Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods
Abstract With the rapid development of communication technologies, the quality of our daily life has been improved with the applications of smart communications and networking, such as intelligent transportation and mobile service computing. However, high user demands for quality of service (QoS) ar...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
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| Series: | IET Communications |
| Online Access: | https://doi.org/10.1049/cmu2.12334 |
| _version_ | 1828240871360823296 |
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| author | Ting Wang Xiong Luo Wenbing Zhao |
| author_facet | Ting Wang Xiong Luo Wenbing Zhao |
| author_sort | Ting Wang |
| collection | DOAJ |
| description | Abstract With the rapid development of communication technologies, the quality of our daily life has been improved with the applications of smart communications and networking, such as intelligent transportation and mobile service computing. However, high user demands for quality of service (QoS) are forcing intelligent transportation to continuously improve immediacy and reduce the tasks offloading delay for the internet of vehicles (IoV). To meet the low latency of vehicle tasks offloading, an offloading scheme combining mobile edge computing (MEC) and deep reinforcement learning (DRL), is proposed in this article. Firstly, a realistic map is simulated, while initializing the tasks queue and building a tasks offloading environment with multiple service nodes. Then, an algorithm that combines deep learning with reinforcement learning, that is, the deep Q‐learning network (DQN) algorithm, is developed to optimize the offloading scheme by reducing the offload latency. Finally, given that the complete information cannot be observed effectively in the environment, a long short‐term memory (LSTM) model is applied within the DQN to train its neural network to improve offloading efficiency. The simulation results show that the MEC‐based vehicle tasks offloading can effectively reduce the latency of vehicle offloading. |
| first_indexed | 2024-04-12T21:48:14Z |
| format | Article |
| id | doaj.art-03245701ffaa4709ab81acbf8ff1557a |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-12T21:48:14Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-03245701ffaa4709ab81acbf8ff1557a2022-12-22T03:15:33ZengWileyIET Communications1751-86281751-86362022-06-0116101230124010.1049/cmu2.12334Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methodsTing Wang0Xiong Luo1Wenbing Zhao2School of Computer and Communication Engineering University of Science and Technology Beijing Beijing ChinaSchool of Computer and Communication Engineering University of Science and Technology Beijing Beijing ChinaDepartment of Electrical Engineering and Computer Science Cleveland State University Cleveland Ohio USAAbstract With the rapid development of communication technologies, the quality of our daily life has been improved with the applications of smart communications and networking, such as intelligent transportation and mobile service computing. However, high user demands for quality of service (QoS) are forcing intelligent transportation to continuously improve immediacy and reduce the tasks offloading delay for the internet of vehicles (IoV). To meet the low latency of vehicle tasks offloading, an offloading scheme combining mobile edge computing (MEC) and deep reinforcement learning (DRL), is proposed in this article. Firstly, a realistic map is simulated, while initializing the tasks queue and building a tasks offloading environment with multiple service nodes. Then, an algorithm that combines deep learning with reinforcement learning, that is, the deep Q‐learning network (DQN) algorithm, is developed to optimize the offloading scheme by reducing the offload latency. Finally, given that the complete information cannot be observed effectively in the environment, a long short‐term memory (LSTM) model is applied within the DQN to train its neural network to improve offloading efficiency. The simulation results show that the MEC‐based vehicle tasks offloading can effectively reduce the latency of vehicle offloading.https://doi.org/10.1049/cmu2.12334 |
| spellingShingle | Ting Wang Xiong Luo Wenbing Zhao Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods IET Communications |
| title | Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods |
| title_full | Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods |
| title_fullStr | Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods |
| title_full_unstemmed | Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods |
| title_short | Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods |
| title_sort | improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods |
| url | https://doi.org/10.1049/cmu2.12334 |
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