2023
DOI: 10.1109/jiot.2023.3257291
|View full text |Cite
|
Sign up to set email alerts
|

Joint UAV Trajectory Planning, DAG Task Scheduling, and Service Function Deployment Based on DRL in UAV-Empowered Edge Computing

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
4
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
8

Relationship

2
6

Authors

Journals

citations
Cited by 20 publications
(4 citation statements)
references
References 33 publications
0
4
0
Order By: Relevance
“…To reduce the training time, the simulation scene in this paper is set at 50 m × 50 m × 10 m, due to the complexity of the algorithm's action and state space [31][32][33][34]. The algorithm presented in this paper applies to scenes of arbitrary size, and the simulation parameters are shown in Table 1.…”
Section: Parameter Initializationmentioning
confidence: 99%
“…To reduce the training time, the simulation scene in this paper is set at 50 m × 50 m × 10 m, due to the complexity of the algorithm's action and state space [31][32][33][34]. The algorithm presented in this paper applies to scenes of arbitrary size, and the simulation parameters are shown in Table 1.…”
Section: Parameter Initializationmentioning
confidence: 99%
“…In [12,13], radio power transmission from UAVs to end devices was proposed to improve battery life or to enable local computing. In [14], a deep reinforcement learning-based algorithm was proposed to solve the UAV trajectory planning, mission scheduling, and deployment in complex regional scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…When performing task offloading, there are different types of tasks, and edge servers with limited resources that can only deploy a subset of all the SFs [10,11]. For the SF deployment problem, Li et al [12] designed a new genetic-algorithm-based SF deployment algorithm, and designed a new edge-computing-service-deployment management platform.…”
Section: Related Workmentioning
confidence: 99%
“…It can be seen that this paper is the first to combine DAG tasks with SF and the UAV position, and optimize the task scheduling plan jointly. Task Scheduling [5,6] Minimization of energy consumption [7,8] Minimization of energy consumption and service latency [9] Minimization of energy consumption [11] Minimization of response time [12] Minimization of response time [13] Task completion time [14] Minimization of energy consumption…”
Section: Related Workmentioning
confidence: 99%