Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing

Abstract

This paper establishes a new multi-unmanned aerial vehicle (multi-UAV) enabled mobile edge computing (MEC) system, where a number of UAVs are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This paper presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing the system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the whole population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

Packet Error Probability and Effective Throughput for Ultra-Reliable and Low-Latency UAV Communications

“…Alzenad et al [8] proposed an optimal placement algorithm for maximizing the number of covered users using the minimum transmit power. Furthermore, UAV served as mobile edge computing (MEC) have been studied in [9], [10]. In [9], a secure UAV system has been studied where multiple ground users offload the tasks to the UAV-enabled MEC system in the presence of several eavesdropping UAVs.…”

“…In [9], a secure UAV system has been studied where multiple ground users offload the tasks to the UAV-enabled MEC system in the presence of several eavesdropping UAVs. In [10], large-scale mobile users was considered in the multi-UAV enabled MEC, where offloading decision and resource allocation were studied.…”

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

“…• Differential evolution (DE) was developed by Storn and Price in 1997 [66]. Due to the simple structure, ease of implementation, and robustness, it has been successfully applied to complex optimization problems, such as resource allocation [67] and job scheduling [68] in CC and EC. In DE, the first step is to initialize a population randomly.…”

Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system

“…For example, Yang et al (2019) optimized the power consumption of a multi-UAV-assisted MEC system by considering the joint device association, power control, computing capacity allocation, and location planning. Wang Y et al (2020) designed a two-layer optimization algorithm for joint UAV deployment and task scheduling in a multi-UAV-assisted MEC system. Chen WH et al (2019) investigated the quality of service in a multi-UAV-assisted MEC system.…”