Soil moisture is a fundamental factor for smart farming that is used to control the water management system. In this paper, the unmanned aerial vehicle (UAV) small cell (UAV-SC) can provide Internet of things (IoT) as the hotspot mobility network, due to the minimum limitation energy of connected IoT. The development of ground sensor (GS) communicates to the UAV-SC called GS-UAV-SC model for soil moisture monitoring is proposed to smart farming. UAV-SC aims to fulfill the data collection task with a limitation of GSs power. In the experiment, the two case scenarios: Napier grass farm and Ruzi grass farm are implemented. The result of soil moisture status is demonstrated as an example of data in real time on a mobile application monitoring system. The proposed system is useful for users/farmers to know the soil moisture data quickly for smart farming applications.
The comparison of path loss model for the unmanned aerial vehicle (UAV) and Internet of Things (IoT) air-to-ground communication system was proposed for rural precision farming. Due to the uncertainty of propagation channel in rural precision farming environment, the comparison of path loss prediction was investigated by the conventional particle swarm optimization (PSO) algorithms: PSO (exponential or Exp), PSO (polynomial or Poly) and the machine learning algorithms: k-nearest neighbor (k-NN), and random forest, are exploited to accurate the path loss models on the basic of the measured dataset. Meanwhile, the empirical model in the rural precision farming was considered. By using the machine learning-based algorithms, the coefficient of determination (R-squared: R2) and root mean squared error (RMSE) were evaluated as highly accuracy and precision more than the conventional PSO algorithms. According to the results, the random forest method was able to perform more than other methods. It has the smallest prediction errors.
The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.
Abstract. This paper has attempted to evaluate the radar cross section (RCS) of two furniture items in an indoor environment in a frequency range of 3–7 GHz of the ultra-wideband (UWB) range. The RCS evaluation is achieved through an extended version of the radar equation that incorporates the channel transfer function of scattering. The time-gating method was applied to remove the multipath effect, a phenomenon which typically occurs in the indoor environment. Two double-ridged waveguide horn antennas for both vertical and horizontal polarizations were used to obtain the transfer function of scattering of the furniture prior to analysis in order to derive their bistatic RCS. The RCS results validate the applicability of the proposed extended radar equation to the indoor propagation prediction.
The purpose of this work was to investigate the air-to-air channel model (A2A-CM) for unmanned aerial vehicle- (UAV-) enabled wireless communications. Specifically, a low-altitude small UAV needs to characterize the propagation mechanisms from ground reflection. In this paper, the empirical path loss channel characterizations of A2A ground reflection CM based on different scenarios were presented by comparing the wireless communication modules for UAVs. Two types of wireless communication modules both WiFi 2.4 GHz and LoRa 868 MHz frequency were deployed to study the path loss channel characterization between Tx-UAV and Rx-UAV. To investigate the path loss, three types of experimental channel models, such as CM1 grass floor, CM2 soil floor, and CM3 rubber floor, were considered under the ground reflection condition. The analytical A2A Two-Ray (A2AT-R) model and the modified Log-Distance model were simulated to compare the correlation with the measurement data. The measurement results in the CM3 rubber floor scenario showed the impact from the ground reflection at 1 m to 3 m Rx-UAV altitudes both 2.4 GHz and 868 MHz which was converged to the A2AT-R model and related to the modified Log-Distance model above 3 m. It clear that there is no ground reflection effect from the CM1 grass floor and CM2 soil floor. This work showed that the analytical A2AT-R model and the modified Log-Distance model can deploy to model the path loss of A2A-CM by using WiFi and LoRa wireless modules.
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