146,403 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
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Mobile landmark search (MLS) recently receives increasing attention for its
great practical values. However, it still remains unsolved due to two important
challenges. One is high bandwidth consumption of query transmission, and the
other is the huge visual variations of query images sent from mobile devices.
In this paper, we propose a novel hashing scheme, named as canonical view based
discrete multi-modal hashing (CV-DMH), to handle these problems via a novel
three-stage learning procedure. First, a submodular function is designed to
measure visual representativeness and redundancy of a view set. With it,
canonical views, which capture key visual appearances of landmark with limited
redundancy, are efficiently discovered with an iterative mining strategy.
Second, multi-modal sparse coding is applied to transform visual features from
multiple modalities into an intermediate representation. It can robustly and
adaptively characterize visual contents of varied landmark images with certain
canonical views. Finally, compact binary codes are learned on intermediate
representation within a tailored discrete binary embedding model which
preserves visual relations of images measured with canonical views and removes
the involved noises. In this part, we develop a new augmented Lagrangian
multiplier (ALM) based optimization method to directly solve the discrete
binary codes. We can not only explicitly deal with the discrete constraint, but
also consider the bit-uncorrelated constraint and balance constraint together.
Experiments on real world landmark datasets demonstrate the superior
performance of CV-DMH over several state-of-the-art methods
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