4.7 Article

An Optimization Framework Based on Deep Reinforcement Learning Approaches for Prism Blockchain

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning

Rui Ma et al.

Summary: The article discusses the rapid development of the Internet of Things in the smart grid and the challenges it brings in terms of control and management. An autonomous incentive-based DCR control and management framework is proposed, which utilizes a model-free deep deterministic policy gradient method to achieve accurate active power adjustment and optimize DCR allocations, maximizing profits for users and system operators.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023)

Article Computer Science, Information Systems

Sharded Blockchain for Collaborative Computing in the Internet of Things: Combined of Dynamic Clustering and Deep Reinforcement Learning Approach

Zhaoxin Yang et al.

Summary: In this article, a clustering-based sharded blockchain strategy for collaborative computing in the IoT is proposed, which improves the scalability of sharded blockchains in IoT applications by optimizing the cluster number and adjusting the consensus parameters.

IEEE INTERNET OF THINGS JOURNAL (2022)

Article Computer Science, Hardware & Architecture

Reinforcement learning approaches for efficient and secure blockchain-powered smart health systems

Abeer Z. Al-Marridi et al.

Summary: Emerging technological innovation towards e-Health transition is a global priority for ensuring people's quality of life. The proposed Healthchain-RL framework combines Blockchain technology and artificial intelligence to optimize medical data exchange among heterogeneous healthcare organizations, addressing security, latency, and cost trade-offs effectively. By leveraging Deep Reinforcement Learning algorithms, the system achieves real-time adaptivity while maintaining maximum security and minimum latency and cost.

COMPUTER NETWORKS (2021)

Article Computer Science, Information Systems

DQN-Based Optimization Framework for Secure Sharded Blockchain Systems

Jusik Yun et al.

Summary: This article proposes a deep network shard-based blockchain scheme that dynamically finds the optimal throughput configuration by analyzing the latency and security-level characteristics of sharded blockchain. Using deep reinforcement learning, the system parameters are optimized to achieve higher TPS and security level by adapting to the network status. Simulation results show that this DQNSB scheme provides a higher TPS compared to existing DRL-enabled blockchain technology while maintaining a high security level.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Computer Science, Hardware & Architecture

Deep reinforcement learning for blockchain in industrial IoT: A survey

Yulei Wu et al.

Summary: With the ambitious plans of renewal and expansion of industrialization in many countries, the efficiency, agility, and cost savings potentially resulting from the application of Industrial Internet of Things (IIoT) are drawing attentions. Blockchain and machine learning technologies may provide the next promising use case for IIoT but they are working in an adversarial way to some extent, with a focus on data privacy and security risks.

COMPUTER NETWORKS (2021)

Article Computer Science, Theory & Methods

BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence

Sushil Kumar Singh et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2020)

Review Green & Sustainable Science & Technology

Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges

Furqan Jameel et al.

SUSTAINABILITY (2020)

Proceedings Paper Computer Science, Information Systems

Secure Regenerating Codes for Reducing Storage and Bootstrap Costs in Sharded Blockchains

Divija Swetha Gadiraju et al.

2020 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2020) (2020)

Proceedings Paper Computer Science, Information Systems

Prism Removes Consensus Bottleneck for Smart Contracts

Gerui Wang et al.

2020 CRYPTO VALLEY CONFERENCE ON BLOCKCHAIN TECHNOLOGY (CVCBT 2020) (2020)

Article Automation & Control Systems

Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach

Mengting Liu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Proceedings Paper Automation & Control Systems

On Analysis of the Bitcoin and Prism Backbone Protocols in Synchronous Networks

Jing Li et al.

2019 57TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON) (2019)

Proceedings Paper Computer Science, Information Systems

Prism: Deconstructing the Blockchain to Approach Physical Limits

Vivek Bagaria et al.

PROCEEDINGS OF THE 2019 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'19) (2019)

Proceedings Paper Computer Science, Theory & Methods

OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding

Eleftherios Kokoris-Kogias et al.

2018 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP) (2018)

Article Computer Science, Information Systems

Blockchain challenges and opportunities: a survey

Zibin Zheng et al.

INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES (2018)

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)