Open Access

ARTICLE

Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

by Bin Xu^1,2,3, Lu Zhang¹, Zipeng Xu¹, Yichuan Liu¹, Jinming Chai¹, Sichong Qin⁴, Yanfei Sun^1,*

1 Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
2 Nanjing Pharmaceutical Co., Ltd., Nanjing, 210012, China
3 Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China
4 Central Washington University, Ellensburg, 98926, United States

* Corresponding Author: Yanfei Sun. Email:

Computers, Materials & Continua 2021, 69(2), 1671-1686. https://doi.org/10.32604/cmc.2021.018395

Received 07 March 2021; Accepted 17 April 2021; Issue published 21 July 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.

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Keywords

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