Charger placement optimization in wireless sensor networks using hybrid graph coloring and Enhanced Aquila...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) require efficient charger deployment for sustainability.
• Existing methods lack optimal solutions for charger placement and quantity.
• Intelligent deployment is crucial for network longevity and performance.

Purpose of the Study:

• To propose a two-stage optimization framework for charger deployment in WSNs.
• To determine the minimal number of chargers and their optimal positions.
• To enhance WSN sustainability through efficient energy management.

Main Methods:

• A hybrid algorithm combining degree-based saturation and Grundy coloring for charger quantity determination.
• An Enhanced Aquila Optimization algorithm for optimal charger placement under coverage and power constraints.
• Comparative analysis against standard optimization algorithms.

Main Results:

• The Enhanced Aquila Optimization algorithm achieved 99% sensor coverage, surpassing existing methods.
• Improved coverage by 6% compared to the standard Aquila Optimization algorithm.
• Demonstrated faster convergence and significant performance improvements.

Conclusions:

• The proposed framework offers a practical, scalable, and energy-efficient solution for WSN charger deployment.
• The Enhanced Aquila Optimization algorithm significantly enhances network coverage and efficiency.
• This approach contributes to the sustainability of wireless sensor networks.