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

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.