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Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm.

Tawfiq Hasanin1, Aisha Alsobhi1, Adil Khadidos2

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

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Summary
This summary is machine-generated.

This study enhances mobile edge computing (MEC) for 5G IoT applications by using the Bald Eagle Search (BES) algorithm to reduce offloading latency and energy consumption for multiusers. The optimized BES algorithm improves task execution efficiency and network performance.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Telecommunications

Background:

  • Mobile Edge Computing (MEC) reduces latency and energy use by offloading intensive computations from mobile users to nearby edge clouds.
  • Deep learning in edge computing can lead to high computational complexity and prolonged execution times.
  • Existing MEC systems face challenges with increased device demand and data traffic, causing delays.

Purpose of the Study:

  • To reduce offloading latency and energy consumption in 5G Internet of Things (IoT) applications using the Bald Eagle Search (BES) optimization algorithm.
  • To enhance the BES algorithm by incorporating Resource Estimation (ROS) for improved task offloading decisions.
  • To minimize end-to-end execution time for multiusers in edge computing environments.

Main Methods:

  • The study proposes an enhanced Bald Eagle Search (BES) optimization algorithm tailored for multi-user offloading in MEC.
  • A Resource Estimation (ROS) stage is introduced into the BES algorithm to optimize resource selection.
  • The algorithm mimics the hunting strategy of bald eagles, involving select, search, and swooping stages for efficient task offloading.

Main Results:

  • The enhanced BES algorithm effectively minimizes offloading latency and energy consumption for mobile edge computing.
  • The integration of ROS estimation leads to the selection of optimal resources, reducing task execution time.
  • Simulations demonstrate the efficiency and stability of the proposed algorithm in reducing end-to-end latency compared to conventional methods.

Conclusions:

  • The proposed BES algorithm with ROS estimation offers a significant improvement in reducing offloading latency and energy consumption in 5G MEC systems.
  • This approach provides faster and near-optimal task execution for IoT devices, enhancing overall network performance.
  • The method proves superior to traditional techniques for minimizing latency in edge computing environments.