A-H. (2019). The impact of spatial-temporal averaging on the dynamic-statistical properties of rain fields. IEEE Transactions on Antennas and Propagation, 67(10), [AP1610-153].
A new comprehensive space-time model for the characterization of point rainfall rate is presented. A detailed assessment of four key rain characteristics (probability of rain/no rain condition, first and second order lognormal statistics and, space and time correlation functions) with consideration of the impact of varying spatial-temporal integration lengths are discussed. A set of empirical equations have been developed and the results show that they provide estimates of probability of rain/no rain with root mean square errors of less than 1.3 in space and 0.04 in time. They provide good estimates of the parameters at any space-time scales, particularly at higher resolutions that are of great importance to the design and planning of networks operating at frequencies above 10 GHz. In particular, the authors have created databases of rain characteristic parameters spanning North West Europe from which rain rate at any location of interest at different space-time scales can be conveniently obtained. These have been validated by comparing the rain rate exceedance distribution, R 0.01 , from the model estimates at different space-time scales across the British Isles with values calculated from measured data. It has been found that the proposed model gives highly accurate estimates of R 0.01 for the continental area with error percentages (E) generally less than 2.5 but the error percentage increases at the edges of the radar scans and in the oceanic area due to low data availability. INDEX TERMS Rainfall rate, rain characteristics, radio-wave propagation, space-time model, satellite.
Because of the lack of fixed infrastructures, the existence of open media and diverse network topologies, internetworking networks and mobile ad hoc networks (MANET’s), the design of MANET protocols is complicated. In this paper, we propose an evolutionary trust mechanism imitating cognitive processes that uses sensitive information to avoid routing. Moreover, we propose an Enhanced Self-organizing Cooperation and Trust based (ESCT) Protocol, where the mobile nodes share self-reliance and interpret information from a cognitive point of view. Each node develops its information dynamically to eradicate malicious entities. The most attractive attribute of the proposed ESCT protocol, even if domestic attackers know how it operates, is to prevent infringements. In this paper, the efficiency of the proposed ESCT protocol is assessed for different routing disturbances and varying number of attackers. The results of a simulation show that, the proposed ESCT protocol supports diverse network platforms and provides an efficient routing method for MANET routers. The proposed ESCT protocol displays increased throughput, reduction in end-to-end delay and increase in packet delivery ratio when compared to the peers that were taken for comparison.
Rain prediction is challenging due to the complex nonlinear combination of atmospheric factors. This paper presents the application of logistic regression modelling to predict rain the next day using weather parameters from the previous days. One year of weather data (temperature, pressure, humidity, sunshine, evaporation, cloud cover, wind direction, and wind speed) from Canberra, Australia has been used to develop the logistic regression-based model. Akaike Information Criterion (AIC) Backward, Baysian Information Criterion (BIC) Stepwise, and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression models have been developed based on input variable selection and prediction. These models are evaluated using Area Under the ROC Curve(AUC) and Hosmer-Lemeshow test to determine the models' adequacies and accuracies to predict rainfall occurrence the next day. The likelihood of rainfall the next day has been interpreted based on the calculated odds ratios with 95% confidence intervals of the selected independent weather parameters. The result showed that the rainfall the next day can be predicted using logistic regression (AIC Backward) with 87% accuracy, provided that the appropriate weather parameters are chosen.
This paper presents two new state-of-the-art Spatial Rain Field Interpolation Convolutional Neural Networks (SRFICNNs), referred to as LD (Learned Deviation) and LI (Learned Interpolation) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high resolution rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models' performances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and Structural SIMilarity (SSIM) in rainfall fields reconstruction if the network depth falls in the range of 15~25 weight layers. The final model uses 20 layers for high resolution point rain rates prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this paper compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimates of these two indices with corresponding Root Mean Square (RMS) error of 5.1709 × 10 -4 and 0.0013, respectively.
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