Utilizing Social Psychology Solutions to Enhance the Quality Assessment Ability of Unreliable Data in Mobile Crowdsensing
Zhehao Cheng, Jiaoyan Chen, Jin Liu · IEEE Internet of Things Journal · 2024
The mobile crowdsensing strategy emerges as a novel and trendy approach that arises from collecting a wide range of physical information in smart cities. In this context, assessing the quality of sensed data becomes essential to ensure that this strategy can be efficiently executed. Typically, popular data assessment strategies have focused on participants’ historical data as well as social contexts, or through online feedback to adjust quality biases. However, when faced with sensing data from more hostile environments, where sufficient supporting information is lacking and data is highly privatized, conventional assessment strategies may appear weak. Therefore, this article introduces an innovative approach by integrating social psychology to address this issue, proposing a data quality assessment strategy termed the “inspector sense model.” In this model, the responsibility for evaluating data quality is assigned to each participant, who serves as both a contributor of sensed data and an evaluator of others’ data quality. Further, this article introduces a probabilistic statistical model to ensure the high reliability of the assessment results. In addition, this article proposes an incentive mechanism called the “All Pay Auction” to ensure that this strategy can be implemented within the cost budget constraints. In particular, the unreliability of submission locations in the uploaded data is cleverly addressed by applying the proposed assessment strategy in reverse, significantly improving the overall data quality assessment capability of the strategy. During the experimental stage, simulation experiments were carried out utilizing temperature observations sourced from the real-world data provided by taxi drivers in Rome, and various performance metrics were analyzed. The results of these experiments demonstrate the capability of the proposed solutions to accurately assess data of unreliable quality, yielding highly precise assessment results.