A Prediction-Based User Selection Framework for Heterogeneous Mobile CrowdSensing

作者：Yang, Yongjian; Liu, Wenbin; Wang, En^*; Wu, Jie

来源：IEEE Transactions on Mobile Computing, 2019, 18(11): 2460-2473.

DOI：10.1109/TMC.2018.2879098

• Mobile CrowdSensing
• user selection
• mobility prediction
• submodular k-secretaries problem

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.

• 出版日期2019-11
• 单位吉林大学