Revenue-Optimal Reverse Auction for Task Allocation in Mobile Crowdsensing Through Transformer Attention

Mobile crowdsensing service (MCS) providers recruit users to complete data collection tasks by rewarding the users to obtain greater revenue. Therefore, maximizing revenue is a focus of the MCS provider. This article expresses this problem as a revenue maximization programming model with budget constraints and designs a reverse-auction mechanism based on the attention model to solve the task allocation and pricing problems. Specifically, we convert the programming model under multiple constraints into an augmented Lagrangian function, optimally solve it through a multilayer neural network on the basis of the attention interactive framework, and finally output the allocation and payment solution. Our design guarantees that the mechanism meets economic criteria such as truthfulness, individual rationality, and budget feasibility. Combining the revenue-optimal reverse-auction mechanism with deep learning provides a new approach to mechanism design. Compared with existing methods, our solution achieves very good results in terms of service provider revenue and generalization experiments.