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

TL;DR

This paper introduces a hybrid PCA-optimized deep neural network trained with GOA for efficient fault detection in WSNs, achieving high accuracy and computational efficiency by reducing data dimensionality and optimizing network architecture.

Contribution

The study presents a novel hybrid PCA-GOA-DNN framework that improves fault detection accuracy and training efficiency in WSNs by combining dimensionality reduction with optimized deep learning architecture.

Findings

Achieved 99.72% classification accuracy.

Reduced data to 4 principal components retaining 99.5% variance.

Outperformed conventional fault detection methods.

Abstract

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.