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

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

Wang, Jishu / Zhu, Chao / Miao, Chen / Zhu, Rui / Zhang, Xuan / Tang, Yahui / Huang, Hexiang / Gao, Chen

The parking lot is one of the important components of the intelligent transportation system (ITS). The current parking lots mainly use instant parking, which has low parking efficiency, during peak hours, which leads to traffic congestion. To guarantee the stable operation of parking lots, we propose a blockchain-enabled parking reservation framework, called BPR. Traditional parking reservation systems may exist the condition of malicious reservations, and resulting in wasted parking spaces. Therefore, we design a reputation mechanism to manage the parking reservation behavior of vehicles and reduce the number of malicious nodes. In addition, to balance the performance of the blockchain at different times (especially during peak hours), we use deep learning (DL) to dynamically adjust the block size to make the blockchain run more efficiently and stably. We deploy the system in Hyperledger Fabric and conduct effectiveness experiments. The comprehensive evaluation results and analysis show that the proposed reputation mechanism can effectively curb malicious nodes from reserving parking spaces and reduce the waste of parking resources. And the block size will be dynamically adjusted to balance the performance of the blockchain at different periods, this method is also applicable to other blockchain performance-sensitive scenes. Finally, this paper is compared with related work to demonstrate the innovation and feasibility of this work from various aspects.

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Document information

Title :

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

Contributors:

Wang, Jishu (author) / Zhu, Chao (author) / Miao, Chen (author) / Zhu, Rui (author) / Zhang, Xuan (author) / Tang, Yahui (author) / Huang, Hexiang (author) / Gao, Chen (author)

Published in:

IEEE Transactions on Intelligent Transportation Systems ; 24 , 3 ; 3555-3570

Publication date :

2023-03-01

Size :

3886721 byte

ISSN :

1524-9050 , 1558-0016

DOI :

https://doi.org/10.1109/TITS.2022.3222960

Type of media :

Article (Journal)