Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-21-2021
Abstract
In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs. The DEVIPSK (differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means) is proposed to solve the model. In DEVIPSK, the population is initialized by K-Means to obtain better initial positions of UAVs. Besides, considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm, a mutation strategy pool is used to update the positions of UAVs. The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system.
Recommended Citation
Xu, B., Zhang, L., Xu, Z., Liu, Y., Chai, J., Qin, S., & Sun, Y. (2021). Energy Optimization in Multi-UAV-Assisted Edge Data Collection System. Computers, Materials & Continua, 69(2), 1671–1686. https://doi.org/10.32604/cmc.2021.018395
Journal
Computers, Materials & Continua
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
This article was originally published Open Access in Computers, Materials & Continua. The full-text article from the publisher can be found here.