29 research outputs found
Social Welfare Maximization Auction in Edge Computing Resource Allocation for Mobile Blockchain
Blockchain, an emerging decentralized security system, has been applied in
many applications, such as bitcoin, smart grid, and Internet-of-Things.
However, running the mining process may cost too much energy consumption and
computing resource usage on handheld devices, which restricts the use of
blockchain in mobile environments. In this paper, we consider deploying edge
computing service to support the mobile blockchain. We propose an auction-based
edge computing resource market of the edge computing service provider. Since
there is competition among miners, the allocative externalities (positive and
negative) are taken into account in the model. In our auction mechanism, we
maximize the social welfare while guaranteeing the truthfulness, individual
rationality and computational efficiency. Based on blockchain mining experiment
results, we define a hash power function that characterizes the probability of
successfully mining a block. Through extensive simulations, we evaluate the
performance of our auction mechanism which shows that our edge computing
resources market model can efficiently solve the social welfare maximization
problem for the edge computing service provider
Profit Maximization Auction and Data Management in Big Data Markets
A big data service is any data-originated resource that is offered over the
Internet. The performance of a big data service depends on the data bought from
the data collectors. However, the problem of optimal pricing and data
allocation in big data services is not well-studied. In this paper, we propose
an auction-based big data market model. We first define the data cost and
utility based on the impact of data size on the performance of big data
analytics, e.g., machine learning algorithms. The big data services are
considered as digital goods and uniquely characterized with "unlimited supply"
compared to conventional goods which are limited. We therefore propose a
Bayesian profit maximization auction which is truthful, rational, and
computationally efficient. The optimal service price and data size are obtained
by solving the profit maximization auction. Finally, experimental results on a
real-world taxi trip dataset show that our big data market model and auction
mechanism effectively solve the profit maximization problem of the service
provider.Comment: 6 pages, 9 figures. This paper was accepted by IEEE WCNC conference
in Dec. 201
MOEA/D with Adaptive Weight Adjustment
Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.</jats:p
Dual Auction Mechanism for Transaction Forwarding and Validation in Complex Wireless Blockchain Network
In traditional blockchain networks, transaction fees are only allocated to
full nodes (i.e., miners) regardless of the contribution of forwarding
behaviors of light nodes. However, the lack of forwarding incentive reduces the
willingness of light nodes to relay transactions, especially in the
energy-constrained Mobile Ad Hoc Network (MANET). This paper proposes a novel
dual auction mechanism to allocate transaction fees for forwarding and
validation behaviors in the wireless blockchain network. The dual auction
mechanism consists of two auction models: the forwarding auction and the
validation auction. In the forwarding auction, forwarding nodes use Generalized
First Price (GFP) auction to choose transactions to forward. Besides,
forwarding nodes adjust the forwarding probability through a no-regret
algorithm to improve efficiency. In the validation auction, full nodes select
transactions using Vickrey-Clarke-Grove (VCG) mechanism to construct the block.
We prove that the designed dual auction mechanism is Incentive Compatibility
(IC), Individual Rationality (IR), and Computational Efficiency (CE).
Especially, we derive the upper bound of the social welfare difference between
the social optimal auction and our proposed one. Extensive simulation results
demonstrate that the proposed dual auction mechanism decreases energy and
spectrum resource consumption and effectively improves social welfare without
sacrificing the throughput and the security of the wireless blockchain network