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

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.