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Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing
| Content Provider | MDPI |
|---|---|
| Author | Xu, Shilin Guo, Caili |
| Copyright Year | 2020 |
| Description | To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives. |
| Starting Page | 6820 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20236820 |
| Journal | Sensors |
| Issue Number | 23 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-29 |
| Access Restriction | Open |
| Subject Keyword | Sensors Transportation Science and Technology Vehicular Cloud Computing Remote Cloud Computing Long Short Term Memory Network Deep Reinforcement Learning Computation Offloading Vehicular Network |
| Content Type | Text |
| Resource Type | Article |
