TY - JOUR
T1 - A Multi-Dimensional Contract Approach for Data Rewarding in Mobile Networks
AU - Xiong, Zehui
AU - Kang, Jiawen
AU - Niyato, Dusit
AU - Wang, Ping
AU - Poor, H. Vincent
AU - Xie, Shengli
N1 - Funding Information:
Manuscript received November 11, 2019; revised March 17, 2020; accepted May 12, 2020. Date of publication June 2, 2020; date of current version September 10, 2020. This work was supported in part by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, under Grant NRF2017EWT-EP003-041 and Grant NRF2015-NRF-ISF001-2277, in part by the Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure under Grant NSoE DeST-SCI2019-0007, in part by the A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing under Grant RGANS1906, in part by the Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (WASP/NTU) under Grant M4082187 (4080), in part by the Singapore Ministry of Education (MOE) Tier 2 under Grant MOE2014-T2-2-015 ARC4/15 and MOE Tier 1 under Grant 2017-T1-002-007 RG122/17, in part by the National Natural Science Foundation of China under Grant U1911401, in part by the Canada Natural Sciences and Engineering Research Council (NSERC) Discovery under Grant RGPIN-2019-06375, in part by the U.S. National Science Foundation under Grant CCF-0939370 and Grant CCF-1908308, and in part by the Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Joint Research Institute (JRI), Nanyang Technological University, Singapore. The associate editor coordinating the review of this article and approving it for publication was J. Lee. (Corresponding author: Jiawen Kang.) Zehui Xiong is with the Alibaba-NTU Joint Research Institute, Nanyang Technological University, Singapore 639798, and also with the School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Data rewards
KW - contract theory
KW - incentive mechanism
KW - information asymmetry
KW - network economics
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U2 - 10.1109/TWC.2020.2997023
DO - 10.1109/TWC.2020.2997023
M3 - Article
AN - SCOPUS:85091153329
SN - 1536-1276
VL - 19
SP - 5779
EP - 5793
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 9
M1 - 9106861
ER -