Energy-Efficient Resource Allocation in UAV Based MEC System for IoT Devices

Du, Yao, Wang, Kezhi, Yang, Kun and Zhang, Guopeng (2018) Energy-Efficient Resource Allocation in UAV Based MEC System for IoT Devices. In: Globecom 2018 - IEEE Global Communications Conference, 9th - 13th December 2018, Abu Dhabi, UAE.

[img]
Preview
Text (Full text)
Du et al - Energy-Efficient Resource Allocation in UAV Based MEC System for IoT Devices.pdf - Accepted Version

Download (165kB) | Preview
Official URL: https://ieeexplore.ieee.org/document/8647789

Abstract

This paper considers an unmanned aerial vehicle based mobile edge computing (UAV based MEC) system, where we assume there is one UAV, acts as an edge cloud, providing data processing services to the Internet of things devices (IoTDs). We consider the UAV hovers at difference places for different time to receive and process data for IoTDs. We aim to minimize the energy consumption of the UAV, including its hovering energy and computation energy, by optimizing the hovering time, scheduling and resource allocation of the tasks received from IoTDs, subject to the quality of service (QoS) requirement of all the IoTDs and the computing resource available at UAV. This is formulated as a mixed-integer non-convex optimization problem, which is difficult to solve in general. We propose an efficient iterative algorithm to get a high-quality suboptimal solution. Simulation results show that our proposed method has a very good performance compared with the other benchmarks.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Internet of Things, Mobile edge computing, Unmanned aerial vehicle, Resource allocation
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 20 Sep 2018 16:26
Last Modified: 01 Aug 2021 12:01
URI: http://nrl.northumbria.ac.uk/id/eprint/35839

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics