Review of Hybrid Localization Frameworks in Wireless Sensor Networks for Precision Agriculture Applications

<ns5:p>Localization is a critical component of Wireless Sensor Networks (WSNs), particularly in precision agriculture, where accurate sensor positioning is essential for irrigation management, crop monitoring, and environmental analysis. However, agricultural environments introduce challenges such as vegetation-induced attenuation, non–line-of-sight (NLOS) conditions, and large-scale deployments. This paper aims to review and analyse hybrid localization frameworks in WSNs and evaluate their effectiveness in improving accuracy, energy efficiency, and robustness under agricultural conditions. A comprehensive review of localization techniques, including range-free, range-based, optimization-based, machine learning, filtering, anchor-light, and UAV-assisted methods, is conducted. A unified taxonomy is developed to compare these approaches based on accuracy, scalability, energy consumption, and NLOS resilience. Additionally, a case study is performed using a hybrid RSSI–DV-Hop approach in both 2D and 3D agricultural environments under realistic attenuation conditions. The analysis shows that hybrid localization methods outperform standalone techniques in challenging environments. The proposed distance-level hybrid approach demonstrates reduced localization error compared to conventional methods, achieving improved RMSE performance under vegetation-induced attenuation in both 2D and 3D scenarios. Hybrid and adaptive localization frameworks offer a promising solution for reliable and energy-efficient WSN deployment in precision agriculture. Future research should focus on environment-aware algorithms, lightweight machine learning integration, and scalable real-world implementations.</ns5:p>