A Multi-Dimensional Contract Approach for Data Rewarding in Mobile Networks

Data rewarding is a novel business model leading a new economic trend in mobile networks, in which the operators stimulate mobile users to watch ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we build a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multi-dimensional private information, i.e., data valuation, ad valuation, and ad sensitivity. In particular, we model the interplay between the advertiser and users by using a multi-dimensional contract approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we utilize the structural properties of our contract problem and convert the multi-dimensional contract into an equivalent one-dimensional contract. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. Extensive numerical evaluations validate the efficiency of the designed multi-dimensional contract for data rewarding compared to other benchmark schemes.