Freshness-Aware Incentive Mechanism for Mobile Crowdsensing With Budget Constraint
Ying Cheng, Xiumin Wang, Pan Zhou, Xinglin Zhang, Weiwei Wu
Abstract
Mobile crowdsensing (MCS) has recently received considerable attention due to its capability of providing a promising paradigm to complete complex sensing tasks. Existing works on MCS mainly focus on designing incentive mechanisms to attract mobile users to participate in crowdsensing, while ignoring the freshness of information, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Age of Information</i> (AoI). Although multiple source nodes with common observation can indeed improve the data quality of MCS, it complicates the calculation of the AoI. To address this issue, this article proposes a freshness-aware incentive mechanism in MCS, which not only captures the conflict interests/competitions among users, but also considers the age of information (AoI). Specifically, we define two data sampling models, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sampling-at-will</i> model and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sampling-predetermined</i> model. For both models, we design efficient auction mechanisms, which recruit appropriate mobile users, determine the payments, and schedule the data sampling, so as to optimize the average AoI and data quality under budget constraint. It is proved that the proposed auction achieves several desirable properties, including individual rationality, budget balance, truthfulness and computational efficiency. We also theoretically derive the upper bound of the average AoI obtained by the proposed scheme. Finally, we conduct simulations to evaluate the efficiency of the proposed mechanism in optimizing the data quality and AoI.