An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm.

Area of Science:

• Agricultural Engineering
• Sensor Networks
• Optimization Algorithms

Background:

• Soil element monitoring wireless sensor networks (SEMWSNs) are crucial for precision agriculture, enabling timely adjustments to irrigation and fertilization.
• Optimizing the number and placement of sensor nodes is critical for maximizing field coverage and ensuring efficient monitoring.
• Existing metaheuristic algorithms face challenges in achieving optimal coverage and avoiding local optima in SEMWSN deployment.

Purpose of the Study:

• To propose a novel Adaptive Chaotic Gaussian Variant Snake Optimization Algorithm (ACGSOA) for optimizing SEMWSN deployment.
• To enhance the coverage rate and convergence speed of SEMWSN deployment strategies.
• To address the challenge of achieving maximum field coverage with a minimal number of sensor nodes.

Main Methods:

• Development of a unique Adaptive Chaotic Gaussian Variant Snake Optimization Algorithm (ACGSOA).
• Introduction of a new chaotic operator to optimize individual position parameters and accelerate convergence.
• Design of an adaptive Gaussian variant operator to prevent local optima during sensor node deployment.

Main Results:

• ACGSOA demonstrated superior performance compared to traditional algorithms like SO, WOA, ABC, and FOA in simulation experiments.
• The proposed ACGSOA achieved significant improvements in convergence speed.
• Coverage rate was enhanced by 7.20% (vs. SO), 7.32% (vs. WOA), 7.96% (vs. ABC), and 11.03% (vs. FOA).

Conclusions:

• The ACGSOA is a robust and efficient algorithm for SEMWSN deployment, offering low complexity and fast convergence.
• ACGSOA significantly improves monitoring field coverage, leading to better agricultural management and crop yield.
• The algorithm effectively avoids local optima, ensuring a more comprehensive deployment strategy for SEMWSNs.