Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning

Liu, Yang; Li, Meng; Si, Pengbo; Yang, Ruifu; Sun, Enchang; Zhang, Yanhua

doi:10.1109/jiot.2020.3030646

Cited by 73 publications

(48 citation statements)

References 36 publications

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“…However, managing the radio resources in such a system becomes an imperative issue. In this context, Yan et al [110] study the problem of power allocation for NOMA-enabled SIoT using a single-agent DQN-based DRL scheme. In their system, the agent is the satellite, whose action space is discrete, corresponding to selecting the power allocation coefficient for each NOMA user.…”

Section: ) In Iot and Other Emerging Wireless Networkmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

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<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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Section: ) In Iot and Other Emerging Wireless Networkmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

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“…Additionally, blockchain-enabled mobile edge computing mainly focused on resource allocation in 2018 and then there were more works related to energy consumption and privacy issues in 2019. This makes sense because IoT devices are energy-constrained, especially for those in industrial IoT systems [116].…”

Section: Summery Of Topics and Trendsmentioning

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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“…X. Li et al in [97] focus on minimizing the long-term cost of cooperative computing offloading, while league learning is introduced to promote offloading performance by enabling the hierarchical agents to explore the environment collaboratively. In [98], the BCenabled EI framework can reduce the computation overhead and the energy consumption of systems by jointly taking into account the selection of computing-power nodes, offloading decision and block size. In accordance with DNN, the DRL approach can handle the high-dynamic and large-dimensional offloading optimization problem by approximating the actionstate value of the agent.…”

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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Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning

Cited by 73 publications

References 36 publications

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

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

Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

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