Deep Reinforcement Learning Empowered Adaptivity for Future Blockchain Networks

Qiu, Chao; Ren, Xiaoxu; Cao, Yifan; Mai, Tianle

doi:10.1109/ojcs.2020.3010987

Cited by 17 publications

(12 citation statements)

References 16 publications

(15 reference statements)

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“…Since the learning-based algorithms can learn the transition regularities of the system environment, the QoS requirements of the users, and the situation of enabling resources, it would provide a feasible way for BC to select more suitable protocols to perform. For example, the works in [169], [170] design a service-oriented permissioned BC and launch several consensus protocols based on the users' QoS requirements. Then, the consensus protocol selection, block producer selection, and bandwidth allocation can be formulated as the MDP, where the state, action and reward function are defined as follows.…”

Section: A Flexible Consensus Protocol Tailoring To Eimentioning

confidence: 99%

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Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang

Ren

Qiu

et al. 2022

IEEE Commun. Surv. Tutorials

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Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.Index Terms-Edge intelligence (EI), blockchain (BC), edge computing, decentralization, distributed ledger technology (DLT).

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Section: A Flexible Consensus Protocol Tailoring To Eimentioning

confidence: 99%

“…Unlike the traditional DRL approach, we leverage dueling DRL to learn the relative advantage of action, by evaluating the value function [170]. As shown in Fig.…”

Section: A Flexible Consensus Protocol Tailoring To Eimentioning

confidence: 99%

Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang

Ren

Qiu

et al. 2022

IEEE Commun. Surv. Tutorials

Self Cite

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“…However if one considers less computationally expensive algorithms like PoS, PBFT and DPoS, though they require less energy they are reasonable for enormous scope frameworks. A new algorithm proof of trust has been proposed to address this issue but still it needs to be investigated [59].…”

Section: Challengesmentioning

confidence: 99%

Approaches towards Blockchain Innovation: A Survey and Future Directions

2021

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A blockchain is a decentralized peer to peer platform which provides security services based on some key concepts, namely authentication, confidentiality, integrity and authorization. It is the process of recording and keeping track of the resources without the intervention of a centralized authority. This paper provides an overview of blockchains, the structure of blockchains, consensus algorithms, etc., It compares the algorithms based on their utility and limitations. Though blockchains provide secure communication, there are some minimal data leaks which are discussed. Various security issues in blockchains are discussed such as denial of service attacks, etc., In addition to security, some other blockchain challenges are presented like scalability, reliability, interoperability, privacy and consensus mechanisms for integration with AI, IoT and edge computing. This paper also explains about the importance of blockchains in the fields of smart healthcare, smart grid, and smart financial systems. Overall, this paper gives the glimpse of various protocols, algorithms, applications, challenges and opportunities that are found in the blockchain domain.

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“…Recently, EI helps the BC realize the pluggable consensus mechanism, which switches the different protocols flexibly to meet the diversified QoS requirements of edge scenarios, while making the BC system more efficient and robust [70], [138]. Since the learning-based algorithms can learn the transition regularities of the system environment, the QoS requirements of the users, and the situation of enabling resources, it would provide a feasible way for BC to select more suitable protocols to perform.…”

Section: A Flexible Consensus Protocolmentioning

confidence: 99%

“…Furthermore, it is addressed by a dueling DRL algorithm to achieve a service-oriented BC and enhance the performance of the BC system. Similarly, in [138], the authors focus on the adaptivity for BC, and quantify four consensus protocols, instead of using only one of them. Each protocol's qualitative performance, including throughput and latency, is analyzed for selecting suitable consensus protocols to meet multiple users' QoS requirements.…”

Section: A Flexible Consensus Protocolmentioning

confidence: 99%

Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

Wang¹,

Ren²,

Qiu³

et al. 2021

Preprint

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Edge intelligence (EI), as an emerging technology, has been attracting significant attention. It pushes the frontier of artificial intelligence (AI) from the cloud to the network edge, aiming to embrace and support the next-generation communications while unleashing AI services. However, it faces challenges in its decentralized management and security, which limit its capabilities to support services with numerous requirements. On the other side, blockchain (BC), as a promising decentralized technology, is beneficial to tackle the above issues. However, there exist some technical challenges for BC, such as transaction capacity, scalability, and fault tolerance. Motivated by significant current interest around EI and BC, this survey examines whether the synergy of EI and BC can make a powerful network with combined functionalities of both cutting-edge technologies. Accordingly, we develop EI-chain and Chain-intelligence to realize reliable computing-power management, data administration, and model optimization at the edges, while improving the functions of BC by leveraging complementary characteristics of EI and BC, further making up for their own limitations. In this survey, a wide spectrum of literature is carefully reviewed to enable the EI-chain and Chain-intelligence. Moreover, we cover the technological aspects of EI-chain and Chain-intelligence: overview, motivations, and frameworks. Finally, some challenges and future directions are explored. We believe this survey will provide developers and researchers a comprehensive view on the synergy of EI and BC, while accelerating the design of a powerful integrated network.

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Deep Reinforcement Learning Empowered Adaptivity for Future Blockchain Networks

Cited by 17 publications

References 16 publications

Integrating Edge Intelligence and Blockchain: What, Why, and How

Integrating Edge Intelligence and Blockchain: What, Why, and How

Approaches towards Blockchain Innovation: A Survey and Future Directions

Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

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