80 research outputs found
Reliable Federated Learning for Mobile Networks
Federated learning, as a promising machine learning approach, has emerged to
leverage a distributed personalized dataset from a number of nodes, e.g.,
mobile devices, to improve performance while simultaneously providing privacy
preservation for mobile users. In the federated learning, training data is
widely distributed and maintained on the mobile devices as workers. A central
aggregator updates a global model by collecting local updates from mobile
devices using their local training data to train the global model in each
iteration. However, unreliable data may be uploaded by the mobile devices
(i.e., workers), leading to frauds in tasks of federated learning. The workers
may perform unreliable updates intentionally, e.g., the data poisoning attack,
or unintentionally, e.g., low-quality data caused by energy constraints or
high-speed mobility. Therefore, finding out trusted and reliable workers in
federated learning tasks becomes critical. In this article, the concept of
reputation is introduced as a metric. Based on this metric, a reliable worker
selection scheme is proposed for federated learning tasks. Consortium
blockchain is leveraged as a decentralized approach for achieving efficient
reputation management of the workers without repudiation and tampering. By
numerical analysis, the proposed approach is demonstrated to improve the
reliability of federated learning tasks in mobile networks
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
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
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Mechanisms for improving information quality in smartphone crowdsensing systems
Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs.
Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii
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
A Survey on Off-chain Networks: Frameworks, Technologies, Solutions and Challenges
Blockchain has received increasing attention in academia and industry.
However, the increasing transaction volumes and limited on-chain storage
underscore scalability as a key challenge hindering the widespread adoption of
blockchain. Fortunately, off-chain networks that enable transactions outside
the blockchain show promising potential to mitigate the scalability challenge.
Off-chain solutions that address blockchain scalability hurdles, such as
payment channel networks, facilitate secure and fast off-chain transactions,
thus relieving the main chain's strain. In this article, we provide a
comprehensive review of key technologies, solutions, and challenges of
off-chain networks. First, we introduce the background of off-chain networks
encompassing design motivation, framework, overview, and application scenarios.
We then review the key issues and technologies associated with off-chain
networks. Subsequently, we summarize the mainstream solutions for the
corresponding key issues. Finally, we discuss some research challenges and open
issues in this area.Comment: 30 pages, 5 figure
Data trust framework using blockchain and smart contracts
Lack of trust is the main barrier preventing more widespread data sharing. The lack of transparent and reliable infrastructure for data sharing prevents many data owners from sharing their data.
Data trust is a paradigm that facilitates data sharing by forcing data controllers to be transparent about the process of sharing and reusing data.
Blockchain technology has the potential to present the essential properties for creating a practical and secure data trust framework by transforming current auditing practices and automatic enforcement of smart contracts logic without relying on intermediaries to establish trust.
Blockchain holds an enormous potential to remove the barriers of traditional centralized applications and propose a distributed and transparent administration by employing the involved parties to maintain consensus on the ledger. Furthermore, smart contracts are a programmable component that provides blockchain with more flexible and powerful capabilities. Recent advances in blockchain platforms toward smart contracts' development have revealed the possibility of implementing blockchain-based applications in various domains, such as health care, supply chain and digital identity.
This dissertation investigates the blockchain's potential to present a framework for data trust. It starts with a comprehensive study of smart contracts as the main component of blockchain for developing decentralized data trust.
Interrelated, three decentralized applications that address data sharing and access control problems in various fields, including healthcare data sharing, business process, and physical access control system, have been developed and examined.
In addition, a general-purpose application based on an attribute-based access control model is proposed that can provide trusted auditability required for data sharing and access control systems and, ultimately, a data trust framework. Besides auditing, the system presents a transparency level that both access requesters (data users) and resource owners (data controllers) can benefit from. The proposed solutions have been validated through a use case of independent digital libraries. It also provides a detailed performance analysis of the system implementation.
The performance results have been compared based on different consensus mechanisms and databases, indicating the system's high throughput and low latency.
Finally, this dissertation presents an end-to-end data trust framework based on blockchain technology.
The proposed framework promotes data trustworthiness by assessing input datasets, effectively managing access control, and presenting data provenance and activity monitoring. A trust assessment model that examines the trustworthiness of input data sets and calculates the trust value is presented.
The number of transaction validators is defined adaptively with the trust value.
This research provides solutions for both data owners and data users’ by ensuring the trustworthiness and quality of the data at origin and transparent and secure usage of the data at the end. A comprehensive experimental study indicates the presented system effectively handles a large number of transactions with low latency
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