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Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

Gabriel Ioan Arcas1, Tudor Cioara2, Ionut Anghel2

  • 1Bosch Engineering Center, 400158 Cluj-Napoca, Romania.

Biomimetics (Basel, Switzerland)
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a whale optimization algorithm to select optimal edge nodes for smart energy grids, improving data transmission and control service latency. The method efficiently manages computational tasks, enhancing decision-making speed and energy network security.

Keywords:
cloud–edge offloadingdirected acyclic graphenergy efficiencysmart gridwhale optimization algorithm

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Increasing IoT devices in smart grids generate large data volumes, challenging control service latency and secure energy delivery.
  • Edge computing offers a solution by offloading computation, but coordinating edge nodes is complex due to vast decision spaces.

Purpose of the Study:

  • To develop an optimized method for selecting edge nodes for computational task offloading in smart grids.
  • To address challenges in latency, data volume, and secure energy delivery within smart energy grids.

Main Methods:

  • Utilized the whale optimization algorithm (WOA) for optimal edge node selection.
  • Employed a directed acyclic graph (DAG) to model dependencies and navigate the decision space.
  • Developed a fitness function considering round-trip time and edge-task resource correlation for offloading decisions.
  • Adapted WOA with modified feedback mechanisms, inertia weight, and convergence factors to prevent suboptimal solutions.

Main Results:

  • The proposed solution effectively balances energy and data network constraints.
  • Demonstrated faster decision-making for optimization, with response times significantly improved.
  • Achieved a low average execution time of approximately 0.03 seconds per iteration.
  • Showcased strong performance in diversity, fitness evolution, and execution time on complex infrastructures.

Conclusions:

  • The whale optimization algorithm provides an effective approach for edge node selection in smart grids.
  • The method enhances optimization speed and resource management while considering network constraints.
  • Offers a robust solution for improving the efficiency and reliability of smart energy grids.