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Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization.

Mohamed H Mousa1,2, Mohamed K Hussein2

  • 1Department of Information Technology, College of Computer Science at AlKamil, University of Jeddah, Jeddah, Saudi Arabia.

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Summary
This summary is machine-generated.

This study proposes a UAV-based task offloading strategy for Internet of Things (IoT) devices in areas with no network. It dynamically clusters IoT devices and uses optimized UAV paths to reduce task delays and conserve UAV energy.

Keywords:
Ant colony optimizationComputation offloadingDifferential evolutionInternet of thingsMobile edge computingParticle swarm optimization

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Internet of Things (IoT) devices require efficient task offloading for improved performance.
  • Edge computing offers solutions but faces deployment challenges in remote or disaster-stricken areas.
  • Unmanned Aerial Vehicles (UAVs) present a viable mobile edge computing platform for challenging environments.

Purpose of the Study:

  • To develop a UAV-based task offloading strategy for IoT devices in disconnected or hostile environments.
  • To address the constraints of limited UAV energy and delay-sensitive IoT tasks.
  • To optimize the dynamic clustering of IoT devices and the UAV's task processing path.

Main Methods:

  • Dynamic clustering of IoT devices based on UAV energy and task delay constraints.
  • Modeling the cluster optimization problem as a mixed-integer, nonlinear constraint optimization.
  • Employing a discrete differential evolution (DDE) algorithm for cluster optimization.
  • Utilizing the ant colony optimization (ACO) algorithm for UAV path planning over cluster heads.

Main Results:

  • The proposed strategy effectively reduces task delays for IoT devices.
  • The strategy demonstrates significant improvements in UAV energy consumption.
  • Simulation results validate the efficiency of the DDE and ACO algorithms in the proposed framework.

Conclusions:

  • UAV-based mobile edge computing is a promising solution for IoT task offloading in challenging terrains.
  • Dynamic clustering and optimized path planning are crucial for efficient UAV-assisted IoT task processing.
  • The proposed DDE-based offloading strategy offers a robust approach to balancing task delays and energy constraints.