COUSTIC: Combinatorial Double auction for Task Assignment in
Device-to-Device Clouds
release_xcdkzsxw5zhu3f53lmmlfe2u6i
by
Yutong Zhai, Liusheng Huang, Long Chen, Ning Xiao, Yangyang Geng
2018
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 mobile device with intensive
sensing task can hire a group of idle neighboring devices. With this new
mechanism, time critical sensing tasks can be handled in time with a
distributed nature. We prove that the proposed mechanism is truthful,
individual rational, budget balance and computational efficient. Our simulation
results demonstrate that combinatorial double auction mechanism gets a 26.3%
and 15.8% gains in comparison to existing double auction scheme and the
centralized maximum matching based algorithm respectively.
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