Two-phased Participant Selection Method Based on Partial Transfer Learning in Mobile Crowdsensing

Abstract

To solve the problem of sensing data redundancy and missing data caused by the uneven distribution of resources in mobile crowd sensing, a two-phased participant selection method based on partial transfer learning is proposed. Firstly, the data is preprocessed. On the one hand, the sensing task features are extracted to analyze the correlation between the source task and the target task feature space. On the other hand, users are divided into active users and passive users according to the historical movement law of sensing users. Secondly, the participant selection in the first stage is carried out. According to the similarity of feature space between the source task and the target task, part of the user resources of the source task are migrated to the target subtask with a similar distribution of its feature space. As a result, the target task can select participants efficiently and accurately. Finally, the participant selection in the second stage is carried out. For the target subtask not covered, the passive users in the subtask area are taken as the assignment object. Simulation results based on real data sets show that this method can effectively improve the task coverage and reduce the perceived excitation cost.