Time-Based Quality-Aware Incentive Mechanism for Mobile Crowd Sensing
Recent years have witnessed the advance of mobile crowd sensing (MCS) system. How to meet the demands of task time requirements and obtain high-quality data with little expense has become a critical problem. We focus on exploring incentive mechanisms for a practical scenario, where the tasks are time window dependent. An important indicator, “quality of user’s data (QOD)” is also considered. First, we design a prediction model based on user history data (p-QOD), to calculate the next time of the user’s QOD. Second, we design a dynamic programming algorithm based on time windows and p-QOD, to ensure all of the task time windows are covered, as well as minimizing the platform’s cost. Finally, we determine the payment for each user through a Vickrey–Clarke–Groves auction (VCG) considering the user’s true data quality (t-QOD), which is based on their submission time. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, fairness, and individual rationality.
KeywordsMobile crowd sensing Incentive mechanism VCG auction Quality of user’s data Time window
The research is supported by “National Natural Science Foundation of China” (No. 61572526) and “Innovation Project for Graduate Students in Central South University” (No. 502211708).
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