Research on the deployment scheme of edge servers for vehicle task offloading

Mobile edge computing can expand the limited computing power of on-board equipment, which is an effective means to assist vehicles to realize complex applications. However, the task offloading process brings a certain delay. How to achieve faster offloading to reduce the delay of vehicle tasks is crucial for delay-sensitive vehicle tasks. The deployment of edge servers is the basis of vehicle computing task offloading, and an efficient deployment method of edge servers can effectively meet the low latency of mobile vehicle access. Therefore, in order to minimize the average access delay between AP points and edge servers, this paper establishes a deployment model of edge servers, and proposes a mobile edge server deployment method based on the K-means algorithm and the genetic algorithm, called KGA, which fully considers the position relationship among vehicles, APs and edge servers and the limitation of wireless transmission bandwidth to determine the deployment location of edge servers and optimize communication delay of all vehicle tasks. Finally, a large number of simulation experiments under different scenarios were completed. The experimental results show that the proposed KGA method can effectively reduce the delay under the premise of the edge server capacity limitation, and its effect is better than several existing representative algorithms.