An improved grey wolf optimizer with multi-stage differentiation strategies coverage in three-dimensional wireless...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are crucial for IoT, connecting physical and digital realms.
• Optimizing coverage in 3D WSNs over complex terrains is a significant challenge.
• Existing 2D models do not accurately represent real-world 3D spatial dynamics.

Purpose of the Study:

• To enhance 3D WSN coverage efficiency.
• To reduce deployment costs in complex 3D environments.
• To introduce an improved optimization algorithm for 3D WSN deployment.

Main Methods:

• Proposed an Improved Grey Wolf Optimizer with Multi-Stage Differentiation Strategies (IGWO-MSDS).
• Incorporated a split-pheromone guidance strategy for early-stage information exchange.
• Utilized a hybrid Grey Wolf-Artificial Bee Colony strategy for mid-stage exploration/exploitation balance.
• Implemented a Lévy flight mechanism for late-stage performance refinement.

Main Results:

• IGWO-MSDS demonstrated superior performance in optimal coverage, average coverage, and standard deviation.
• Simulations confirmed IGWO-MSDS outperforms GWO, SSA, WOA, GOA, OGWO, DGWO1, and DGWO2.
• The algorithm achieved significant improvements in coverage efficiency and cost reduction.

Conclusions:

• IGWO-MSDS offers a scalable and energy-efficient solution for 3D WSN deployment.
• The proposed method effectively addresses coverage optimization challenges in complex 3D environments.
• This contributes to the advancement of IoT systems in real-world, complex settings.