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

Abstract

Wireless Sensor Networks (WSNs) play a vital role in bridging the physical and digital worlds, enabling real-time data collection for IoT applications. However, optimizing coverage in three-dimensional (3D) WSNs with complex terrains remains a significant challenge, as traditional two-dimensional models fail to reflect real-world spatial dynamics. This paper proposes an improved Grey Wolf Optimizer with Multi-Stage Differentiation Strategies (IGWO-MSDS) to enhance 3D WSN coverage while reducing deployment cost and improving coverage efficiency. The algorithm introduces three key enhancements: (1) a split-pheromone guidance strategy in the early iteration stage to boost information exchange among agents; (2) a hybrid Grey Wolf–Artificial Bee Colony strategy during the mid-stage to balance global exploration and local exploitation; and (3) a Lévy flight mechanism in the late stage to refine search performance. IGWO-MSDS was evaluated through extensive simulations and compared with GWO, SSA, WOA, GOA, OGWO, DGWO1, and DGWO2. Results show that IGWO-MSDS achieves superior performance across key metrics, including optimal coverage, average coverage, and standard deviation. The proposed approach provides a scalable and energy-efficient solution for 3D WSN deployment, contributing to the advancement of IoT systems in complex environments.