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Related Concept Videos

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The terms 'conserved quantity' and 'conservation law' have specific scientific meanings in physics, which differ from the meanings associated with their everyday use. For example, in everyday usage, water could be conserved by not using it, by using less of it, or by re-using it. However, in scientific terms, a conserved quantity of a system stays constant, changes by a definite amount that is transferred to other systems, and is converted into other forms of that...
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Related Experiment Video

Updated: Apr 22, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Towards Green IoT: Energy Conservation in Core IoT Network Using Network Coding and Soft Actor Critic.

B E Narasimhayya1, Bindhu Madhavi Prathipati2, Azadeh Amoozegar3

  • 1The Oxford College of Engineering, VTU; narasimha.jain@rediffmail.com.

Journal of Visualized Experiments : Jove
|April 20, 2026
PubMed
Summary

This study introduces a hybrid network coding (NC) and soft actor-critic (SAC) reinforcement learning model to significantly reduce energy consumption in Internet of Things (IoT) networks. The NC+SAC approach enhances routing efficiency, leading to a 40% decrease in energy usage and a longer network lifespan.

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Last Updated: Apr 22, 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:

  • Internet of Things (IoT) networks face escalating energy efficiency challenges due to high energy demands of data routing in core networks.
  • Traditional routing methods struggle to balance performance and energy preservation in resource-constrained IoT environments.

Purpose of the Study:

  • To develop and evaluate a novel hybrid model integrating network coding (NC) with soft actor-critic (SAC) reinforcement learning for energy-efficient IoT routing.
  • To address the critical need for sustainable and high-performance communication infrastructures in the expanding IoT landscape.

Main Methods:

  • A hybrid model combining network coding (NC) for reduced transmissions and soft actor-critic (SAC) reinforcement learning for dynamic, energy-aware path selection was proposed.
  • The system was simulated using NS-3 with a 5x5 grid topology of IoT nodes, evaluating performance under various traffic conditions.

Main Results:

  • The NC + SAC hybrid model demonstrated a 40% reduction in energy consumption compared to traditional and standalone methods.
  • Achieved a 97% packet delivery ratio, a 50% improvement in throughput, and significantly extended network lifetime.
  • Validated through comprehensive NS-3 simulations across multiple performance metrics.

Conclusions:

  • The integrated NC-SAC approach offers a scalable framework for next-generation green IoT networks by effectively balancing reliability, energy preservation, and performance.
  • This learning-driven coded communication strategy is crucial for developing sustainable IoT infrastructures.
  • The hybrid model provides a robust solution to the energy efficiency challenges inherent in core IoT networks.