JiShuWang/BPR

"BPR: Blockchain-Enabled Efficient and Secure Parking Reservation Framework with Block Size Dynamic Adjustment Method", has been published in IEEE Transactions on Intelligent Transportation Systems.

BPR：Blockchain-Enabled Efficient and Secure Parking Reservation Framework with Block Size Dynamic Adjustment Method

This repository contains the author's implementation in PyTorch of dynamic adjustment method of block size for the paper "BPR：Blockchain-Enabled Efficient and Secure Parking Reservation Framework with Block Size Dynamic Adjustment Method".

Address

https://ieeexplore.ieee.org/document/9961087

Cite

@ARTICLE{9961087,

author={Wang, Jishu and Zhu, Chao and Miao, Chen and Zhu, Rui and Zhang, Xuan and Tang, Yahui and Huang, Hexiang and Gao, Chen},

journal={IEEE Transactions on Intelligent Transportation Systems},

title={BPR: Blockchain-Enabled Efficient and Secure Parking Reservation Framework With Block Size Dynamic Adjustment Method},

year={2023},

volume={24},

number={3},

pages={3555-3570},

doi={10.1109/TITS.2022.3222960}}

Dependencies

• Python (>=3.6)
• numpy (>=1.20.3)
• pandas (>=1.2.4)
• matplotlib (>=3.4.2)
• seaborn (>=0.11.2)
• torch (>=1.7.1)
• scikit-learn (>=0.24.2)

Implementation

Here, we provide an implementation of the Dynamic Adjustment Method of Block Size in BPR. The repository is organized as follows:

• The Prediction of Transaction Send Rates

  • data/ contains transaction send rates dataset and block performance dataset;

  • model/dataset.py contains procedures for data preprocessing for transaction send rates dataset ;

  • model/RNN.py contains the implementation of LSTM;

  • model/train.py puts all of the above together and may be used to execute a full training run on transaction send rates dataset.

• The Blockchain Performance Scoring Model

  • model/Blockchain Performance Model.ipynb contains the implementation for Blockchain Performance Model.