Network Utility Maximization based on Incentive Mechanism for Truthful Reporting of Local Information
Jie Gao, Lian Zhao, Xuemin (Sherman) Shen

TL;DR
This paper introduces a new model for network utility maximization that incorporates incentive mechanisms to ensure truthful local information reporting in distributed networks, addressing practical challenges of information honesty.
Contribution
It proposes a general model for NUM with local information and develops mechanisms for truthful reporting in user-centric and network-centric problems.
Findings
Dual pricing fails to guarantee truthful reporting unless resources are over-supplied.
Proposed mechanisms motivate truthful reporting with nonnegative utility gains.
Case study and simulations validate the effectiveness of the mechanisms.
Abstract
Classic network utility maximization problems are usually solved assuming all information is available, implying that information not locally available is always truthfully reported. This may not be practical in all scenarios, especially in distributed/semi-distributed networks. In this paper, incentive for truthful reporting in network optimizations with local information is studied. A novel general model for extending network utility maximization (NUM) problems to incorporate local information is proposed, which allows each user to choose its own objective locally and/or privately. Two specific problems, i.e., a user-centric problem (UCP) and a network-centric problem (NCP), are studied. In the UCP, a network center aims to maximize the collective benefit of all users, and truthful reporting from the users regarding their local information is necessary for finding the solution. We…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Age of Information Optimization · Smart Grid Energy Management
