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Tsinghua Science and Technology 2021, 26(6): 869-877 https://doi.org/10.26599/TST.2020.9010046
Open Access | Issue | Published: 09 June 2021

Quality-Aware User Recruitment Based on Federated Learning in Mobile Crowd Sensing

Show Author's Information Wei Zhang,  Zhuo Li( ),  Xin Chen
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, and School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China
School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China
Keywords:
crowd sensing, federated learning, quality aware, user recruitment
Cite this article:
Zhang W, Li Z, Chen X. Quality-Aware User Recruitment Based on Federated Learning in Mobile Crowd Sensing. Tsinghua Science and Technology, 2021, 26(6): 869-877. https://doi.org/10.26599/TST.2020.9010046
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Abstract Full text About this article

With the rapid development of mobile devices, the use of Mobile Crowd Sensing (MCS) mode has become popular to complete more intelligent and complex sensing tasks. However, large-scale data collection may reduce the quality of sensed data. Thus, quality control is a key problem in MCS. With the emergence of the federated learning framework, the number of complex intelligent calculations that can be completed on mobile devices has increased. In this study, we formulate a quality-aware user recruitment problem as an optimization problem. We predict the quality of sensed data from different users by analyzing the correlation between data and context information through federated learning. Furthermore, the lightweight neural network model located on mobile terminals is used. Based on the prediction of sensed quality, we develop a user recruitment algorithm that runs on the cloud platform through terminal-cloud collaboration. The performance of the proposed method is evaluated through simulations. Results show that compared with existing algorithms, i.e., Random Adaptive Greedy algorithm for User Recruitment (RAGUR) and Context-Aware Tasks Allocation (CATA), the proposed method improves the quality of sensed data by 23.5 % and 38.8 %, respectively.


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Quality-Aware User Recruitment Based on Federated Learning in Mobile Crowd Sensing

Show Author's information Wei Zhang, Zhuo Li( ), Xin Chen
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, and School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China
School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China

Abstract

With the rapid development of mobile devices, the use of Mobile Crowd Sensing (MCS) mode has become popular to complete more intelligent and complex sensing tasks. However, large-scale data collection may reduce the quality of sensed data. Thus, quality control is a key problem in MCS. With the emergence of the federated learning framework, the number of complex intelligent calculations that can be completed on mobile devices has increased. In this study, we formulate a quality-aware user recruitment problem as an optimization problem. We predict the quality of sensed data from different users by analyzing the correlation between data and context information through federated learning. Furthermore, the lightweight neural network model located on mobile terminals is used. Based on the prediction of sensed quality, we develop a user recruitment algorithm that runs on the cloud platform through terminal-cloud collaboration. The performance of the proposed method is evaluated through simulations. Results show that compared with existing algorithms, i.e., Random Adaptive Greedy algorithm for User Recruitment (RAGUR) and Context-Aware Tasks Allocation (CATA), the proposed method improves the quality of sensed data by 23.5 % and 38.8 %, respectively.

Keywords: crowd sensing, federated learning, quality aware, user recruitment

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Publication history

Received: 25 August 2020
Accepted: 25 September 2020
Published: 09 June 2021
Issue date: December 2021

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© The author(s) 2021.

Acknowledgements

This research was partly supported by the National Natural Science Foundation of China (Nos. 61872044 and 61502040), Beijing Municipal Program for Top Talent, Beijing Municipal Program for Top Talent Cultivation (No. CIT & TCD201804055), and Qinxin Talent Program of Beijing Information Science and Technology University.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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