An efficient charging strategy for wireless sensor networks based on saturation degree and Enhanced Grey Wolf...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) are essential for modern applications, facing challenges in long-term energy management.
• Recharging sensors is preferred over battery replacement for cost-effectiveness and sustainability.
• Optimal charger placement is critical for maximizing sensor coverage and minimizing deployment costs in WSNs.

Purpose of the Study:

• To propose a novel hybrid optimization framework for the charger placement problem in WSNs.
• To enhance the efficiency and cost-effectiveness of energy replenishment in wireless sensor networks.
• To ensure maximum sensor coverage and network longevity through strategic charger deployment.

Main Methods:

• A hybrid optimization framework combining graph-theoretical Degree of Saturation approach with Enhanced Grey Wolf Optimization algorithm.
• The Degree of Saturation method is utilized to identify sensor groups, reducing the number of required chargers.
• Enhanced Grey Wolf Optimization algorithm is employed to determine the optimal spatial positions for charger deployment.

Main Results:

• The proposed hybrid method achieved a network efficiency of 97%, outperforming conventional techniques.
• Demonstrated significant improvements over wavelet-based methods (Haar, Daubechies 2, Biorthogonal, Symlets 8) and evolutionary algorithms (Raindrop, Blackhole).
• Simulations confirmed the robustness and effectiveness of the Enhanced Grey Wolf Optimization-based approach for practical WSN deployments.

Conclusions:

• The hybrid optimization framework offers a superior solution for charger placement in wireless sensor networks.
• The Enhanced Grey Wolf Optimization algorithm significantly improves energy replenishment efficiency and network coverage.
• The proposed method provides a robust and effective strategy for real-world WSN deployments, enhancing operational longevity.