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Fuzzy inference rule based task offloading model (FI-RBTOM) for edge computing.

Kashif Ibrahim1, Ahthasham Sajid2, Ihsan Ullah1

  • 1Department of Computer Science and Information Technology, University of Balochistan, Quetta, Balochistan, Pakistan.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This research introduces an efficient rule-based task-offloading model (FI-RBTOM) to address challenges in edge computing, optimizing task placement for reduced delays and improved services.

Keywords:
Cloud computingEdge computingFI-RBTOMFuzzy logicLocal

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Edge computing aims to minimize latency and enhance service quality.
  • Challenges include high user mobility, dynamic IoT environments, and limited device resources.
  • Task offloading is a critical issue in edge computing.

Purpose of the Study:

  • To propose an efficient rule-based task-offloading model (FI-RBTOM).
  • To optimize task placement decisions (edge, cloud, or local processing).
  • To address resource constraints and dynamic environments in edge computing.

Main Methods:

  • Developed an efficient rule-based task-offloading model (FI-RBTOM).
  • Input parameters include bandwidth, CPU utilization, task length, and task size.
  • Simulations conducted using MATLAB with fuzzy logic.

Main Results:

  • The FI-RBTOM model demonstrated effective task offloading decisions.
  • Achieved an overall error rate of 0.39875 using MATLAB simulations.
  • Model trained with 75% data and tested with 25% data.

Conclusions:

  • The proposed FI-RBTOM model offers an efficient solution for task offloading in edge computing.
  • The model effectively manages task placement considering various environmental and resource parameters.
  • Results indicate the model's potential to improve edge computing service quality and reduce delays.