Peng, H.; Zhang, X.; Li, H.; Xu, L.; Wang, X. An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing. Electronics2023, 12, 2719.
Peng, H.; Zhang, X.; Li, H.; Xu, L.; Wang, X. An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing. Electronics 2023, 12, 2719.
Peng, H.; Zhang, X.; Li, H.; Xu, L.; Wang, X. An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing. Electronics2023, 12, 2719.
Peng, H.; Zhang, X.; Li, H.; Xu, L.; Wang, X. An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing. Electronics 2023, 12, 2719.
Abstract
A full connected world is expected to gain in the 6th generation mobile network (6G). As a typi-cal fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. In the future of vehicular net-works, wide variety of services need powerful computing resources and higher quality of ser-vice (QoS). Existing resources are insufficient to match these requirements. Aim to this problem, An intelligent service offloading framework is provided. Based on the framework, an Algorithm of Improved Gradient Descent (AIGD) is created to accelerate the speed of iteration. So, the con-vergence of convolutional neural network (CNN) based on AIGD is able to be accelerated too. Then, an Algorithm of convolutional long short-term memory (CN_LSTM) Based Traffic Predic-tion (ACLBTP) is designed to gain the predicted number of vehicles belonged to the edge node. At last, an Algorithm of Service Offloading Based on CN_LSTM (ASOBCL) is conducted to of-fload these services to the vehicles belonged to the edge node. In ASOBCL, sorting technique is adopted to speed up the offloading work. Simulation results demonstrate the fact that the pre-diction strategy designed in this paper has high accuracy. The low offloading time and main-taining stable load balance is gained via running ASOBCL. Low offloading time means short response time. And, the QoS is guaranteed. So, these strategies designed in this paper are effec-tive and valuable.
Keywords
6G; IoV; AI; edge computing; QoS; CNN; LSTM
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
