Mobile Crowdsensing User Recruitment Algorithm Based on Combination Multi-Armed Bandit

JIANG Weijin; CHEN Pingping; ZHANG Wanqing; SUN Yongxia; CHEN Junpeng

doi:10.11999/JEIT210119

Volume 44 Issue 3

Mar. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 1119-1128

JIANG Weijin, CHEN Pingping, ZHANG Wanqing, SUN Yongxia, CHEN Junpeng. Mobile Crowdsensing User Recruitment Algorithm Based on Combination Multi-Armed Bandit[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1119-1128. doi: 10.11999/JEIT210119

Citation:

JIANG Weijin, CHEN Pingping, ZHANG Wanqing, SUN Yongxia, CHEN Junpeng. Mobile Crowdsensing User Recruitment Algorithm Based on Combination Multi-Armed Bandit[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1119-1128. doi: 10.11999/JEIT210119

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Mobile Crowdsensing User Recruitment Algorithm Based on Combination Multi-Armed Bandit

doi: 10.11999/JEIT210119

1.
School of Computer Science, Hunan University of Technology and Business, Changsha 410205, China
2.
School of Computer Science, Wuhan University of Technology, Wuhan 430073, China

Funds: The National Natural Science Foundation of China (61472136, 61772196), The Natural Science Foundation of Hunan Province (2020JJ4249), The Degree and Graduate Education Reform Research Project of Hunan Province (2020JGYB234), Hunan Provincial Innovation Foundation For Postgraduate (CX20211108, CX20211151), Scientific Research Project of Hunan Provincial Department of Education (21A0374)

Received Date: 2021-02-01
Accepted Date: 2021-12-14
Rev Recd Date: 2021-12-12

Available Online: 2022-01-12

Publish Date: 2022-03-28

Abstract

Abstract

In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user's perceived quality or cost value, how to establish a suitable user recruitment mechanism is the key issue that this article needs to solve. It is necessary to try to ensure the efficiency and profit maximization of the mobile crowdsensing platform. Therefore, a mobile crowdsensing user recruitment algorithm based on a Combined Multi-Armed Bandit (CMAB) is proposed to solve the recruitment problem of known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multi-arm bandit model. Each rocker is represented by a different user’s choice, and the income obtained represents the user’s perceived quality. Secondly, the Upper Confidence Bound (UCB) algorithm is proposed to update the user’s perceived quality according to the completion of the task. Users’ perceived quality values are sorted from high to low, and then the user with the largest ratio of perceived quality to recruitment cost is selected under budget conditions, tasks are assigned, and their perceived quality is updated. Finally, A large number of experimental simulations based on real data sets are carried out to verify the feasibility and effectiveness of the algorithm.
- Mobile crowdsensing,
- Combination Multi-Armed Bandit(CMAB),
- User recruitment,
- Perceived quality,
- Reinforcement learning

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References(19)

References

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