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.
Highlights
The Mobile Edge Computing (MEC) Model [1] was proposed to solve such problems as delay, high pressure on network bandwidth, high energy consumption, and insufficient caching capacity [2] caused by centralized data processing in cloud computing mode
A differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means [12] is proposed (DEVIPSK)
The main contributions of this paper are as follows: (1) A differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means is proposed to optimize the deployment of UAVs
Summary
The Mobile Edge Computing (MEC) Model [1] was proposed to solve such problems as delay, high pressure on network bandwidth, high energy consumption, and insufficient caching capacity [2] caused by centralized data processing in cloud computing mode. Yang et al [6] designed a multi-UAV deployment scheme for MEC enhanced IoT architecture, which provides computing offloading services for ground IoT devices with limited local processing capacity They proposed a multi-UAV deployment mechanism based on a differential evolution algorithm to balance the load of UAVs. Mozaffari et al [7] considered a UAV-enabled MEC system and established a game model to achieve lower energy consumption. A differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means [12] is proposed (DEVIPSK). (1) A differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means is proposed to optimize the deployment of UAVs.
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