COUSTIC: Combinatorial Double auction for Task Assignment in Device-to-Device Clouds
Yutong Zhai, Liusheng Huang, Long Chen, Ning Xiao, Yangyang Geng

TL;DR
This paper introduces a truthful, distributed combinatorial double auction mechanism for task assignment in device-to-device clouds, enabling efficient crowd sensing without centralized control.
Contribution
It proposes a novel incentive mechanism that is truthful, individual rational, budget balanced, and computationally efficient for D2D crowd sensing tasks.
Findings
Achieves 26.3% higher gains than existing double auction schemes.
Outperforms centralized maximum matching algorithms by 15.8%.
Handles time-critical sensing tasks effectively in a distributed manner.
Abstract
With the emerging technologies of Internet of Things (IOTs), the capabilities of mobile devices have increased tremendously. However, in the big data era, to complete tasks on one device is still challenging. As an emerging technology, crowdsourcing utilizing crowds of devices to facilitate large scale sensing tasks has gaining more and more research attention. Most of existing works either assume devices are willing to cooperate utilizing centralized mechanisms or design incentive algorithms using double auctions. Which is not practical to deal with the case when there is a lack of centralized controller for the former, and not suitable to the case when the seller device is also resource constrained for the later. In this paper, we propose a truthful incentive mechanism with combinatorial double auction for crowd sensing task assignment in device-to-device (D2D) clouds, where a single…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · IoT and Edge/Fog Computing · Data Stream Mining Techniques
