66 research outputs found
Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions
In recent years, low-carbon transportation has become an indispensable part
as sustainable development strategies of various countries, and plays a very
important responsibility in promoting low-carbon cities. However, the security
of low-carbon transportation has been threatened from various ways. For
example, denial of service attacks pose a great threat to the electric vehicles
and vehicle-to-grid networks. To minimize these threats, several methods have
been proposed to defense against them. Yet, these methods are only for certain
types of scenarios or attacks. Therefore, this review addresses security aspect
from holistic view, provides the overview, challenges and future directions of
cyber security technologies in low-carbon transportation. Firstly, based on the
concept and importance of low-carbon transportation, this review positions the
low-carbon transportation services. Then, with the perspective of network
architecture and communication mode, this review classifies its typical attack
risks. The corresponding defense technologies and relevant security suggestions
are further reviewed from perspective of data security, network management
security and network application security. Finally, in view of the long term
development of low-carbon transportation, future research directions have been
concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable
Energy Review
BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models
In recent years, artificial intelligence (AI) and machine learning (ML) are
reshaping society's production methods and productivity, and also changing the
paradigm of scientific research. Among them, the AI language model represented
by ChatGPT has made great progress. Such large language models (LLMs) serve
people in the form of AI-generated content (AIGC) and are widely used in
consulting, healthcare, and education. However, it is difficult to guarantee
the authenticity and reliability of AIGC learning data. In addition, there are
also hidden dangers of privacy disclosure in distributed AI training. Moreover,
the content generated by LLMs is difficult to identify and trace, and it is
difficult to cross-platform mutual recognition. The above information security
issues in the coming era of AI powered by LLMs will be infinitely amplified and
affect everyone's life. Therefore, we consider empowering LLMs using blockchain
technology with superior security features to propose a vision for trusted AI.
This paper mainly introduces the motivation and technical route of blockchain
for LLM (BC4LLM), including reliable learning corpus, secure training process,
and identifiable generated content. Meanwhile, this paper also reviews the
potential applications and future challenges, especially in the frontier
communication networks field, including network resource allocation, dynamic
spectrum sharing, and semantic communication. Based on the above work combined
and the prospect of blockchain and LLMs, it is expected to help the early
realization of trusted AI and provide guidance for the academic community
Novel Charging and Discharging Schemes for Electric Vehicles in Smart Grids
PhD ThesisThis thesis presents smart Charging and Discharging (C&D) schemes in the smart grid that enable a decentralised scheduling with large volumes of Electric Vehicles (EV) participation. The proposed C&D schemes use di erent strategies to atten the power consumption pro le by manipulating the charging or discharging electricity quantity. The novelty of this thesis lies in: 1. A user-behaviour based smart EV charging scheme that lowers the overall peak demand with an optimised EV charging schedule. It achieves the minimal impacts on users' daily routine while satisfying EV charging demands. 2. A decentralised EV electricity exchange process matches the power demand with an adaptive blockchain-enabled C&D scheme and iceberg order execution algorithm. It demonstrates improved performance in terms of charging costs and power consumption pro le. 3. The Peer-to-Peer (P2P) electricity C&D scheme that stimulates the trading depth and energy market pro le with the best price guide. It also increases the EV users' autonomy and achieved maximal bene ts for the network peers while protecting against potential attacks. 4. A novel consensus-mechanism driven EV C&D scheme for the blockchain-based system that accommodates high volume EV scenarios and substantially reduces the power uctuation level. The theoretical and comprehensive simulations prove that the penetration of EV with the proposed schemes minimises the power uctuation level in an urban area, and also increases the resilience of the smart grid system
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the
sensitivity of energy data, particularly when used to train machine learning
models for different services. These data-driven models often require huge
amounts of data to achieve acceptable performance leading in most cases to
risks of privacy leakage. By pushing the training to the edge, Federated
Learning (FL) offers a good compromise between privacy preservation and the
predictive performance of these models. The current paper presents an overview
of FL applications in SGs while discussing their advantages and drawbacks,
mainly in load forecasting, electric vehicles, fault diagnoses, load
disaggregation and renewable energies. In addition, an analysis of main design
trends and possible taxonomies is provided considering data partitioning, the
communication topology, and security mechanisms. Towards the end, an overview
of main challenges facing this technology and potential future directions is
presented
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
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
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