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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor

Halimjon Khujamatov1, Mohaideen Pitchai2, Alibek Shamsiev3

  • 1Department of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Gyeonggi-do, Republic of Korea.

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Summary

This study introduces a new method for wireless sensor networks that uses a chaotic genetic algorithm and grey wolf optimizer to reduce energy use. The approach improves network performance and extends the lifespan of sensor nodes.

Keywords:
chaotic genetic algorithmclusteringenergy efficiencygrey wolf optimizerroutingsensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) face significant energy depletion during data transmission, particularly in large-scale deployments.
  • Clustering architectures aim to minimize energy consumption but achieving optimal energy efficiency and high packet rates in routing remains challenging.
  • Existing strategies often struggle with the NP-hard nature of developing effective clustering-based routing algorithms for WSNs.

Purpose of the Study:

  • To develop an energy-efficient clustering mechanism and an energy-saving routing system for large-scale wireless sensor networks.
  • To address the NP-hard problem of optimizing both energy efficiency and packet transmission rates in WSNs.
  • To introduce a novel hybrid optimization approach combining chaotic genetic algorithm and grey wolf optimizer for WSN routing.

Main Methods:

  • Formulation of an energy-efficient clustering mechanism utilizing a chaotic genetic algorithm (CGA).
  • Development of an energy-saving routing system employing a bio-inspired grey wolf optimizer (GWO) algorithm.
  • Integration of CGA and GWO into a hybrid method (CGA-GWO) for selecting energy-aware cluster heads and optimizing routing paths.

Main Results:

  • The proposed CGA-GWO method effectively minimizes overall energy consumption in wireless sensor networks.
  • Simulation results indicate enhanced network functionality compared to three other relevant systems.
  • Key performance metrics such as the number of live nodes, average remaining energy, and packet delivery ratio were improved.

Conclusions:

  • The CGA-GWO approach offers a superior solution for energy-efficient routing in large-scale wireless sensor networks.
  • The hybrid optimization strategy successfully balances energy conservation with high data transmission rates.
  • The method demonstrates significant improvements in network longevity and data delivery efficiency.