What question did this study set out to answer?

To develop a framework for real-time performance prediction in LoRa networks used in smart agriculture.

January 14, 2026AgriEngineering1 citationsOpen Access

RF-Driven Adaptive Surrogate Models for LoRaDisC Network Performance Prediction in Smart Agriculture and Field Sensing Environments

SBShowkat Ahmad BhatSouthern Taiwan University of Science and Technology ISIshfaq Bashir SofiConcordia University MCMing-Che ChenNational Yunlin University of Science and Technology

Puntos clave

To develop a framework for real-time performance prediction in LoRa networks used in smart agriculture.
Implemented a deep learning-driven framework using Bayesian surrogate modeling and Random Forest for prediction
Targeted link- and network-level performance metrics like PRR, RSSI, and SNR
Utilized multivariate time series features for accurate predictions
Conducted experiments on the ChirpBox outdoor LoRa testbed to validate findings
Achieved a mean absolute error of 8.83 in performance predictions
Demonstrated strong correlation between aggregated metrics and PRR
Identified significant influence of environmental variables like temperature on performance

Resumen

LoRa-based IoT systems are increasingly used in smart farming, greenhouse monitoring, and large-scale agricultural sensing, where long-range, energy-efficient communication is essential. However, estimating link quality metrics such as PRR, RSSI, and SNR typically requires continuous packet transmission and sequence logging, an impractical approach for power-constrained field nodes. This study proposes a deep learning-driven framework for real-time prediction of link- and network-level performance in multihop LoRa networks, targeting the LoRaDisC protocol commonly deployed in agricultural environments. By integrating Bayesian surrogate modeling with Random Forest-guided hyperparameter optimization, the system accurately predicts PRR, RSSI, and SNR using multivariate time series features. Experiments on a large-scale outdoor LoRa testbed (ChirpBox) show that aggregated link layer metrics strongly correlate with PRR, with performance influenced by environmental variables such as humidity, temperature, and field topology. The optimized model achieves a mean absolute error (MAE) of 8.83 and adapts effectively to dynamic environmental conditions. This work enables energy-efficient, autonomous communication in agricultural IoT deployments, supporting reliable field sensing, crop monitoring, livestock tracking, and other smart farming applications that depend on resilient low-power wireless connectivity.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo