Journal ArticleDOI
Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning
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TLDR
This article integrates mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability ofIIoT devices and improve the efficiency of the consensus process and introduces deep reinforcement learning (DRL) to solve the formulated problem.Abstract:
Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.read more
Citations
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Applications of Blockchain in Industry 4.0: a Review
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Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios
TL;DR: A new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP) and can enable safe and secure cloud/edge/IoT offloading by employing blockchain.
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Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
TL;DR: In this paper, a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area, and some open issues and research gaps for future investigations are discussed.
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The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions
TL;DR: A systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.
Journal ArticleDOI
Great partners: how deep learning and blockchain help improve business operations together
Suyuan Luo,Tsan-Ming Choi +1 more
TL;DR: This paper aims to investigate how deep learning and BKC together can help improve business operations, and explores deep learning’s applications for BKC, BKC's applications for deep learning as well as how deepLearning and B KC have been used together for business operations.
References
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Journal ArticleDOI
Internet of Things in Industries: A Survey
Li Da Xu,Wu He,Shancang Li +2 more
TL;DR: This review paper summarizes the current state-of-the-art IoT in industries systematically and identifies research trends and challenges.
Journal ArticleDOI
Deep Reinforcement Learning: A Brief Survey
TL;DR: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world as discussed by the authors.
Journal ArticleDOI
A brief survey of deep reinforcement learning
TL;DR: This survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic, and highlight the unique advantages of deep neural networks, focusing on visual understanding via RL.
Journal ArticleDOI
Industrial Internet of Things: Challenges, Opportunities, and Directions
TL;DR: The concepts of IoT, Industrial IoT, and Industry 4.0 are clarified and the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy are focused on.
Journal ArticleDOI
Blockchain for Internet of Things: A Survey
TL;DR: An in-depth survey of BCoT is presented and the insights of this new paradigm are discussed and the open research directions in this promising area are outlined.