Blockchain-Enabled Cyber–Physical Systems: A Review

Zhao, Wenbing; Jiang, Congfeng; Gao, Honghao; Yang, Shunkun; Luo, Xiong

doi:10.1109/jiot.2020.3014864

Cited by 55 publications

(17 citation statements)

References 72 publications

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“…While there is much discussion about how blockchain can improve the efficiency and security of artificial intelligence [16], cyber-physical systems [17], and IoT [34], the opposite has not received as much attention. The primary reason for this is believed to be due to traditional blockchain's rigid data structure and execution, presenting very few improvements for machine learning besides improving efficiency with consensus calculations [35].…”

Section: A Machine Learning For Blockchain With Iot Devicesmentioning

confidence: 99%

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An Intelligent Distributed Ledger Construction Algorithm for IoT

Rawlins

Jagannathan

2022

IEEE Access

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Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to wellknown security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machinelearning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

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Section: A Machine Learning For Blockchain With Iot Devicesmentioning

confidence: 99%

“…Analysis and simulations of the proposed work show superior performance for confirming late transactions compared to other schemes. Based on the author's understanding, this is the first blockchain protocol to actively use machine-learning in decision-making, which is an open opportunity in general blockchain research [16,17].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Distributed Ledger Construction Algorithm for IoT

Rawlins

Jagannathan

2022

IEEE Access

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“…Blockchain is a decentralized and distributed method of recording and tracking digital interactions [5]. Zhao [6] describes how blockchain utilizes a chain-like data structure, which operates on a peer-to-peer network without a centralized trusted authority, and uses cryptography such as cryptographic hash and public-key cryptography. Each block contains various transactions, and blocks are chained together and have great redundancy.…”

Section: B Blockchainmentioning

confidence: 99%

“…Therefore, if any blocks are altered or removed, this can easily be identified, and this also makes it very difficult to damage information on the blockchain. Furthermore, blockchain uses the Proof of Work (PoW) algorithm, which is used to validate transactions and create new blocks on the chain through solving a complex mathematical puzzle [6].…”

Section: B Blockchainmentioning

confidence: 99%

“…Others focus on control and operation of blockchain-based CPS. Zhao et al dissected various blockchain-enabled CPS in terms of the operations and features utilized, and classified them according to the sensitivity and throughput in [6]. Kanhere addressed in [10] that, although a decentralized approach realizes the true potential of CPS taking the unique features into account, the application of blockchain for diverse CPS domains has its own complex challenges.…”

Section: Related Workmentioning

confidence: 99%

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A Literature Review on Blockchain-enabled Security and Operation of Cyber-Physical Systems

Khalil¹,

Franco²,

Parvez³

et al. 2021

Preprint

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Blockchain has become a key technology in a plethora of application domains owing to its decentralized public nature. The cyber-physical systems (CPS) is one of the prominent application domains that leverage blockchain for myriad operations, where the Internet of Things (IoT) is utilized for data collection. Although some of the CPS problems can be solved by simply adopting blockchain for its secure and distributed nature, others require complex considerations for overcoming blockchain-imposed limitations while maintaining the core aspect of CPS. Even though a number of studies focus on either the utilization of blockchains for different CPS applications or the blockchain-enabled security of CPS, there is no comprehensive survey including both perspectives together. To fill this gap, we present a comprehensive overview of contemporary advancement in using blockchain for enhancing different CPS operations as well as improving CPS security. To the best of our knowledge, this is the first paper that presents an in-depth review of research on blockchain-enabled CPS operation and security.

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Stylized image resolution enhancement scheme based on an improved convolutional neural network in cyber‐physical systems

Liu

Li²

2021

Concurrency and Computation

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Edge computing based on intelligent techniques provides powerful processing abilities in cyber‐physical systems (CPS), thereby enabling real‐time insights, and analyses for applications. To decrease network redundancy and improve the speed of convolutional neural networks (CNNs) when performing high‐resolution stylization in image style transfer tasks, a resolution enhancement scheme is proposed based on an improved CNN in CPS. A style transfer network (RESTN) model and a two‐stage neural network training method are presented. In the first stage, the resolution enhancement ability of RESTN is trained with high‐resolution and low‐resolution image pairs, and in the second stage, the style transfer ability of RESTN is supplemented. The parameters of the feature extraction phase are shared for learning different styles, and the octave convolution operation is adopted to reduce network redundancy. Experimental results show that compared with the traditional model‐based iterative method, the proposed RESTN model can conduct training three times faster and with half of the memory cost.

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Blockchain-Enabled Cyber–Physical Systems: A Review

Cited by 55 publications

References 72 publications

An Intelligent Distributed Ledger Construction Algorithm for IoT

An Intelligent Distributed Ledger Construction Algorithm for IoT

A Literature Review on Blockchain-enabled Security and Operation of Cyber-Physical Systems

Stylized image resolution enhancement scheme based on an improved convolutional neural network in cyber‐physical systems

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