Modified COST-235 Empirical Pathloss Model for Agricultural WSN Using Particle Swarm Optimization - Syahfrizal Tahcfulloh; Etty Wahyuni; Dwi Santoso; Rizkyandi Juliannanda (International Islamic University Malaysia Engineering Journal)
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.