Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing
Hubei University of Technology · Singapore Institute of Technology · +3 more institutions
Abstract
Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the…
Citation impact
- FWCI
- 233.56
- Percentile
- 100%
- References
- 37
Authors
5Topics & keywords
- Computer science
- Reinforcement learning
- Mobile edge computing
- Markov decision process
- Computation offloading
- Distributed computing
- Edge computing
- Wireless
- Affordable and clean energy