An incentive mechanism in expert-decision-based crowdsensing networks

Bing Jia, Hao Gong, Zhaopeng Zong, Tao Zhou, Thar Baker, Ahmed Al-Shamma’a, Yan Jia

Research output: Contribution to journalArticlepeer-review


With the rapid development of wireless communication and smart devices, crowdsensing applications became popular due to their flexibility to deploy and low cost use. Incentive mechanism is one of the most important research contents in crowdsensing, about crowdsensing incentive mechanism, most existing data quality evaluation methods measure the contributions of users only in terms of data quality, and ignore to measure the sensing cost of users. This leads to the problems of different quality evaluation standards, difficult to measure the data quality and difficult to give a reasonable and effective evaluation to complex problems. However, expert-decision can effectively solve these problems and give high-quality evaluation decision for complex and numerous data results. In this paper, aiming at the shortcomings of existing research, we propose an expert-decision-based crowdsensing framework and gives the multidimensional rating for incentive mechanism based on user cost and data quality (MRAI-UCDQ), which consists of user cost evaluation model, data quality evaluation model, contribution quantification and reward distribution by analysing user sensing cost data and collected sensing data (comprehensive evaluation with quantitative and qualitative analysis). Finally, through nearly 30 days of real experiments, 159 volunteers were recruited and 7000 pieces of sensory data were collected. The result shows the MRAI-UCDQ improves the evaluation performance of data quality and stimulates the user’s perceived participation.
Original languageEnglish
Article number108834
JournalApplied Soft Computing
Publication statusPublished - 19 Apr 2022

Bibliographical note

Funding Information:
Funder: National Natural Science Foundation of China , Award Number: 42161070 , Grant Recipient: Bing Jia.

Publisher Copyright:
© 2022 Elsevier B.V.


  • Collected sensing data
  • Crowdsensing
  • Expert-decision
  • Sensing cost data
  • Statistical analytics


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