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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
1.1K
Smart pareto-optimized genetic algorithm for energy-efficient clustering and routing in wireless sensor networks.
M Rajalakshmi1, S Ponni Alias Sathya2
1Department of Artificial Intelligence and Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India. rajalakshmi.res89@gmail.com.
Scientific Reports
|October 8, 2025
Summary
A new Pareto-based Genetic Algorithm for Energy-Efficient Clustering and Routing (PGAECR) optimizes wireless sensor networks (WSNs). This method enhances energy efficiency and network longevity, outperforming existing solutions.
Area of Science:
- Computer Science
- Network Engineering
- Wireless Sensor Networks
Background:
- Wireless Sensor Networks (WSNs) face limitations in power, storage, and computation.
- These constraints hinder efficiency and network lifespan in critical applications like healthcare, business, and military operations.
Purpose of the Study:
- To introduce a novel Pareto-based Genetic Algorithm for Energy-Efficient Clustering and Routing (PGAECR).
- To enhance energy efficiency and extend the operational lifetime of WSNs.
Main Methods:
- PGAECR integrates historical best solutions to accelerate convergence and improve results.
- Clustering and routing decisions are unified within a single chromosome.
- A multi-objective fitness function evaluates energy consumption, residual energy, load distribution, and network longevity.
Main Results:
- PGAECR demonstrated superior performance compared to five other methods.
- The algorithm achieved a 12.4% reduction in energy consumption.
- Network longevity was increased by 15.7%.
Conclusions:
- PGAECR effectively addresses WSN limitations by optimizing energy efficiency and load balancing.
- The proposed algorithm significantly extends network lifespan and reduces energy usage.
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