Detecting sensor faults in wireless sensor networks for precision agriculture using long short-term memory

Abstract

The reliable acquisition of soil data from wireless sensor networks (WSNs) deployed in farmlands is critical for optimizing precision agriculture (PA) practices. However, sensor faults can significantly degrade data quality, hindering PA techniques. Our work proposes a novel long short-term memory (LSTM) network-based method for fault detection in WSNs for PA applications. Unlike traditional methods, our approach utilizes a lightweight, transfer learning-based LSTM architecture specifically designed to address the challenge of limited labeled training data availability in agricultural settings. The model effectively captures temporal dependencies within sensor data sequences, enabling accurate predictions of normal sensor behavior and identification of anomalies indicative of faults. Experimental validation confirms the effectiveness of our method in diverse real-world WSN deployments, ensuring data integrity and enhancing network reliability. This study paves the way for improved decision-making and optimized PA practices.