Optimization of User Service Rate with Image Compression in Edge Computing-Based Vehicular Networks

Liujing Zhang, Jin Li (), Wenyang Guan and Xiaoqin Lian
Additional contact information
Liujing Zhang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Jin Li: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Wenyang Guan: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Xiaoqin Lian: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Mathematics, 2024, vol. 12, issue 4, 1-19

Abstract: The prevalence of intelligent transportation systems in alleviating traffic congestion and reducing the number of traffic accidents has risen in recent years owing to the rapid advancement of information and communication technology (ICT). Nevertheless, the increase in Internet of Vehicles (IoV) users has led to massive data transmission, resulting in significant delays and network instability during vehicle operation due to limited bandwidth resources. This poses serious security risks to the traffic system and endangers the safety of IoV users. To alleviate the computational load on the core network and provide more timely, effective, and secure data services to proximate users, this paper proposes the deployment of edge servers utilizing edge computing technologies. The massive image data of users are processed using an image compression algorithm, revealing a positive correlation between the compression quality factor and the image’s spatial occupancy. A performance analysis model for the ADHOC MAC (ADHOC Medium Access Control) protocol is established, elucidating a positive correlation between the frame length and the number of service users, and a negative correlation between the service user rate and the compression quality factor. The optimal service user rate, within the constraints of compression that does not compromise detection accuracy, is determined by using the target detection result as a criterion for effective compression. The simulation results demonstrate that the proposed scheme satisfies the object detection accuracy requirements in the IoV context. It enables the number of successfully connected users to approach the total user count, and increases the service rate by up to 34%, thereby enhancing driving safety, stability, and efficiency.

Keywords: Internet of Vehicles; mobile edge computing; object detection; image compression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/4/558/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/4/558/ (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:jmathe:v:12:y:2024:i:4:p:558-:d:1338008

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

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