14,265 research outputs found
Cloud/fog computing resource management and pricing for blockchain networks
The mining process in blockchain requires solving a proof-of-work puzzle,
which is resource expensive to implement in mobile devices due to the high
computing power and energy needed. In this paper, we, for the first time,
consider edge computing as an enabler for mobile blockchain. In particular, we
study edge computing resource management and pricing to support mobile
blockchain applications in which the mining process of miners can be offloaded
to an edge computing service provider. We formulate a two-stage Stackelberg
game to jointly maximize the profit of the edge computing service provider and
the individual utilities of the miners. In the first stage, the service
provider sets the price of edge computing nodes. In the second stage, the
miners decide on the service demand to purchase based on the observed prices.
We apply the backward induction to analyze the sub-game perfect equilibrium in
each stage for both uniform and discriminatory pricing schemes. For the uniform
pricing where the same price is applied to all miners, the existence and
uniqueness of Stackelberg equilibrium are validated by identifying the best
response strategies of the miners. For the discriminatory pricing where the
different prices are applied to different miners, the Stackelberg equilibrium
is proved to exist and be unique by capitalizing on the Variational Inequality
theory. Further, the real experimental results are employed to justify our
proposed model.Comment: 16 pages, double-column version, accepted by IEEE Internet of Things
Journa
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Socially Trusted Collaborative Edge Computing in Ultra Dense Networks
Small cell base stations (SBSs) endowed with cloud-like computing
capabilities are considered as a key enabler of edge computing (EC), which
provides ultra-low latency and location-awareness for a variety of emerging
mobile applications and the Internet of Things. However, due to the limited
computation resources of an individual SBS, providing computation services of
high quality to its users faces significant challenges when it is overloaded
with an excessive amount of computation workload. In this paper, we propose
collaborative edge computing among SBSs by forming SBS coalitions to share
computation resources with each other, thereby accommodating more computation
workload in the edge system and reducing reliance on the remote cloud. A novel
SBS coalition formation algorithm is developed based on the coalitional game
theory to cope with various new challenges in small-cell-based edge systems,
including the co-provisioning of radio access and computing services,
cooperation incentives, and potential security risks. To address these
challenges, the proposed method (1) allows collaboration at both the user-SBS
association stage and the SBS peer offloading stage by exploiting the ultra
dense deployment of SBSs, (2) develops a payment-based incentive mechanism that
implements proportionally fair utility division to form stable SBS coalitions,
and (3) builds a social trust network for managing security risks among SBSs
due to collaboration. Systematic simulations in practical scenarios are carried
out to evaluate the efficacy and performance of the proposed method, which
shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author
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