Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT With Edge Computing: A Deep Reinforcement Learning Approach

Li, Meng; Yu, F. Richard; Si, Pengbo; Wu, Wenjun; Zhang, Yanhua

doi:10.1109/jiot.2020.3007869

Cited by 87 publications

(36 citation statements)

References 44 publications

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“…Joint delay and blockchain-based security optimization has been presented in [69], focusing on (machine-tomachine) M2M communication. As blockchain systems require increased computation time to complete the smart contracts, delay requirements might not be met [70].…”

Section: ) Delay Reductionmentioning

confidence: 99%

“…Furthermore, in decentralized learning paradigms, such as federated learning which better suit privacy sensitive applications, it is necessary to ensure that shared models will be based on information exchange among trustworthy peers. In this area, recent works have adopted blockchains and smart contracts, highlighting their efficiency in M2M, D2D and V2V RL-aided edge caching but still, further advancements are needed [69], [139], [140], [152], [153]. In addition, further adoption of FL can alleviate privacy concerns, as for example in F-RANs where data from IoT devices are collected and at central servers for content popularity prediction.…”

Section: E Security Privacy and Trustmentioning

confidence: 99%

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A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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Section: ) Delay Reductionmentioning

confidence: 99%

Section: E Security Privacy and Trustmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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“…Other concepts are based on the integration of blockchain and edge computing. These include concepts based on deep reinforcement learning [47], artificial intelligence [48,49], and video surveillance systems [50]. Most of these studies used permissionless blockchain, which does not provide adequate privacy.…”

Section: Blockchain-based Edge Computing Iot Architecturementioning

confidence: 99%

BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems

Ajayi

Rafferty

Santos

et al. 2021

IoT

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The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.

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“…The optimal amount of computational resources allocated to devices would be formulated as a markov decision process (MDP) and solved by a RL-based algorithm, while a DRL version is presented to promote the computational performance further. By jointly considering the tasks caching, computing, as well as BC system usage, the work in [91] presents a novel resource allocation framework to reduce the unnecessary latency, while promoting the caching efficiency and system security in machine-to-machine communications. After data computing and processing, they can be uploaded into the BC, while being authorized by the consensus layer.…”

Section: Computing Devices (Cd)mentioning

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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Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT With Edge Computing: A Deep Reinforcement Learning Approach

Cited by 87 publications

References 44 publications

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems

Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

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