Empirical approach of ad hoc path loss propagation model in realistic forest environments

The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

Section: Technical Analysismentioning

confidence: 99%

Section: Technical Analysismentioning

confidence: 99%

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Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review

Barrios-Ulloa

Ariza-Colpas

Sánchez-Moreno

et al. 2022

“…Accordingly, researchers have accelerated their efforts to provide reliable models for various environments and scenarios over a wide range of frequency regimes to assist network engineers in designing reliable future wireless networks and accurate link budget calculations. Moreover, accurate predictions could be beneficial in radio resource management schemes that aim to meet specific Quality-of-Service (QoS) criteria [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels

Elmezughi

Salih

Afullo

et al. 2022

Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.

“…One of the primary uses are attempts to locate an object (device) on the basis of received signal strength indicator (RSSI). Many papers describe the use of RSSI for indoor location [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] and outdoor location [ 8 , 9 , 10 , 11 , 12 , 13 ]. The main reason for these applications is the wide availability and affordability of devices using the received signal strength indicator.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Path-Loss Exponent by Bayesian Filtering Method

Wójcicki

Zientarski

Charytanowicz

et al. 2021

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.