Resource Management in UAV Enabled MEC Networks

Muhammad Abrar, Ziyad M. Almohaimeed*, Ushna Ajmal, Rizwan Akram, Rooha Masroor, Muhammad Majid Hussain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

Original languageEnglish
Pages (from-to)4847-4860
Number of pages14
JournalComputers, Materials and Continua
Issue number3
Early online date28 Dec 2022
Publication statusPublished - 2023


  • ground mobile users
  • internet of things
  • Mobile edge computing
  • UAVs

ASJC Scopus subject areas

  • Biomaterials
  • Modelling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


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