Wireless Sensor Networks Nodes Clustering and Optimization Based on Fuzzy C-Means and Water Strider Algorithms

Wireless sensor networks (WSNs) face critical challenges in energy management and network lifetime optimization due to limited battery resources and communication overhead. This study introduces a novel hybrid clustering protocol that integrates the Water Strider Algorithm (WSA) with Fuzzy C-Means (FCM) clustering to achieve superior energy efficiency and network longevity. The proposed WSA-FCM method employs WSA for global optimization of cluster-head positions and FCM for refined node membership assignment with fuzzy boundaries. Through extensive experimentation across networks of 200-800 nodes with 10 independent simulation runs, the method demonstrates significant improvements: First Node Death (FND) delayed by 16.1% ($678\pm12$ vs $584\pm18$ rounds), Last Node Death (LND) extended by 11.9% ($1,262\pm8$ vs $1,128\pm11$ rounds), and 37.4% higher residual energy retention ($5.47\pm0.09$ vs $3.98\pm0.11$ J) compared to state-of-the-art hybrid methods. Intra-cluster distances are reduced by 19.4% with statistical significance (p < 0.001). Theoretical analysis proves convergence guarantees and complexity bounds of $O(n\times c\times T)$, while empirical scalability analysis demonstrates near-linear scaling behaviour. The method outperforms recent hybrid approaches including MOALO-FCM, MSSO-MST, Fuzzy+HHO, and GWO-FCM across all performance metrics with rigorous statistical validation.

Efficient Fault Detection in WSN Based on PCA-Optimized Deep Neural Network Slicing Trained with GOA

Fault detection in Wireless Sensor Networks (WSNs) is crucial for reliable data transmission and network longevity. Traditional fault detection methods often struggle with optimizing deep neural networks (DNNs) for efficient performance, especially in handling high-dimensional data and capturing nonlinear relationships. Additionally, these methods typically suffer from slow convergence and difficulty in finding optimal network architectures using gradient-based optimization. This study proposes a novel hybrid method combining Principal Component Analysis (PCA) with a DNN optimized by the Grasshopper Optimization Algorithm (GOA) to address these limitations. Our approach begins by computing eigenvalues from the original 12-dimensional dataset and sorting them in descending order. The cumulative sum of these values is calculated, retaining principal components until 99.5% variance is achieved, effectively reducing dimensionality to 4 features while preserving critical information. This compressed representation trains a six-layer DNN where GOA optimizes the network architecture, overcoming backpropagation's limitations in discovering nonlinear relationships. This hybrid PCA-GOA-DNN framework compresses the data and trains a six-layer DNN that is optimized by GOA, enhancing both training efficiency and fault detection accuracy. The dataset used in this study is a real-world WSNs dataset developed by the University of North Carolina, which was used to evaluate the proposed method's performance. Extensive simulations demonstrate that our approach achieves a remarkable 99.72% classification accuracy, with exceptional precision and recall, outperforming conventional methods. The method is computationally efficient, making it suitable for large-scale WSN deployments, and represents a significant advancement in fault detection for resource-constrained WSNs.