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The Journal of Supercomputing

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06-04-2021

A task recommendation framework for heterogeneous mobile crowdsensing

Authors: Jian Wang, Jiaxin Liu, Zhongnan Zhao, Guosheng Zhao

Published in: The Journal of Supercomputing | Issue 10/2021

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Abstract

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved.

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Title: A task recommendation framework for heterogeneous mobile crowdsensing
Authors: Jian Wang
Jiaxin Liu
Zhongnan Zhao
Guosheng Zhao
Publication date: 06-04-2021
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 10/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03745-0

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