34 research outputs found
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
Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain
As the core issue of blockchain, the mining requires solving a proof-of-work
puzzle, which is resource expensive to implement in mobile devices due to high
computing power needed. Thus, the development of blockchain in mobile
applications is restricted. In this paper, we consider the edge computing as
the network enabler for mobile blockchain. In particular, we study optimal
pricing-based edge computing resource management to support mobile blockchain
applications where the mining process can be offloaded to an Edge computing
Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly
maximize the profit of the ESP and the individual utilities of different
miners. In Stage I, the ESP sets the price of edge computing services. In Stage
II, 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 uniform and discriminatory pricing schemes.
Further, the existence and uniqueness of Stackelberg game are validated for
both pricing schemes. At last, the performance evaluation shows that the ESP
intends to set the maximum possible value as the optimal price for profit
maximization under uniform pricing. In addition, the discriminatory pricing
helps the ESP encourage higher total service demand from miners and achieve
greater profit correspondingly.Comment: 7 pages, submitted to one conference. arXiv admin note: substantial
text overlap with arXiv:1710.0156
Competition and Cooperation Analysis for Data Sponsored Market: A Network Effects Model
The data sponsored scheme allows the content provider to cover parts of the
cellular data costs for mobile users. Thus the content service becomes
appealing to more users and potentially generates more profit gain to the
content provider. In this paper, we consider a sponsored data market with a
monopoly network service provider, a single content provider, and multiple
users. In particular, we model the interactions of three entities as a
two-stage Stackelberg game, where the service provider and content provider act
as the leaders determining the pricing and sponsoring strategies, respectively,
in the first stage, and the users act as the followers deciding on their data
demand in the second stage. We investigate the mutual interaction of the
service provider and content provider in two cases: (i) competitive case, where
the content provider and service provider optimize their strategies separately
and competitively, each aiming at maximizing the profit and revenue,
respectively; and (ii) cooperative case, where the two providers jointly
optimize their strategies, with the purpose of maximizing their aggregate
profits. We analyze the sub-game perfect equilibrium in both cases. Via
extensive simulations, we demonstrate that the network effects significantly
improve the payoff of three entities in this market, i.e., utilities of users,
the profit of content provider and the revenue of service provider. In
addition, it is revealed that the cooperation between the two providers is the
best choice for all three entities.Comment: 7 pages, submitted to one conferenc
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
On Cyber Risk Management of Blockchain Networks: A Game Theoretic Approach
Open-access blockchains based on proof-of-work protocols have gained
tremendous popularity for their capabilities of providing decentralized
tamper-proof ledgers and platforms for data-driven autonomous organization.
Nevertheless, the proof-of-work based consensus protocols are vulnerable to
cyber-attacks such as double-spending. In this paper, we propose a novel
approach of cyber risk management for blockchain-based service. In particular,
we adopt the cyber-insurance as an economic tool for neutralizing cyber risks
due to attacks in blockchain networks. We consider a blockchain service market,
which is composed of the infrastructure provider, the blockchain provider, the
cyber-insurer, and the users. The blockchain provider purchases from the
infrastructure provider, e.g., a cloud, the computing resources to maintain the
blockchain consensus, and then offers blockchain services to the users. The
blockchain provider strategizes its investment in the infrastructure and the
service price charged to the users, in order to improve the security of the
blockchain and thus optimize its profit. Meanwhile, the blockchain provider
also purchases a cyber-insurance from the cyber-insurer to protect itself from
the potential damage due to the attacks. In return, the cyber-insurer adjusts
the insurance premium according to the perceived risk level of the blockchain
service. Based on the assumption of rationality for the market entities, we
model the interaction among the blockchain provider, the users, and the
cyber-insurer as a two-level Stackelberg game. Namely, the blockchain provider
and the cyber-insurer lead to set their pricing/investment strategies, and then
the users follow to determine their demand of the blockchain service.
Specifically, we consider the scenario of double-spending attacks and provide a
series of analytical results about the Stackelberg equilibrium in the market
game
Securing Large-Scale D2D Networks Using Covert Communication and Friendly Jamming
We exploit both covert communication and friendly jamming to propose a
friendly jamming-assisted covert communication and use it to doubly secure a
large-scale device-to-device (D2D) network against eavesdroppers (i.e.,
wardens). The D2D transmitters defend against the wardens by: 1) hiding their
transmissions with enhanced covert communication, and 2) leveraging friendly
jamming to ensure information secrecy even if the D2D transmissions are
detected. We model the combat between the wardens and the D2D network (the
transmitters and the friendly jammers) as a two-stage Stackelberg game.
Therein, the wardens are the followers at the lower stage aiming to minimize
their detection errors, and the D2D network is the leader at the upper stage
aiming to maximize its utility (in terms of link reliability and communication
security) subject to the constraint on communication covertness. We apply
stochastic geometry to model the network spatial configuration so as to conduct
a system-level study. We develop a bi-level optimization algorithm to search
for the equilibrium of the proposed Stackelberg game based on the successive
convex approximation (SCA) method and Rosenbrock method. Numerical results
reveal interesting insights. We observe that without the assistance from the
jammers, it is difficult to achieve covert communication on D2D transmission.
Moreover, we illustrate the advantages of the proposed friendly
jamming-assisted covert communication by comparing it with the
information-theoretical secrecy approach in terms of the secure communication
probability and network utility
Achieving Covert Communication in Large-Scale SWIPT-Enabled D2D Networks
We aim to secure a large-scale device-to-device (D2D) network against
adversaries. The D2D network underlays a downlink cellular network to reuse the
cellular spectrum and is enabled for simultaneous wireless information and
power transfer (SWIPT). In the D2D network, the transmitters communicate with
the receivers, and the receivers extract information and energy from their
received radio-frequency (RF) signals. In the meantime, the adversaries aim to
detect the D2D transmission. The D2D network applies power control and
leverages the cellular signal to achieve covert communication (i.e., hide the
presence of transmissions) so as to defend against the adversaries. We model
the interaction between the D2D network and adversaries by using a two-stage
Stackelberg game. Therein, the adversaries are the followers minimizing their
detection errors at the lower stage and the D2D network is the leader
maximizing its network utility constrained by the communication covertness and
power outage at the upper stage. Both power splitting (PS)-based and time
switch (TS)-based SWIPT schemes are explored. We characterize the spatial
configuration of the large-scale D2D network, adversaries, and cellular network
by stochastic geometry. We analyze the adversary's detection error minimization
problem and adopt the Rosenbrock method to solve it, where the obtained
solution is the best response from the lower stage. Taking into account the
best response from the lower stage, we develop a bi-level algorithm to solve
the D2D network's constrained network utility maximization problem and obtain
the Stackelberg equilibrium. We present numerical results to reveal interesting
insights