A Reinforcement Learning and Blockchain-Based Trust Mechanism for Edge Networks

Xiao, Liang; Ding, Yong; Jiang, Donghua; Huang, Jinhao; Wang, Dongming; Li, Jie; Poor, H. Vincent

doi:10.1109/tcomm.2020.2995371

Cited by 99 publications

(29 citation statements)

References 34 publications

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“…The trade-off and optimization among energy consumption, privacy, and latency were jointly considered. Furthermore, [29] investigated the trust mechanism for edge network by using blockchain technology. Selfish edge attacks were discussed in this paper.…”

Section: Tasks Offloading In Blockchain Systemsmentioning

confidence: 99%

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Du¹,

Miao²,

Tong³

et al. 2022

Blockchain Potential in AI

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Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence.

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Section: Tasks Offloading In Blockchain Systemsmentioning

confidence: 99%

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Du¹,

Miao²,

Tong³

et al. 2022

Blockchain Potential in AI

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show abstract

“…Furthermore, blockchain was introduced as a strong security mechanism for MEC systems in vehicular networks [34]. In addition, Reference [35] introduced a blockchain-based trust mechanism for MEC systems. By establishing a reputation system for the edge nodes, the miner in the blockchain network was, thus, selected in a trusted manner.…”

Section: Integration Of Mec and Dltmentioning

confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

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“…(22). In the following, we show that a set of control policies obtained by Algorithm 2 and a well-trained orchestrator together minimize the statistical distance of normal and target environments, as represented by the hierarchical learning objective in Eq (30).…”

Section: Policy Transferabilitymentioning

confidence: 99%

“…We set longrange dynamic map information such as the trajectories of moving objects as near-edge global data. This configuration is based on one of the edge computing scenarios [29], [30]. Furthermore, the dynamic map information is updated to the device, similarly to the learning map scenario [31], where update periods are randomly given.…”

Section: B Case Studymentioning

confidence: 99%

“…The recent advance of reinforcement learning (RL) has yielded its broad adoption in the area of autonomous cyberphysical systems that operate through interacting with surrounding environments, e.g., autonomous driving [1], [2], robots [3], [32], [33], drones [4], [34], mobile edge computing [30], and others. Such an RL-based system senses environment states, aiming to make timely observations and conduct proper reactions.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

rocorl: Transferable Reinforcement Learning-Based Robust Control for Cyber-Physical Systems With Limited Data Updates

Yoo

Yeom

et al. 2020

IEEE Access

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A Reinforcement Learning and Blockchain-Based Trust Mechanism for Edge Networks

Cited by 99 publications

References 34 publications

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

rocorl: Transferable Reinforcement Learning-Based Robust Control for Cyber-Physical Systems With Limited Data Updates

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