Deep Learning-Based Fault Detection in Wireless Sensor Networks for Precision Agriculture

Wireless Sensor Networks (WSNs) are integral to precision agriculture, enabling real-time monitoring of environmental parameters critical to crop management. However, the reliability of WSNs is often compromised due to sensor faults such as missing data, sensor degradation, and communication errors, leading to inaccurate readings. This paper proposes a deep learning-based framework for fault detection in agricultural WSNs using Recurrent Neural Networks (RNNs). We utilize the Intel Berkeley Research Lab dataset, which includes real-world sensor data with naturally occurring faults, to train the RNN model. The model captures temporal dependencies in sensor readings and effectively distinguishes between faulty and normal sensor behaviors. Experimental results demonstrate that the RNN-based approach outperforms traditional machine learning techniques in terms of accuracy, precision, and robustness, offering a reliable solution for maintaining the integrity of data in agricultural WSNs. This research enhances the fault resilience of precision agriculture systems, ensuring more accurate decision-making and contributing to sustainable farming practices.