Search Results (10)

Signal propagation models play a fundamental role in radio frequency communication research. However, emerging communication methods, such as body-coupled communication (BCC), require the creation of new models. In this paper, we introduce mathematical models that approximate the human body as an electrical circuit, as well as linear regression- and random forest-based predictive models that infer the expected signal loss from its frequency, measurement point locations, and body parameters. The results demonstrate a close correspondence between the amplitude-frequency response (AFR) predicted by the electrical circuit models and the experimental data gathered from volunteers. The accuracy of our predictive models was assessed by using their root mean square errors (RMSE), ranging from 1.5 to 7 dB depending on the signal frequency within the 0.05 to 20 MHz range. These results allow researchers and engineers to simulate and forecast the expected signal loss within BCC systems during their design phase. Full article

Modeling radio signal propagation remains one of the most critical tasks in the planning of wireless communication systems, including wireless sensor networks (WSN). Despite the existence of a considerable number of propagation models, the studies aimed at characterizing the attenuation in the wireless channel are still numerous and relevant. These studies are used in the design and planning of wireless networks deployed in various environments, including those with abundant vegetation. This paper analyzes the performance of three vegetation propagation models, ITU-R, FITU-R, and COST-235, and compares them with path loss measurements conducted in a cassava field in Sincelejo, Colombia. Additionally, we applied four machine learning techniques: linear regression (LR), k-nearest neighbors (K-NN), support vector machine (SVM), and random forest (RF), aiming to enhance prediction accuracy levels. The results show that vegetation models based on traditional approaches are not able to adequately characterize attenuation, while models obtained by machine learning using RF, K-NN, and SVM can predict path loss in cassava with RMSE and MAE values below 5 dB. Full article

The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments. Full article

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. Full article

Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments. Full article

Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics in forested areas and tree canopy openness were investigated in the Takakuma experimental forest in Kagoshima, Japan. The aim was to evaluate the performance of the LoRa 920 MHz band with spreading factor (SF12) in a forested hilly area. The received signal strength indicator (RSSI) was measured as a function of the distance between the transmitter antenna and ground station (GS). To illustrate the effect of canopy openness on radio wave propagation, sky view factor (SVF) and a forest canopy height model were considered at each location of a successfully received RSSI. A positive correlation was found between the RSSI and SVF. It was found that between the GS and transmitter antenna, if the canopy height is above 23 m, the signal diffracted and RSSI fell to −120 to −127 dBm, so the presence of the obstacle height should be considered. Further research is needed to clarify the detailed tree density between the transmitter and ground station to propose an optimal propagation model for a forested environment. Full article

Machine learning models have been widely deployed to tackle the problem of radio propagation. In addition to helping in the estimation of path loss, they can also be used to better understand the details of various propagation scenarios. Our current work exploits the inherent ranking of feature importances provided by XGBoost and Random Forest as a means of indicating the contribution of the underlying propagation mechanisms. A comparison between two different transmitter antenna heights, revealing the associated propagation profiles, is made. Feature selection is then implemented, leading to models with reduced complexity, and consequently reduced training and response times, based on the previously calculated importances. Full article

Ecotourism activities are attracting more people each day, including national forest parks. Unfortunately, the number of incidents involving visitors to natural parks grows at the same pace. Among the most prevalent risks inside forests are getting lost and the occurrence of natural disasters. In this work, we propose a system for monitoring and assisting visitors of forest parks, based on a low power wide range wireless network, LoRa. The proposed visitor assisting system is composed of mobile terminals that communicate between them and with fixed infrastructure, using a protocol designed for exchanging visitor locations data. The infrastructure consists of wireless gateways distributed on the trails, the totems. User terminals, the mobile nodes, work collaboratively through a Delay and Disruption Tolerant Network (DTN), to cope with the possibility that the gateway infrastructure does not cover the whole trail. In addition to improvements and gains for minimizing risks, the proposal also brings contributions to the preservation of the environment, raising awareness of the influence of human presence in the natural environment and to the development of environmental education actions. Full article

In this paper we consider the D2D (Device-to-Device) communication taking place between Wireless Sensor Networks (WSN) elements operating in vegetation environments in order to achieve the radio channel characterization at 2.4 GHz, focusing on the radio links blocked by oak and pine trees modelled from specimens found in a real recreation area located within forest environments. In order to fit and validate a radio channel model for this type of scenarios, both measurements and simulations by means of an in-house developed 3D Ray Launching algorithm have been performed, offering as outcomes the path loss and multipath information of the scenarios under study for forest immersed isolated trees and non-isolated trees. The specific forests, composed of thick in-leaf trees, are called Orgi Forest and Chandebrito, located respectively in Navarre and Galicia, Spain. A geometrical and dielectric model of the trees were created and introduced in the simulation software. We concluded that the scattering produced by the tree can be divided into two zones with different dominant propagation mechanisms: an obstructed line of sight (OLoS) zone far from the tree fitting a log-distance model, and a diffraction zone around the edge of the tree. 2D planes of delay spread value are also presented which similarly reflects the proposed two-zone model. Full article

The attenuation due to vegetation can limit drastically the performance of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) communication systems. Even more for the envisaged high data rates expected for the upcoming 5G mobile wireless communications. In this context, radio planning tasks become necessary in order to assess the validity of future WSN and IoT systems operating in vegetation environments. For that purpose, path loss models for scenarios with vegetation play a key role since they provide RF power estimations that allow an optimized design and performance of the wireless network. Although different propagation models for vegetation obstacles can be found in the literature, a model combining path loss and multipath propagation is rarely considered. In this contribution, we present the characterization of the radio channel for IoT and 5G systems working at 2.4 GHz, focusing on the radio links blocked by oak and pine trees modelled from specimens found in a real recreation area located within a dense forest environment. This specific forest, composed of thick in-leaf trees, is called Orgi Forest and it is situated in Navarre, Spain. In order to fit and validate a radio channel model for this type of scenarios, both measurements and simulations by means of an in-house developed 3D Ray Launching algorithm have been performed, offers as outcomes the path loss and multipath information of the scenario under study. A geometrical and dielectric model of the trees were created and introduced in the simulation software. The path loss was then estimated as dependent of the radio link range for two species of trees at 2.4 GHz. We concluded that the scattering produced by the tree can be divided into two zones with different dominant propagation mechanisms: a free-space zone far from the tree and a diffraction zone around the edge of the tree. 2D planes of delay spread value are also presented which similarly reflects the proposed two-zone model. Full article