Mobile Edge Computing (MEC)-Enabled UAV Placement and Computation Efficiency Maximization in Disaster Scenario

Zaiba Shah, Umer Javed, Muhammad Naeem, Sherali Zeadally, Waleed Ejaz

Research output: Contribution to journalArticlepeer-review


Unmanned aerial vehicles (UAVs) are considered for disaster management to provide better coverage or help first responders provide advanced services. Furthermore, UAVs combined with mobile edge computing (MEC) capabilities, have recently emerged as a support for cloud computing by deploying UAV-assisted MEC at the network edge to reduce the latency and load of cloud computing data centers. We investigate the problem of maximizing the utility function by jointly optimizing the computing power, user associations, performance, duration, and location of user equipments (UEs) in UAV-assisted MEC networks in a disaster scenario. We considered several constraints including delay, quality of service, and coverage. The optimization problem is a mixed-integer non-linear programming problem. We propose a multi-stage offloading algorithm based on a learning algorithm and an interior-point method to obtain a viable solution. The simulation results obtained demonstrate the effectiveness of our proposed algorithm compared to existing schemes.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Vehicular Technology
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:


  • Autonomous aerial vehicles
  • Computational capacity
  • disaster management
  • Minimization
  • mobile edge computing
  • Multi-access edge computing
  • offloading
  • Quality of service
  • Resource management
  • Task analysis
  • Trajectory
  • unmanned aerial vehicles

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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