Joint Secure Offloading and Resource Allocation for Vehicular Edge Computing Network: A Multi-Agent Deep Reinforcement Learning Approach
Xidian University · KTH Royal Institute of Technology · +3 more institutions
Abstract
The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share…
Citation impact
- FWCI
- 36.23
- Percentile
- 100%
- References
- 45
Authors
9Topics & keywords
- Reinforcement learning
- Computer science
- Joint (building)
- Resource allocation
- Distributed computing
- Artificial intelligence
- Edge computing
- Enhanced Data Rates for GSM Evolution
- Decent work and economic growth
Funding
- NNNational Natural Science Foundation of ChinaAwards: 62001357, 62132013, 62102301
- JSJapan Society for the Promotion of ScienceAwards: JP20H04174, JP22K11989, JP19K20250
- NKNational Key Research and Development Program of ChinaAward: 2020YFB1807500
- BABasic and Applied Basic Research Foundation of Guangdong ProvinceAward: 2020A1515110772