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Title	Data Quality Assessment In Mobile Crowdsensing By Utilizing Psychology Effect
ID_Doc	17319
Authors	Cheng Z.; Chen J.; Liu J.; Li M.
Year	2023
Published	Proceedings - 2023 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2023
DOI	http://dx.doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00048
Abstract	Mobile crowdsensing has become a popular strategy for collecting physical information in smart cities. To ensure the efficiency and effectiveness of this approach, assessing the quality of sensing data is crucial. Typically, strategies for evaluating the quality of mobile crowdsensing data involve using correlated historical data from participants to make inferences about the current data quality or adjusting deviations in data quality through online feedback. However, these approaches may not perform well when both historical data and interactions with participants are unavailable, making the evaluation of data quality challenging. This work addresses the challenge of assessing the quality of sensing data when referable information is lacking. It introduces a psychological phenomenon where participants are more willing to provide truthful feedback when it doesn't compromise their privacy. Specifically, the system invites participants to engage in evaluating the quality of data uploaded by other participants after they have uploaded their own data. Finally, we evaluate the proposed strategy by applying it to the assessment of urban air quality. Experimental results demonstrate that the proposed strategy achieves higher data quality. © 2023 IEEE.
Author Keywords	Data Quality Assessment; Mobile Crowdsensing; Psychology Effect; Visitor Data

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