An improved grey wolf optimizer with multi-stage differentiation strategies coverage in three-dimensional wireless sensor network
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 mo...
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| Published in: | Scientific reports |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
24.11.2025
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| ISSN: | 2045-2322, 2045-2322 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2045-2322 2045-2322 |
| DOI: | 10.1038/s41598-025-28820-x |