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author:

Guo, Z. (Guo, Z..) [1] | Liu, Q. (Liu, Q..) [2] | Gao, Z. (Gao, Z..) [3] | Liu, L. (Liu, L..) [4] | Gong, Y. (Gong, Y..) [5] | Liu, X. (Liu, X..) [6] (Scholars:刘西蒙) | Kumar, N. (Kumar, N..) [7] | Guizani, M. (Guizani, M..) [8]

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Scopus

Abstract:

As an emerging distributed ledger technology, blockchain provides multi-party trust between unreliable devices to share data and resources cooperatively, which facilitates the Internet of Vehicles (IoV) applications. In the typical blockchain enabled IoV (BIoV) scenarios, based on the data of common interest collected by various sensors, the IoV devices jointly maintain the world state in the form of address-balance pairs as the proof for transaction issuing and validation, where Ethereum like blockchain is used as the finite-state machine driven by the transactions. However, with the extension of the IoV network, an enormous number of accounts leads to the explosive growth of the state data, which has been the main challenge for BIoV with resource-limited devices. This paper proposes a modular-based adaptive bit-width compression (ABC) scheme to reduce the state data storage on each device by representing the address as a remainder with a shorter bit-width. Besides, a new transaction validation method is designed with the support of the XOR filter, which guarantees that the core functions of the blockchain can still be performed normally with the proposed scheme applied. Theoretical analysis and simulation results show that the compression ratio for the address data could be more than 80%, which dramatically improves the scalability of BIoV system. In addition, the extra privacy-preserving property is introduced with the compression scheme because the account information is unrecoverable from the remainders. IEEE

Keyword:

Address compression blockchain-enabled Internet of Vehicles (BIoV) Blockchains Data structures Distributed ledger modular arithmetic privacy-preserving Proposals Real-time systems Scalability Smart contracts

Community:

  • [ 1 ] [Guo Z.]School of Microelectronics, Tianjin University, Tianjin, China
  • [ 2 ] [Liu Q.]School of Microelectronics, Tianjin University, Tianjin, China
  • [ 3 ] [Gao Z.]School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  • [ 4 ] [Liu L.]Guangzhou Institute of Technology, Xidian University, Guangzhou, China
  • [ 5 ] [Gong Y.]Department of Information and Telecommunication Engineering, Beijing Information Science and Technology University, Beijing, China
  • [ 6 ] [Liu X.]College of Computer and Data Science, Fuzhou University, Fuzhou, China
  • [ 7 ] [Kumar N.]CSED, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
  • [ 8 ] [Guizani M.]Department of Machine Learning, Mo hamed Bin Zayed University of Artificial Intelligence, Masdar, United Arab Emirates

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Source :

IEEE Transactions on Vehicular Technology

ISSN: 0018-9545

Year: 2024

Issue: 10

Volume: 73

Page: 1-16

6 . 1 0 0

JCR@2023

Cited Count:

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SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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