Modified COST-235 Empirical Pathloss Model for Agricultural WSN Using Particle Swarm Optimization
Authors: Tahcfulloh Syahfrizal , Wahyuni Etty , Santoso Dwi , Juliannanda Rizkyandi 
Published: January 2025

The increasing demand for agricultural products yearly encourages farmers to seek solutions to migrate from conventional farming to smart and precise farming by utilizing technological advances such as implementing wireless sensor networks (WSN). Unlike conventional farming, this technology is believed to provide many advantages, including low cost, high efficiency, optimized land use, and high productivity results. However, this system is highly dependent on the availability of network interconnection where the bottleneck is the instability of signal strength and path loss, especially for radio wave propagation from the transmitter (Tx) in the form of sensors to the receiver (Rx) in the form of data processors where its performance depends on the distance, agricultural, environmental conditions, and surrounding vegetation. This paper explicitly examines and analyzes radio wave propagation modeling for measuring radio frequency (RF) signal strength in local agriculture's 2.4 GHz WSN system, such as Adan rice, corn, and peanuts. The particle-swarm-optimization (PSO) method is used to modify empirical path loss models such as Weissberger, ITU-vegetation, COST-235, Egli, and FITU-R, which also involve the influence of rain attenuation. Several other factors are also considered in the evaluation and analysis, i.e., the planting period of agricultural crops (seedlings, growth, and maturity), vegetation depth, and the height of the Tx-Rx antenna from the ground. The results of the experimental evaluation show that the PL COST-235 model continues to be optimized using the PSO method because it has the lowest RMSE both in conditions without and with rain attenuation, which are 23.30 and 9.33, respectively. Meanwhile, after the selected model is optimized using the PSO method, the RMSE for both conditions becomes 2.49 and 5.29.   
ABSTRAK: Permintaan yang semakin meningkat terhadap produk pertanian setiap tahun mendorong para petani untuk mencari penyelesaian bagi beralih daripada pertanian konvensional kepada pertanian pintar dan tepat dengan memanfaatkan kemajuan teknologi seperti penggunaan rangkaian sensor tanpa wayar (WSN). Berbeza dengan pertanian konvensional, teknologi ini dipercayai memberikan banyak kelebihan, termasuk kos yang rendah, kecekapan yang tinggi, pengoptimuman penggunaan tanah, dan hasil produktiviti yang tinggi. Namun begitu, sistem ini sangat bergantung kepada ketersediaan rangkaian interkoneksi di mana kelemahan utamanya adalah ketidakstabilan kekuatan isyarat dan kehilangan laluan (path loss), terutamanya bagi penyebaran gelombang radio dari pemancar (Tx) berbentuk sensor ke penerima (Rx) berbentuk pemproses data, yang prestasinya bergantung kepada jarak, keadaan persekitaran pertanian, dan tumbuh-tumbuhan di sekeliling. Kajian ini secara khusus meneliti dan menganalisis pemodelan penyebaran gelombang radio untuk mengukur kekuatan isyarat frekuensi radio (RF) dalam sistem WSN 2.4 GHz di pertanian tempatan seperti padi Adan, jagung, dan kacang tanah. Kaedah pengoptimuman kawanan zarah (particle-swarm-optimization, PSO) digunakan untuk mengubah suai model kehilangan laluan empirikal seperti Weissberger, ITU-vegetation, COST-235, Egli, dan FITU-R, yang turut melibatkan pengaruh pelemahan hujan. Beberapa faktor lain juga dipertimbangkan dalam penilaian dan analisis ini, seperti tempoh penanaman tanaman pertanian (anak benih, pertumbuhan, dan kematangan), kedalaman tumbuh-tumbuhan, dan ketinggian antena Tx-Rx dari permukaan tanah. Hasil penilaian eksperimen menunjukkan bahawa model PL COST-235 terus dioptimumkan menggunakan kaedah PSO kerana ia mempunyai nilai RMSE paling rendah dalam kedua-dua keadaan tanpa dan dengan pelemahan hujan, iaitu masing-masing 23.30 dan 9.33. Sementara itu, selepas model yang dipilih dioptimumkan menggunakan kaedah PSO, nilai RMSE bagi kedua-dua keadaan menjadi 2.49 dan 5.29.