Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks
Guangdong University of Technology · Hunan Normal University · +2 more institutions
Abstract
Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle…
Citation impact
- FWCI
- 51.66
- Percentile
- 100%
- References
- 38
Authors
4Topics & keywords
- Reinforcement learning
- Computer science
- Edge computing
- Resource allocation
- Mobile edge computing
- Resource management (computing)
- Computer network
- Distributed computing