The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (3): 34-42.doi: 10.19682/j.cnki.1005-8885.2022.1001

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User selection based on user-union and relative entropy in mobile crowdsensing


  1. 1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    2. National Secrecy Science and Technology Evaluation Center, Beijing 100044, China

  • Received:2021-04-22 Revised:2021-09-24 Online:2022-06-30 Published:2022-06-30
  • Contact: Wang Huiqiang
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61872104), and Fundamental Research Fund for the Central Universities in China (3072020CF0603)


A critical issue in mobile crowdsensing (MCS) involves selecting appropriate users from a number of participants to guarantee the completion of a sensing task. Users may upload unnecessary data to the sensing platform, leading to redundancy and low user selection efficiency. Furthermore, using exact values to evaluate the quality of the user-union will further reduce selection accuracy when users form a union. This paper proposes a user selection method based on user-union and relative entropy in MCS. More specifically, a user-union matching scheme based on similarity calculation is constructed to achieve user-union and reduce data redundancy effectively. Then, considering the interval-valued influence, a user-union selection strategy with the lowest relative entropy is proposed. Extensive testing was conducted to investigate the impact of various parameters on user selection. The results obtained are encouraging and provide essential insights into the different aspects impacting the data

redundancy and interval-valued estimation of MCS user selection.

Key words: mobile crowdsensing (MCS), user selection, interval-value, user-union, relative entropy