Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Yanli Liu, Qiang Qian, Heng Zhang (), Jingchao Li, Yikai Zhong and Neal N. Xiong
Additional contact information
Yanli Liu: School of Electronic Information, Shanghai Dianji University, Shanghai 201306, China
Qiang Qian: School of Electronic Information, Shanghai Dianji University, Shanghai 201306, China
Heng Zhang: School of Electronic Information, Shanghai Dianji University, Shanghai 201306, China
Jingchao Li: School of Electronic Information, Shanghai Dianji University, Shanghai 201306, China
Yikai Zhong: School of Electronic Information, Shanghai Dianji University, Shanghai 201306, China
Neal N. Xiong: Department of Mathematics and Computer Science, Sul Ross State University, Alpine, TX 79830, USA

Sustainability, 2023, vol. 16, issue 1, 1-26

Abstract: With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34 × 10 7 . In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.

Keywords: Internet of Vehicles; traffic sign detection; sustainability; blockchain technology; GGS-YOLO; model accuracy verification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/1/171/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/1/171/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2023:i:1:p:171-:d:1306294

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().