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A task recommendation framework for heterogeneous mobile crowdsensing

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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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Acknowledgements

This present research work was supported by the National Natural Science Foundation of China (61403109, 61202458), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007), the Heilongjiang Natural Science Foundation (LH2020F034) and the China Postdoctoral Science Foundation (2019M651263).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Jian Wang, Jiaxin Liu & Zhongnan Zhao

  2. School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

    Guosheng Zhao

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  1. Jian Wang
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  2. Jiaxin Liu
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  3. Zhongnan Zhao
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  4. Guosheng Zhao
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Correspondence to Jian Wang.

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Wang, J., Liu, J., Zhao, Z. et al. A task recommendation framework for heterogeneous mobile crowdsensing. J Supercomput 77, 12121–12142 (2021). https://doi.org/10.1007/s11227-021-03745-0

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