Truthful Mechanisms for Message Dissemination via Device-to-Device Communications

• Published in: IEEE transactions on vehicular technology Vol. 66; no. 11; pp. 10307 - 10321
• Main Authors: Zhao, Yiming, Song, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Cost engineering; D2D communications; Device-to-device communication; Electronic devices; Games; incentive constraints; Interference; Mathematical models; message dissemination; NP-hard problem; Randomization; Relays; Resource management; traffic offloading; truthfulness
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2017.2725449

Device-to-device (D2D) communications are not only featured by high spectral and energy efficiency, but also offer appealing benefits for applications, such as message dissemination, traffic offloading, and coverage expansion. In this paper, we consider a message dissemination scenario where a base station (BS) can direct message requests to be fulfilled by some source devices via D2D communications and thereby save the resource cost. Furthermore, a source device may be subject to a cost budget due to its limited resources. To minimize the overall cost or maximize the BS's cost saving from offloading, it is essential to properly assign the message request(s) for each source and decide the payment to incentivize participation. The request direction is an NP-hard problem, while the payment determination is also nontrivial. The payment should be sufficient to compensate for a source device's resource cost, and meanwhile, incentivize the source to truthfully declare its private cost and budget. Modeling the problem as a reverse auction, we develop both a deterministic mechanism and a randomized mechanism, which are truthful or truthful in expectation, respectively. Both mechanisms are individually rational and subject to a computational complexity lower than that of the well-known Vickrey-Clarke-Groves mechanism or its generalized version. While the deterministic mechanism performs well in an average sense, the randomized mechanism maintains an approximation guarantee for the worst case. Extensive numerical results demonstrate the effectiveness of the proposed mechanisms with various settings in the D2D content distribution scenario.