Vehicular Edge Offloading based on Anticipated Value of Computational Tasks
Takamasa Higuchi, Seyhan Uçar, Chang‐Heng Wang, Onur Altintas · 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) · 2021
Vehicular edge computing is enabling a variety of new services that better assist safety and comfort of driving. However, vehicles cannot offload an unlimited amount of computational tasks and input sensor data to a remote edge server because of the limitations in network bandwidth. In this paper, we design a learning-based task of floading mechanism that selects a small subset of input sensor data, which are expected to improve the application performance if processed by a rich and resource-intensive algorithm, hosted by the edge server. As a case study, we apply this framework to a vision-based object tracking application. The simulation results show that the proposed solution significantly improves object tracking accuracy with the same amount of resource consumption.