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Related Experiment Video

Updated: Jun 21, 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

Reinforcement learning enabled hybrid optimisation for energy-efficient multipath routing in wireless sensor

R Nareshkumar1, Prabu Selvam2, Karthikeyan Kaliyaperumal3

  • 1Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology Tiruchirappalli, Tiruchirappalli, 621105, India.

Scientific Reports
|June 19, 2026
PubMed
Summary

This study introduces a novel framework using Reinforcement Learning and hybrid optimization for energy-efficient wireless sensor networks. It enhances data delivery and network lifespan through adaptive scheduling and optimized routing.

Keywords:
Dynamic sleep schedulingEnergy efficientFault-tolerant communicationLoad balancingMultipath routingReinforcement learningThroughput

Related Experiment Videos

Last Updated: Jun 21, 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

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) face significant challenges in energy efficiency, scalability, and data delivery due to limited node energy and dynamic network topologies.
  • Existing routing protocols often exhibit high energy consumption, inefficient cluster head selection, and poor adaptability to network changes, reducing overall network lifespan and performance.

Purpose of the Study:

  • To propose a novel Reinforcement Learning-based Multipath Hybrid Whale-Grey Wolf Optimization framework to address the limitations of current WSN routing protocols.
  • To enhance energy efficiency, scalability, and data delivery in WSNs through adaptive node management and optimized routing strategies.

Main Methods:

  • Incorporation of Deep Reinforcement Learning (DRL) for adaptive sleep scheduling and node activation, considering residual energy, node centrality, and proximity to cluster heads.
  • Development of a hybrid optimization algorithm combining Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) for efficient cluster head selection.
  • Implementation of a multipath routing technique to establish stable and energy-efficient paths between cluster heads and the base station, prioritizing path stability, residual energy, and path quality.

Main Results:

  • The proposed framework demonstrates significant improvements in network performance.
  • Achieved a packet delivery ratio of 97.8%, indicating highly reliable data transmission.
  • Recorded a total energy consumption of 320 J and a base station throughput of 72 kbps, showcasing substantial energy efficiency and effective data handling.

Conclusions:

  • The Reinforcement Learning-based Multipath Hybrid Whale-Grey Wolf Optimization framework effectively overcomes the energy efficiency, scalability, and data delivery challenges in WSNs.
  • The adaptive scheduling, optimized cluster head selection, and multipath routing contribute to an extended network lifespan and improved overall performance.
  • The proposed model offers a robust and efficient solution for modern wireless sensor network applications.