Multiaccess Edge Computing-Based Simulation as a Service for 5G Mobile Applications: A Case Study of Tollgate Selection for Autonomous Vehicles

As the data rate and area capacity are enormously increased with the advent of 5G wireless communication, the network latency becomes a severe issue in a 5G network. Since there are various types of terminals in a 5G network such as vehicles, medical devices, robots, drones, and various sensors which perform complex tasks interacting with other devices dynamically, there is a need to handle heavy computing resource intensive operations. Placing a multiaccess edge computing (MEC) server at the base station, which is located at the edge, can be one of the solutions to it. The application running on the MEC platform needs a specific simulation technique to analyze complex systems inside the MEC network. We proposed and implemented a simulation as a service (SIMaaS) for the MEC platform, which is to offload the simulation using a Cloud infrastructure based on the concept of computation offloading. In the case study, the Monte-Carlo simulations are conducted using the proposed SIMaaS to select the optimal highway tollgate where vehicles are allowed to enter. It shows how clients of the MEC platform use SIMaaS to obtain certain goals.