1,749 research outputs found
POEM: Pricing Longer for Edge Computing in the Device Cloud
Multiple access mobile edge computing has been proposed as a promising
technology to bring computation services close to end users, by making good use
of edge cloud servers. In mobile device clouds (MDC), idle end devices may act
as edge servers to offer computation services for busy end devices. Most
existing auction based incentive mechanisms in MDC focus on only one round
auction without considering the time correlation. Moreover, although existing
single round auctions can also be used for multiple times, users should trade
with higher bids to get more resources in the cascading rounds of auctions,
then their budgets will run out too early to participate in the next auction,
leading to auction failures and the whole benefit may suffer. In this paper, we
formulate the computation offloading problem as a social welfare optimization
problem with given budgets of mobile devices, and consider pricing longer of
mobile devices. This problem is a multiple-choice multi-dimensional 0-1
knapsack problem, which is a NP-hard problem. We propose an auction framework
named MAFL for long-term benefits that runs a single round resource auction in
each round. Extensive simulation results show that the proposed auction
mechanism outperforms the single round by about 55.6% on the revenue on average
and MAFL outperforms existing double auction by about 68.6% in terms of the
revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on
Algorithms and Architectures for Parallel Processing (ICA3PP
Enabling Privacy-preserving Auctions in Big Data
We study how to enable auctions in the big data context to solve many
upcoming data-based decision problems in the near future. We consider the
characteristics of the big data including, but not limited to, velocity,
volume, variety, and veracity, and we believe any auction mechanism design in
the future should take the following factors into consideration: 1) generality
(variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness
and verifiability (veracity). In this paper, we propose a privacy-preserving
construction for auction mechanism design in the big data, which prevents
adversaries from learning unnecessary information except those implied in the
valid output of the auction. More specifically, we considered one of the most
general form of the auction (to deal with the variety), and greatly improved
the the efficiency and scalability by approximating the NP-hard problems and
avoiding the design based on garbled circuits (to deal with velocity and
volume), and finally prevented stakeholders from lying to each other for their
own benefit (to deal with the veracity). We achieve these by introducing a
novel privacy-preserving winner determination algorithm and a novel payment
mechanism. Additionally, we further employ a blind signature scheme as a
building block to let bidders verify the authenticity of their payment reported
by the auctioneer. The comparison with peer work shows that we improve the
asymptotic performance of peer works' overhead from the exponential growth to a
linear growth and from linear growth to a logarithmic growth, which greatly
improves the scalability
SMART: Coordinated Double-Sided Seal Bid Multiunit First Price Auction Mechanism for Cloud-Based TVWS Secondary Spectrum Market
Spectrum trading is an important aspect of television white space (TVWS) and it is driven by
the failure of spectrum sensing techniques. In spectrum trading, the primary users lease their unoccupied
spectrum to the secondary users for a market fee. Although spectrum trading is considered as a reliable
approach, it is confronted with a spectrum transaction completion time problem, which negatively impacts
on end-users Quality of Service and Quality of Experience metrics. Spectrum transaction completion time
is the duration to successfully conduct TVWS spectrum trading. To address this issue, this paper proposes
simple mechanism auction reward truthful (SMART), a fast and iterative machine learning-assisted spectrum
trading model to address this issue. Simulated results indicate thatSMART out-performs referenced VERUM
algorithm in three key performance indicators: bit-error rate, instantaneous throughput, and probability of
dropped packets by 10%, 5%, and 15%, respectively
Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges
In recent years, blockchain has gained widespread attention as an emerging
technology for decentralization, transparency, and immutability in advancing
online activities over public networks. As an essential market process,
auctions have been well studied and applied in many business fields due to
their efficiency and contributions to fair trade. Complementary features
between blockchain and auction models trigger a great potential for research
and innovation. On the one hand, the decentralized nature of blockchain can
provide a trustworthy, secure, and cost-effective mechanism to manage the
auction process; on the other hand, auction models can be utilized to design
incentive and consensus protocols in blockchain architectures. These
opportunities have attracted enormous research and innovation activities in
both academia and industry; however, there is a lack of an in-depth review of
existing solutions and achievements. In this paper, we conduct a comprehensive
state-of-the-art survey of these two research topics. We review the existing
solutions for integrating blockchain and auction models, with some
application-oriented taxonomies generated. Additionally, we highlight some open
research challenges and future directions towards integrated blockchain-auction
models
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