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Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.

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Updated: Jun 28, 2025

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

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

Published on: September 8, 2023

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic

Mohammed Y Aalsalem¹

¹Farasan Networking Research Laboratory, College of Computer Science & Information Technology, Jazan University, Jazan, Saudi Arabia.

|April 17, 2024

Summary

This summary is machine-generated.

This study introduces the Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) to improve Wireless Sensor Networks (WSNs). HM-PGA effectively mitigates energy hotspots, extending network lifetime and conserving energy.

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Last Updated: Jun 28, 2025

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Area of Science:

Computer Science
Network Engineering
Optimization Algorithms

Background:

Wireless Sensor Networks (WSNs) are critical for distributed monitoring, but energy consumption imbalances create hotspot challenges near data sinks.
Effective clustering and routing are vital for WSN performance and longevity.
Existing methods struggle with efficient Cluster Head selection and hotspot mitigation.

Purpose of the Study:

To propose an optimized clustering and routing protocol for WSNs to address energy consumption imbalances.
To enhance network lifetime and performance by mitigating hotspot issues.
To introduce a novel approach for Cluster Head selection using metaheuristic optimization.

Main Methods:

Employing a clustering approach with sub-clustering for efficient data aggregation.
Utilizing a Genetic Algorithm (GA) for Cluster Head (CH) selection, considering multiple factors.
Integrating Prairie Dog Optimization (PDO) to enhance GA's management and overcome its limitations.
Developing the Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) protocol.

Main Results:

The HM-PGA protocol significantly improves WSN performance, particularly in hotspot avoidance.
Achieved a network lifetime of 20913 milliseconds.
Maintained 310 joules of remaining energy, indicating efficient energy conservation.
Demonstrated superior performance compared to existing WSN techniques through comparative analysis.

Conclusions:

The HM-PGA approach effectively balances energy consumption and extends the operational lifespan of WSNs.
Combining GA and PDO provides a robust mechanism for optimal CH selection and network management.
The proposed method offers a significant advancement in WSN performance and reliability, especially in energy-constrained environments.