LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system

As the scale of Mobile CrowdSensing (MCS) system expands, effective mobile user allocation and recruitment system design becomes crucial. Mobile users can be divided into opportunistic users and participatory ones. Most of the existing recruitment strategy have neglected some aspects, such as without considering the low-paying opportunistic users, without comprehensively considering users’ attributes and without considering their future location, etc. In this paper, the recruitment scheme is investigated by considering the opportunistic users. Firstly, the User Recruitment problem based on User Comprehensive Capabilities (urUCC) is proposed with the objective of maximizing the total revenue. In addition, this problem is proved to be NP-Hard. Secondly, the Opportunistic Users recruitment strategy based on Deep Learning (OUDL) is designed, which consists of three parts, the user location prediction algorithm based on the Long Short-Term Memory (LSTM), the user evaluation algorithm based on the topsis comprehensive evaluation method and the dynamic user recruitment algorithm. Finally, a large number of simulation experiments are conducted by using real datasets. It is proved that the strategy OUDL can recruit high-quality opportunistic users to participate in the sensing task while guaranteeing the task completion rate. Compared with other strategies, the task coverage of the strategy OUDL increased by more than 5% while the comprehensive quality of users increased by about 10%. Thus, the quality of data can be guaranteed while reducing the cost.