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Electronics
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AI in Blockchain Assisted Cyber-Physical Systems
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AI in Blockchain Assisted Cyber-Physical Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 3753

Special Issue Editors

Prof. Dr. Ning Xi

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi’an 710071, China
Interests: network security; IoT security; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Dawei Wei

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi’an 710071, China
Interests: AI; network security; blockchain
Dr. Yuanyu Zhang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: AI; blockchain; IoT security

Special Issue Information

Dear Colleagues,

Cyber-physical systems (CPSs), such as smart cars, Industrial Internet of Things (IIoT), and unmanned aerial vehicles (UAVs), are providing a new paradigm for industrial manufacturing, city management, transportation, entertainment, and so on. With the expansion of CPS scale, the distributed network architecture is being widely applied rather than the original centralized network due to its autonomy and reliability. The emergence of blockchain helps CPSs execute in a decentralized, secure, and trusted manner. However, the issue of how to efficiently and automatically manage the blockchain-assisted CPS has become a problem. Artificial intelligence (AI) has demonstrated the ability to make intelligent decisions in multiple fields. It is becoming a hot topic to integrate AI into blockchain-assisted CPSs. Therefore, this Special Issue solicits innovative research on the integration of AI and blockchain-assisted CPSs, including but not limited to the following topics:

  1. AI-enhanced architecture for blockchain-assisted CPSs;
  2. The integration of federated AI and blockchain-assisted CPSs;
  3. The intelligent data management of blockchain in CPSs;
  4. The security and privacy of AI in blockchain-assisted CPSs;
  5. AI-enhanced consensus protocols for blockchain-assisted CPSs;
  6. The AI-assisted verification of smart contracts;
  7. Application cases in typical CPSs such as IIoT and UAVs.

Prof. Dr. Ning Xi
Dr. Dawei Wei
Dr. Yuanyu Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • blockchain
  • cyber-physical systems
  • security
  • industrial Internet of Things
  • UAV networks

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Published Papers (3 papers)

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Research

18 pages, 1123 KiB  
Article
Intelligent Upgrading of the Localized GNSS Monitoring System: Profound Integration of Blockchain and AI
by Tianzeng Lu, Yanan Sun, Qinglin Zhu, Xiaolin Zhou, Qiaoyang Li and Jianan Liu
Electronics 2025, 14(3), 490; https://doi.org/10.3390/electronics14030490 - 25 Jan 2025
Viewed by 812
Abstract
With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, [...] Read more.
With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns. Full article
(This article belongs to the Special Issue AI in Blockchain Assisted Cyber-Physical Systems)
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21 pages, 617 KiB  
Article
Unleashing the Potential of Permissioned Blockchain: Addressing Privacy, Security, and Interoperability Concerns in Healthcare Data Management
by Delowar Hossain, Quazi Mamun and Rafiqul Islam
Electronics 2024, 13(24), 5050; https://doi.org/10.3390/electronics13245050 - 23 Dec 2024
Viewed by 1511
Abstract
Blockchain technology leverages a cryptographic system to provide secure and immutable storage of transaction histories within a decentralised framework. While various industries have demonstrated interest in integrating blockchain into their IT systems, concerns regarding accessibility, privacy, performance, and scalability persist. Permissioned blockchain frameworks [...] Read more.
Blockchain technology leverages a cryptographic system to provide secure and immutable storage of transaction histories within a decentralised framework. While various industries have demonstrated interest in integrating blockchain into their IT systems, concerns regarding accessibility, privacy, performance, and scalability persist. Permissioned blockchain frameworks offer a viable solution for securing confidential records. Extensive research has been conducted to explore the opportunities, challenges, application areas, and performance evaluations of different public and permissioned blockchain platforms. Given the sensitive nature of medical information, healthcare organisations must adhere to various legal obligations, including HIPAA regulations, to protect these data. Although navigating these requirements can be challenging, it is crucial for safeguarding the reputation of healthcare providers, maintaining patient trust, and avoiding legal repercussions. Permissioned blockchains represent decentralised digital ledgers tailored to collaborate among businesses and organisations. Their popularity has increased significantly in recent years, resulting in the availability of several leading options, such as Hyperledger Fabric, Corda, Quorum, and MultiChain. Each of these platforms presents its own set of advantages and disadvantages. Although blockchain technology remains relatively nascent in the permissioned realm, several factors warrant consideration when comparing these platforms. This study will review the existing landscape of blockchain technologies in healthcare applications and identify the research scopes. This research aims to determine how permissioned blockchain technology can effectively fulfil the requirements for managing healthcare data. Full article
(This article belongs to the Special Issue AI in Blockchain Assisted Cyber-Physical Systems)
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17 pages, 1178 KiB  
Article
LFL-COBC: Lightweight Federated Learning on Blockchain-Based Device Contribution Allocation
by Qiaoyang Li, Yanan Sun, Ke Gao, Ning Xi, Xiaolin Zhou, Mingyan Wang and Kefeng Fan
Electronics 2024, 13(22), 4395; https://doi.org/10.3390/electronics13224395 - 9 Nov 2024
Cited by 1 | Viewed by 850
Abstract
In the distributed cyber-physical systems (CPSs) within the industrial domain, the volume of data produced by interconnected devices is escalating at an unprecedented pace, presenting novel opportunities to enhance service quality through data sharing. Nevertheless, data privacy protection emerges as a significant challenge [...] Read more.
In the distributed cyber-physical systems (CPSs) within the industrial domain, the volume of data produced by interconnected devices is escalating at an unprecedented pace, presenting novel opportunities to enhance service quality through data sharing. Nevertheless, data privacy protection emerges as a significant challenge for data providers in wireless networks. This paper puts forward a solution integrating blockchain and lightweight federated learning, designated as LFL-COBC, which aims to tackle the issues related to data privacy and device performance optimization. We initially analyze multiple dimensions influencing the performance of computing devices, such as mining capacity, data quality, computational efficiency and local device deviation, which are crucial for augmenting user engagement. Based on these dimensions, we deduce a set of cooperation strategies for selecting the optimal committee members and rewarding the contributions of node devices equitably, thereby stimulating cooperation between users and servers. To intelligently and automatically detect device anomalies and alleviate the operational burden, a convolutional neural network (CNN) model is employed. Additionally, to address the escalating cost of customer participation and the potential data explosion issue, a near-optimal model pruning algorithm is designed. This algorithm can make the model obtained from the training of node equipment lightweight, thereby reducing the load of federated learning and the blockchain, as well as enhancing the overall efficiency of the system. The efficacy of our approach is demonstrated through numerical experiments on the HDFS and BGL public data sets. Experimental results indicate that the LFL-COBC scheme can effectively safeguard data privacy and optimize device performance concurrently, providing an effective solution for device anomaly detection in CPSs. Full article
(This article belongs to the Special Issue AI in Blockchain Assisted Cyber-Physical Systems)
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