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

Author:

Wang Jian1ORCID,Liu Jiaxin1ORCID,Zhao Guosheng2ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China

2. College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China

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.

Funder

National Natural Science Foundation of China

Specialized Research Fund for the Doctoral Program of Higher Education of China

Natural Science Foundation of Heilongjiang Province

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

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