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

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Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs.

Peng Jiang1, Yang Feng2, Feng Wu3

  • 1College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China. pjiang@hdu.edu.cn.

Sensors (Basel, Switzerland)
|September 3, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel routing algorithm for underwater wireless sensor networks (UWSNs) that optimizes energy consumption and extends network life by using Krill Herd optimization for dual-cluster selection.

Keywords:
Krill Herd optimizationUWSNsdynamic layeredrouting algorithm

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Last Updated: Mar 15, 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
  • Electrical Engineering
  • Network Engineering

Background:

  • Underwater wireless sensor networks (UWSNs) face challenges with limited node energy and high cluster head loads in traditional routing algorithms.
  • Existing clustering routing algorithms often struggle with energy efficiency and network longevity due to node energy constraints.

Purpose of the Study:

  • To propose a dynamic layered dual-cluster routing algorithm for UWSNs that addresses energy limitations and cluster head load.
  • To enhance the efficiency and lifespan of underwater wireless sensor networks through optimized routing.

Main Methods:

  • A dynamic layered dual-cluster routing algorithm is proposed, incorporating Krill Herd optimization.
  • Cluster size is determined by node-to-sink distance, and a dynamic layering mechanism prevents repeated cluster head selection.
  • The Krill Herd optimization algorithm and its Lagrange model are utilized to select optimal cluster heads and guide node selection.

Main Results:

  • The proposed algorithm effectively reduces energy consumption within clusters.
  • It achieves a balanced energy distribution across the network, preventing premature node failure.
  • Simulation results demonstrate a significant prolongation of the overall network lifetime.

Conclusions:

  • The dynamic layered dual-cluster routing algorithm based on Krill Herd optimization offers a viable solution for energy-efficient routing in UWSNs.
  • This approach enhances network performance by balancing energy consumption and extending operational duration.
  • The method effectively manages cluster head load and optimizes data collection and transition in underwater environments.