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Related Experiment Video

Updated: Jun 3, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Hybrid Prairie Dog and Dwarf Mongoose optimization algorithm-based application placement and resource scheduling

R Baskar1, E Mohanraj2, M Saradha3

  • 1Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, Namakkal, 637 215, Tamil Nadu, India. rbaskar@ksrct.ac.in.

Scientific Reports
|January 8, 2025
PubMed
Summary

A new Hybrid Prairie Dog and Dwarf Mongoose Optimisation Algorithm-based Resource Scheduling (HPDDMOARS) technique optimizes Internet of Things (IoT) application placement in fog computing. This method significantly reduces energy consumption, makespan, and cost for delay-sensitive IoT applications.

Keywords:
Dwarf Mongoose optimization algorithm (DMOA)Fog computingInternet of things (IoT)Prairie Dog optimization algorithm (PDOA)Resource scheduling

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Artificial Intelligence

Background:

  • Fog computing is crucial for delay-sensitive Internet of Things (IoT) applications.
  • Resource-constrained fog devices necessitate efficient resource management for IoT application deployment.
  • Optimizing Quality of Service (QoS) for diverse IoT applications in fog environments is a complex, NP-complete problem.

Purpose of the Study:

  • To develop an efficient resource management strategy for fog computing environments.
  • To address the challenge of deploying diverse IoT applications while meeting QoS requirements.
  • To optimize energy consumption, cost, and makespan for IoT application placement.

Main Methods:

  • The study introduces the Hybrid Prairie Dog and Dwarf Mongoose Optimisation Algorithm-based Resource Scheduling (HPDDMOARS) technique.
  • HPDDMOARS is a weighted multi-objective mechanism for IoT application placement, optimizing energy, cost, and makespan.
  • It combines the exploration capabilities of the Prairie Dog Optimization Algorithm (PDOA) with the exploitation strengths of the Dwarf Mongoose Optimization Algorithm (DMOA).

Main Results:

  • Experimental validation demonstrated significant improvements compared to baseline approaches.
  • Achieved a 22.18% reduction in energy consumption.
  • Reduced makespan by 24.98% and lowered cost by 18.64%.

Conclusions:

  • The proposed HPDDMOARS technique effectively addresses the challenges of IoT application placement in fog computing.
  • It balances exploration and exploitation phases to achieve optimal resource allocation.
  • The method successfully meets QoS criteria while minimizing energy, cost, and makespan.