An Enhanced Whale Optimization Algorithm with Log-Normal Distribution for Optimizing Coverage of Wireless Sensor Networks

2025-11-19

AI Summary

To address the weak exploration capability and premature convergence of the conventional Whale Optimization Algorithm (WOA) in wireless sensor network (WSN) coverage optimization, this paper proposes a Log-Normal enhanced WOA (LNO-WOA). The method innovatively integrates three components: high-quality node set initialization, a leader-guided cognitive mechanism, and a logarithmic-normal modulated spiral update strategy—collectively enhancing population diversity and local exploitation precision. Evaluated on a 60 m × 60 m sensing field with 25 sensor nodes, LNO-WOA achieves a coverage rate of 99.0013%, outperforming AROA and standard WOA by up to 15.5%. It also demonstrates superior convergence speed, stability, and global search capability. The proposed approach thus provides a robust and efficient optimization framework for WSN deployment.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Wireless Sensor Networks (WSNs) are essential for monitoring and communication in complex environments, where coverage optimization directly affects performance and energy efficiency. However, traditional algorithms such as the Whale Optimization Algorithm (WOA) often suffer from limited exploration and premature convergence. To overcome these issues, this paper proposes an enhanced WOA which is called GLNWOA. GLNWOA integrates a log-normal distribution model into WOA to improve convergence dynamics and search diversity. GLNWOA employs a Good Nodes Set initialization for uniform population distribution, a Leader Cognitive Guidance Mechanism for efficient information sharing, and an Enhanced Spiral Updating Strategy to balance global exploration and local exploitation. Tests on benchmark functions verify its superior convergence accuracy and robustness. In WSN coverage optimization, deploying 25 nodes in a 60 m $ imes$ 60 m area achieved a 99.0013% coverage rate, outperforming AROA, WOA, HHO, ROA, and WOABAT by up to 15.5%. These results demonstrate that GLNWOA offers fast convergence, high stability, and excellent optimization capability for intelligent network deployment.

Problem

• Enhancing WOA to overcome premature convergence in WSN coverage optimization
• Improving search diversity using log-normal distribution and spiral updating strategy
• Achieving higher coverage rates than traditional algorithms for network deployment

Innovation

• Integrates log-normal distribution to improve convergence
• Uses Good Nodes Set for uniform population initialization
• Employs Leader Cognitive Guidance for information sharing