Joint Interdependent Task Scheduling and Energy Balancing for Multi-UAV-Enabled Aerial Edge Computing: A Multiobjective Optimization Approach

To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, bo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE internet of things journal Vol. 10; no. 23; pp. 20368 - 20382
Main Authors: Huang, Xumin, Peng, Chaoda, Wu, Yuan, Kang, Jiawen, Zhong, Weifeng, Kim, Dong In, Qi, Long
Format: Journal Article
Language:English
Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.12.2023
Subjects:
Balancing
Edge computing
Evolutionary algorithms
Multiple objective analysis
Optimization
Task scheduling
Unmanned aerial vehicles
ISSN:2327-4662, 2327-4662
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multiobjective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multiobjective optimization problem involving two objectives: 1) the makespan minimization of all tasks and 2) energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multiobjective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3288379