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Related Experiment Video

Updated: Jul 9, 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

Multi-objective optimization approach for energy efficient clustering and routing in wireless sensor networks.

E Madhankumar1, K Selvaraj2, Dae-Ki Kang3

  • 1Department of Electronics and Communication Engineering, St. Peter's College of Engineering and Technology, Chennai, India.

Scientific Reports
|July 7, 2026
PubMed
Summary

This study introduces Multi-Objective Di-Strategy GrayLag Goose Optimization (MO-DSGGO) to enhance energy efficiency and reduce delay in Wireless Sensor Networks (WSNs). The novel approach optimizes clustering and routing, outperforming existing methods for reliable network performance.

Keywords:
Cluster HeadDelayEnergy efficientMulti-Objective Di-Strategy GrayLag Goose OptimizationWireless Sensor Network

Related Experiment Videos

Last Updated: Jul 9, 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:

  • Wireless Sensor Networks (WSN)
  • Optimization Algorithms
  • Network Performance

Background:

  • Wireless Sensor Networks (WSNs) are crucial for applications like smart cities and environmental monitoring, but face challenges in balancing energy efficiency and communication delay.
  • Energy constraints in WSNs necessitate sensors entering low-power states, leading to communication delays and potential network congestion.
  • Effective energy management and optimal multi-hop routing are vital for minimizing delays and ensuring efficient WSN operation.

Purpose of the Study:

  • To propose a novel optimization algorithm, Multi-Objective Di-Strategy GrayLag Goose Optimization (MO-DSGGO), for enhancing energy efficiency and reducing delay in WSNs.
  • To optimize clustering and routing strategies within WSNs using MO-DSGGO.
  • To evaluate the performance of MO-DSGGO against existing methods in terms of delay, energy consumption, throughput, and node lifetime.

Main Methods:

  • Implementation of MO-DSGGO utilizing logistic mapping and symmetric adaptive division population for enhanced exploration and diversity in the solution space.
  • Definition of multi-objectives including distance to Cluster Head (CH) and Base Station (BS), intra-cluster distance, node degree, average delay, and residual energy as fitness functions.
  • Selection of CH and optimal multi-hop routing paths based on the defined multi-objective fitness functions.

Main Results:

  • MO-DSGGO achieved a delay of 0.176 ms and energy consumption of 7.2 J for 100 nodes, and 95% throughput for 250 nodes.
  • The proposed method demonstrated superior performance compared to EOR-iABC and EOAMRCL in terms of delay and energy consumption.
  • MO-DSGGO exhibited faster and more stable convergence, achieving a node lifetime of 97.272% and a Packet Delivery Ratio (PDR) of 95.38%.

Conclusions:

  • MO-DSGGO effectively optimizes energy efficiency and minimizes delay in WSNs through advanced clustering and routing techniques.
  • The algorithm shows significant improvements over existing methods, offering a more reliable and efficient solution for WSN management.
  • MO-DSGGO provides a robust framework for improving WSN performance metrics, including node lifetime and throughput.