Edge Computing-Based Task Offloading Algorithm for 5th Gen Internet of Vehicles Communication

Abstract

The new in-vehicle tasks that are emerging are requiring stronger communication and processing capabilities due to the Internet of Vehicles' rapid expansion. Highway car users may now get more dependable and fast services thanks to the widespread installation of 5G millimeter-wave base stations. Simultaneously, mobile edge computing (MEC) technology reduces transmission latency by deploying MEC servers surrounding user terminals that have computation and storage capacity to provide computer services for onboard operations. To address the allocation challenge, the combined optimization problem of computation and communication resources is modeled as a 0-1 mixed-integer linear programming problem. Initially, the primary optimization issue is divided into two smaller issues: allocating resource blocks and making decisions on unloading. Swarm algorithms are used to solve each of the subproblems independently. Finally, using the heuristic approach, the subproblems are solved iteratively to get the best resource block allocation plan and unloading decision vector. The outcomes of the simulation demonstrate that the suggested algorithm is capable of fulfilling the demands of every onboard task. Simultaneously, the system's average latency is reduced.

Keywords: Mobile Edge Computing, Task Offloading Algorithm, 5 Generation Network, Internet of Vehicles, Linear Programming