<![CDATA[This paper focuses on enhancing wireless sensor networks (WSNs) for monitoring water quality in aquaculture, specifically shrimp ponds, by improving pathloss (PL) models. Radio wave propagation in such environments is challenging due to unpredictable signal attenuation caused by factors like distance, antenna height, terrain, vegetation, and weather conditions. Reliable PL modeling is essential for optimizing network performance. The research evaluates the performance of theoretical PL models, including ITU, Fitting-ITU (FITU), and Weissberger, by comparing their predictions with actual 2.4GHz radio frequency (RF) measurements. Statistical metrics such as root-mean-square error (RMSE) and the coefficient of determination (R²) were used to assess model accuracy. Initial results showed significant discrepancies, with an average RMSE of 28.7dB and an R² of only 5%. To address these issues, the study employed modification techniques (quadratic and cubic polynomial adjustments) and optimization methods, particularly particle swarm optimization (PSO). These approaches refined the theoretical models, aligning them more closely with real-world data. The optimized PSO model reduced the RMSE to 8.34dB and further to 1.89dB, while improving R² from 5% to 95.6%, demonstrating a near-perfect fit. This study highlights the critical role of PSO and similar techniques in bridging the gap between theoretical predictions and practical applications, ensuring more reliable WSN performance in aquaculture environments. The findings contribute to the development of robust, high-accuracy models tailored to the unique challenges of aquaculture settings.]]>
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