A Reputation-Based Incentive and Allocation Model Using Double Auction for Mobile Crowdsensing

Mobile crowdsensing (MCS) leverages distributed mobile users to complete large-scale sensing tasks. With the rapid advancement of sensing capabilities in urban environments, data acquisition has become increasingly efficient and scalable. However, promoting user participation and improving task quality remain significant challenges. While auction-based models in MCS aim to optimize task allocation efficiency and incentivize high-quality data contributions, they often fail to account for the effective evaluation of worker distribution and task quality. To address these limitations, we propose a Reputation-Based Incentive Mechanism Double-Auction Model (RBIM), which considers both uneven worker arrivals and the diminishing returns of task quality. RBIM categorizes tasks based on difficulty levels, allowing workers to choose tasks aligned with their preferences. The task quality is evaluated by integrating platform costs and requester satisfaction, which subsequently influence both worker compensation and reputation scores. Moreover, the payment scheme incorporates reputation-based adjustments to further incentivize reliable participation and enhance task completion rates. Experimental results demonstrate that RBIM consistently outperforms several benchmark algorithms, significantly improving task completion rates, task quality, and overall system utility. Additionally, the proposed mechanism satisfies essential economic properties including individual rationality (IR), truthfulness (TF), and budget feasibility, while maximizing social welfare within given budget constraints (BC).