A Novel Machine Learning Strategy for Anomaly Identification Scheme in Wireless Sensor Networks Using Supervised Training Principles

Anomaly detection in Wireless Sensor Networks (WSN s) is critical for maintaining the integrity and reliability of various monitoring systems, especially in agricultural settings. The core of this study is the development of a hybrid anomaly detection model, SVM-RFNet, which integrates Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NN). By applying noise reduction techniques, such as smoothing algorithms, the data quality is significantly enhanced, resulting in cleaner and more stable datasets. Feature engineering, including temporal trend analysis and Principal Component Analysis (PCA), further refines the dataset by extracting and selecting the most informative features. This model combines the strengths of each method, leveraging SVM's robustness in high-dimensional data, RF's ensemble learning capabilities, and NN's ability to learn complex patterns. The SVM-RFNet model achieved an impressive accuracy of 96%, demonstrating its efficacy in real-time anomaly detection. The paper concludes with a discussion on future research directions, including the integration of advanced deep learning techniques and the development of self-healing networks, which promise to further enhance the capabilities and applications of WSN s in various domains.