45 research outputs found
Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory
In Internet of Vehicles (IoV), data sharing among vehicles is essential to
improve driving safety and enhance vehicular services. To ensure data sharing
security and traceability, highefficiency Delegated Proof-of-Stake consensus
scheme as a hard security solution is utilized to establish blockchain-enabled
IoV (BIoV). However, as miners are selected from miner candidates by
stake-based voting, it is difficult to defend against voting collusion between
the candidates and compromised high-stake vehicles, which introduces serious
security challenges to the BIoV. To address such challenges, we propose a soft
security enhancement solution including two stages: (i) miner selection and
(ii) block verification. In the first stage, a reputation-based voting scheme
for the blockchain is proposed to ensure secure miner selection. This scheme
evaluates candidates' reputation by using both historical interactions and
recommended opinions from other vehicles. The candidates with high reputation
are selected to be active miners and standby miners. In the second stage, to
prevent internal collusion among the active miners, a newly generated block is
further verified and audited by the standby miners. To incentivize the standby
miners to participate in block verification, we formulate interactions between
the active miners and the standby miners by using contract theory, which takes
block verification security and delay into consideration. Numerical results
based on a real-world dataset indicate that our schemes are secure and
efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio
Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends
Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
In recent years, mobile devices are equipped with increasingly advanced
sensing and computing capabilities. Coupled with advancements in Deep Learning
(DL), this opens up countless possibilities for meaningful applications.
Traditional cloudbased Machine Learning (ML) approaches require the data to be
centralized in a cloud server or data center. However, this results in critical
issues related to unacceptable latency and communication inefficiency. To this
end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer
to the edge, where data is produced. However, conventional enabling
technologies for ML at mobile edge networks still require personal data to be
shared with external parties, e.g., edge servers. Recently, in light of
increasingly stringent data privacy legislations and growing privacy concerns,
the concept of Federated Learning (FL) has been introduced. In FL, end devices
use their local data to train an ML model required by the server. The end
devices then send the model updates rather than raw data to the server for
aggregation. FL can serve as an enabling technology in mobile edge networks
since it enables the collaborative training of an ML model and also enables DL
for mobile edge network optimization. However, in a large-scale and complex
mobile edge network, heterogeneous devices with varying constraints are
involved. This raises challenges of communication costs, resource allocation,
and privacy and security in the implementation of FL at scale. In this survey,
we begin with an introduction to the background and fundamentals of FL. Then,
we highlight the aforementioned challenges of FL implementation and review
existing solutions. Furthermore, we present the applications of FL for mobile
edge network optimization. Finally, we discuss the important challenges and
future research directions in F
Securing SDN controlled IoT Networks Through Edge-Blockchain
The Internet of Things (IoT) connected by Software Defined Networking (SDN) promises to bring great benefits to cyber-physical systems. However, the increased attack surface offered by the growing number of connected vulnerable devices and separation of SDN control and data planes could overturn the huge benefits of such a system. This paper addresses the vulnerability of the trust relationship between the control and data planes. To meet this aim, we propose an edge computing based blockchain-as-a-service (BaaS), enabled by an external BaaS provider. The proposed solution provides verification of inserted flows through an efficient, edge-distributed, blockchain solution. We study two scenarios for the blockchain reward purpose: (a) information symmetry, in which the SDN operator has direct knowledge of the real effort spent by the BaaS provider; and (b) information asymmetry, in which the BaaS provider controls the exposure of information regarding spent effort. The latter yields the so called “moral hazard”, where the BaaS may claim higher than actual effort. We develop a novel mathematical model of the edge BaaS solution; and propose an innovative algorithm of a fair reward scheme based on game theory that takes into account moral hazard. We evaluate the viability of our solution through analytical simulations. The results demonstrate the ability of the proposed algorithm to maximize the joint profits of the BaaS and the SDN operator, i.e. maximizing the social welfare
APPLYING COLLABORATIVE ONLINE ACTIVE LEARNING IN VEHICULAR NETWORKS FOR FUTURE CONNECTED AND AUTONOMOUS VEHICLES
The main objective of this thesis is to provide a framework for, and proof of concept of, collaborative online active learning in vehicular networks. Another objective is to advance the state of the art in simulation-based evaluation and validation of connected intelligent vehicle applications. With advancements in machine learning and artificial intelligence, connected autonomous vehicles (CAVs) have begun to migrate from laboratory development and testing conditions to driving on public roads. Their deployment in our environmental landscape offers potential for decreases in road accidents and traffic congestion, as well as improved mobility in overcrowded cities. Although common driving scenarios can be relatively easily solved with classic perception, path planning, and motion control methods, the remaining unsolved scenarios are corner cases in which traditional methods fail. These unsolved cases are the keys to deploying CAVs safely on the road, but they require an enormous amount of data collection and high-quality human annotation, which are very cost-ineffective considering the ever-changing real-world scenarios and highly diverse road/weather conditions. Additionally, evaluating and testing applications for CAVs in real testbeds are extremely expensive, as obvious failures like crashes tend to be rare events and can hardly be captured through predefined test scenarios. Therefore, realistic simulation tools with the benefit of lower cost as well as generating reproducible experiment results are needed to complement the real testbeds in validating applications for CAVs. Therefore, in this thesis, we address the challenges therein and establish the fundamentals of the collaborative online active learning framework in vehicular network for future connected and autonomous vehicles.Ph.D
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
Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical Systems: An Integration of Blockchain and Edge Computing
Smart grid has emerged as a successful application of cyber-physical systems in the energy sector. Among numerous key technologies of the smart grid, vehicle-to-grid (V2G) provides a promising solution to reduce the level of demand-supply mismatch by leveraging the bidirectional energy-trading capabilities of electric vehicles. In this paper, we propose a secure and efficient V2G energy trading framework by exploring blockchain, contract theory, and edge computing. First, we develop a consortium blockchain-based secure energy trading mechanism for V2G. Then, we consider the information asymmetry scenario, and propose an efficient incentive mechanism based on contract theory. The social welfare optimization problem falls into the category of difference of convex programming and is solved by using the iterative convex-concave procedure algorithm. Next, edge computing has been incorporated to improve the successful probability of block creation. The computational resource allocation problem is modeled as a two-stage: 1) Stackelberg leader-follower game and 2) the optimal strategies are obtained by using the backward induction approach. Finally, the performance of the proposed framework is validated via numerical results and theoretical analysis