Fast Determination of Optimal Transmission Rate for Wireless Blockchain Networks: A Graph Convolutional Neural Network Approach

One of the primary challenges in wireless blockchain networks is to ensure security and high throughput with constrained communication and energy resources. In this paper, with curve fitting on the collected blockchain performance dataset, we explore the impact of the data transmission rate configuration on the wireless blockchain system under different network topologies, and give the blockchain a utility function which balances the throughput, energy efficiency, and stale rate. For efficient blockchain network deployment, we propose a novel Graph Convolutional Neural Network (GCN)-based approach to quickly and accurately determine the optimal data transmission rate. The experimental results demonstrate that the average relative deviation between the blockchain utility obtained by our GCN-based method and the optimal utility is less than 0.21%.

[1]  Jianchun Li,et al.  Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion , 2023, Developments in the Built Environment.

[2]  Jianchun Li,et al.  Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network , 2022, Engineering Structures.

[3]  Kun Xu,et al.  Meta-Regulation: Adaptive Adjustment to Block Size and Creation Interval for Blockchain Systems , 2022, IEEE Journal on Selected Areas in Communications.

[4]  Jiawen Kang,et al.  Connectivity-Aware Contract for Incentivizing IoT Devices in Complex Wireless Blockchain , 2022, IEEE Internet of Things Journal.

[5]  Zibin Zheng,et al.  FA-GNN: Filter and Augment Graph Neural Networks for Account Classification in Ethereum , 2022, IEEE Transactions on Network Science and Engineering.

[6]  Lei Zhang,et al.  How Much Communication Resource is Needed to Run a Wireless Blockchain Network? , 2021, IEEE Network.

[7]  Dong In Kim,et al.  Toward an Automated Auction Framework for Wireless Federated Learning Services Market , 2019, IEEE Transactions on Mobile Computing.

[8]  Shahid Mumtaz,et al.  When Internet of Things Meets Blockchain: Challenges in Distributed Consensus , 2019, IEEE Network.

[9]  Victor C. M. Leung,et al.  Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Industrial Informatics.

[10]  Zhiyuan Liu,et al.  Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.

[11]  Manuel Díaz,et al.  On blockchain and its integration with IoT. Challenges and opportunities , 2018, Future Gener. Comput. Syst..

[12]  Dong In Kim,et al.  Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory , 2018, IEEE Transactions on Vehicular Technology.

[13]  Yonggang Wen,et al.  A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks , 2018, IEEE Access.

[14]  Dusit Niyato,et al.  Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks , 2018, IEEE Transactions on Parallel and Distributed Systems.

[15]  Peng Jiang,et al.  A Survey on the Security of Blockchain Systems , 2017, Future Gener. Comput. Syst..

[16]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[17]  Bitcoin Proof of Stake: A Peer-to-Peer Electronic Cash System , 2020 .

[18]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[19]  S. Nakamoto,et al.  Bitcoin: A Peer-to-Peer Electronic Cash System , 2008 .