the task-based distance comparison mechanism are designed to reduce the overall distance of workers in Repot, respectively. The experimental evaluation is performed on a real-world data set and the results show the feasibility and effectiveness of Repot.