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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Dynamic fog node placement optimization using adaptive dynamic pufferfish optimization for real-time IoT networks.

Ashraf A Abu-Ein1,2, Obaida M Al-Hazaimeh1,3, Mohammed Tawfik4

  • 1Department of Computer Networks and Cybersecurity, Faculty of Information Technology, Jadara University, Irbid, Jordan.

Scientific Reports
|March 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the Dynamic Pufferfish Optimization Algorithm (D-POA) for adaptive fog node placement in dynamic environments. D-POA enhances connectivity and coverage while significantly reducing movement costs for improved IoT service quality.

Keywords:
Adaptive algorithmsDynamic optimizationFog computingNetwork adaptationPufferfish optimization algorithmReal-time reconfiguration

Related Experiment Videos

Last Updated: Mar 12, 2026

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

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

Published on: September 8, 2023

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • The proliferation of Internet of Things (IoT) devices necessitates efficient fog computing architectures.
  • Dynamic environments with mobile nodes, failures, and traffic fluctuations pose challenges for optimal fog node placement.
  • Maintaining low-latency processing and service quality requires adaptive strategies for fog node deployment.

Purpose of the Study:

  • To introduce a novel bio-inspired algorithm for real-time, adaptive fog node repositioning.
  • To address the multi-objective optimization problem of fog node placement considering connectivity, coverage, and movement costs.
  • To minimize service disruption in dynamic fog computing environments.

Main Methods:

  • Development of the Dynamic Pufferfish Optimization Algorithm (D-POA), inspired by pufferfish behavior.
  • Formulation of the fog node placement problem as a continuous multi-objective optimization task.
  • Experimental evaluation across diverse dynamic network scenarios.

Main Results:

  • D-POA achieved 97.8% network connectivity and 98.4% area coverage.
  • Movement costs were reduced by 38-57% compared to baseline algorithms.
  • Near-linear scalability was demonstrated, maintaining over 96% solution quality for networks up to 1000 nodes.

Conclusions:

  • The Dynamic Pufferfish Optimization Algorithm (D-POA) offers an effective solution for adaptive fog node placement in dynamic IoT environments.
  • D-POA successfully balances exploration and exploitation for optimal real-time reconfiguration.
  • The algorithm demonstrates significant improvements in connectivity, coverage, and cost-efficiency, outperforming existing methods.