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Task-Offloading Optimization Using a Genetic Algorithm in Hybrid Fog Computing for the Internet of Drones.

Mohamed Amine Attalah1, Sofiane Zaidi2, Naçima Mellal3

  • 1Department of Electronics, University Center of Tipaza, Tipaza 42000, Algeria.

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Summary
This summary is machine-generated.

This study introduces GA Hybrid-Fog, a novel strategy for optimizing task offloading in the Internet of Drones (IoD). It significantly reduces delays by intelligently offloading tasks from Unmanned Aerial Vehicles (UAVs) to fog resources.

Keywords:
Internet of Dronesfog computing networksgenetic algorithm optimizationtask offloading in IoDunmanned aerial vehicles

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • The Internet of Drones (IoD) faces challenges in task offloading due to dynamic topologies and intermittent connectivity.
  • Stringent requirements for reduced delay in task offloading are critical for effective IoD operations.

Purpose of the Study:

  • To propose an optimized task-offloading strategy for fog-enabled IoD environments.
  • To address challenges of high dynamics and intermittent connections in IoD task offloading.

Main Methods:

  • A heuristic genetic algorithm (GA) is employed for task offloading optimization.
  • Hybrid fog computing technology is integrated, utilizing both fog base stations (FBSs) and fog UAVs (FUAVs).
  • The strategy, named GA Hybrid-Fog, optimizes offloading decisions from edge Unmanned Aerial Vehicles (UAVs).

Main Results:

  • GA Hybrid-Fog effectively optimizes transmission and fog computing delays.
  • The proposed method guarantees enhanced storage and processing capacity for offloaded tasks.
  • Experimental results demonstrate significant improvements in task-offloading delays compared to existing IoD technologies.

Conclusions:

  • GA Hybrid-Fog presents a superior approach for task offloading in dynamic IoD environments.
  • The integration of GA with hybrid fog computing effectively mitigates delay and enhances resource utilization.
  • This strategy offers a promising solution for improving the performance of Unmanned Aerial Vehicle (UAV) networks.