Near-Ground Wireless Coverage Design in Rural Environments

[EN] Due to the broad range of options that wireless systems offer, Wi-Fi products are increasingly being used in agriculture environments to improve farming practices and better control the output of the production. However, the foliage has proven to harm radio-frequency propagation as well as decreasing the coverage area of Wireless Sensor Networks (WSNs). Therefore, near-ground channel characterization can help in avoiding high antennas and vegetation. Nevertheless, theoretical models tend to fail when forecasting near-ground path losses. This paper aims at determining how the field components such as soil, grass and, trunks affect radio-links in near-ground scenarios. To do this, we measure the Received Signal Strength (RSSI), the Signal to Interference Ratio (SIR) and the Round-Trip Time (RTT) of a Wireless Local Area Network (WLAN), at different distances, and the results are compared with 3 prediction models: the Free-Space Propagation Model, Two-Ray Ground Reflection Model and, One-Slope Log-Normal Model. The experiment was carried out by collecting experimental data at two different locations, i.e., an orange tree plantation and a field without vegetation, taking measurements every meter. A comprehensive analysis of the influence of rural environments can help to obtain better near-ground WSN performance and coverage in precision agriculture.This work has been partially supported by European Union through the ERANETMED project ERANETMED3- 227 SMARTWATIR, by the Ministerio de Ciencia, Innovación y Universidades through the Ayudas para la adquisición de equipamiento científico-técnico, Subprograma estatal de infraestructuras de investigación y equipamiento científico-técnico (plan Estatal I+D+i 2017- 2020) (project EQC2018-004988-P), by the Universidad de Granada through the "Programa de Proyectos de Investigación Precompetitivos para Jóvenes Investigadores. Modalidad A jóvenes Doctores of "Plan Propio de Investigación y Transferencia 2019" (PPJIA2019.10) and by the Campus de Excelencia Internacional Global del Mar (CEI·Mar) through the "Ayudas Proyectos Jóvenes Investigadores CEI·Mar 2019" (Project CEIJ-020).Botella-Campos, M.; Jimenez, JM.; Sendra, S.; Lloret, J. (2020). Near-Ground Wireless Coverage Design in Rural Environments. IARIA XPS Press. 14-19. http://hdl.handle.net/10251/178039S141

Wireless Channel Path-Loss Modelling for Agricultural and Vegetation Environments: A Survey

This work undertakes an extensive survey of the channel modelling methods and path-loss characterization carried out in agricultural fields and vegetation environments in an attempt to study the state-of-the-art in this field, which, though vastly explored, still presents extremely diverse opportunities and challenges. The interface for communication between nodes in a typical agricultural field is the wireless channel or air interface, making it imperative to address the impairments that are exclusive to such a communication scenario by studying the characteristics of the medium. The performance of the channel is a direct indicator of the quality of communication. It is required to have a lucid understanding of the channel to ensure quality in transmission of the required information, while simultaneously ensuring maximum capacity by employing limited resources. The impairments that are the very nature of a typical wireless channel are treated in an explicit manner covering the theoretical and mathematical models, analytical aspects and empirical models. Although there are several propagation models characterized for generic indoor and outdoor environments, these cannot be applied to agricultural, vegetation, forest and foliage scenarios due to the various additional factors that are specific to these environments. Owing to the wide variety, size, properties and span of the foliage, it also becomes extremely challenging to develop a generic predictive model for all kinds of crops or vegetation. The survey is categorized into fields containing specific crops, greenhouse environment and forest/foliage scenarios and the key findings are presented

Experimental Validation of a Best-Fit Model for Predicting Radio Wave Propagation through Vegetation

In this study, a model for predicting radio wave propagation through vegetation at 900 and 1800MHz is proposed. An integrated model comprising of ground and foliage induced effects is evaluated with respect to experimental data obtained through drive test in and around a vegetation environment, using Test Mobile System (TEMS) investigation tools. Measured path loss was compared against predictions made by four empirical vegetation models. Results indicate that the European Cooperation in Science and Technology (COST) 235 model gives the best prediction and compare favourably with measured path loss in areas where vegetation is dominant. Although, this model showed the most accurate prediction of foliage loss in the investigated area, there is a need to modify it for enhanced signal prediction. The modified model was found to predict the measured path loss with Root Mean Square Errors (RMSEs) of 6.98dB and 10.00dB at 900 and 1800MHz, respectively. Overall, findings revealed that these RMSEs are within the acceptable range of up to 15.00dB, for quality signal prediction in related environment

Bounding the Practical Error of Path Loss Models

We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios where a priori predictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible

CHANNEL MODELING FOR FIFTH GENERATION CELLULAR NETWORKS AND WIRELESS SENSOR NETWORKS

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance

Electromagnetic Characteristics of the Soil

The electromagnetic characteristics of the soil are discussed in this chapter. The characteristics of porous bedrock, soil medium, and impacts of rain attenuations are also presented. The models of dielectric soil properties are studied with a rigorous focus on the constitutive parameters of subsurface soil medium. Moreover, the permittivity and wavenumber in soil are explained. In addition, the frequency-dependent dielectric properties such as dispersion in soil, absorption characteristic, and penetration depth versus frequency are reviewed. Furthermore, the effective permittivity of soil–water mixture for through-the soil-propagation mechanism is analyzed thoroughly

Techniques for Wireless Channel Modeling in Harsh Environments

With the rapid growth in the networked environments for different industrial, scientific and defense applications, there is a vital need to assure the user or application a certain level of Quality of Service (QoS). Environments like the industrial environment are particularly harsh with interference from metal structures (as found in the manufacturing sector), interference generated during wireless propagation, and multipath fading of the radio frequency (RF) signal all invite novel mitigation techniques. The challenge of achieving the benefits like improved energy efficiency using wireless is closely coupled with maintaining network QoS requirements. Assessment and management of QoS needs to occur, allowing the network to adapt to changes in the RF, information, and operational environments. The capacity to adapt is paramount to maintaining the required operational performance (throughput, latency, reliability and security). This thesis address the need for accurate radio channel modeling techniques to improve the performance of the wireless communication systems. Multiple different channel modeling techniques are considered including statistical models, ray tracing techniques, finite time-difference technique, transmission line matrix method (TLM), and stochastic differential equation-based (SDE) dynamic channel models. Measurement of ambient RF is performed at several harsh industrial environments to demonstrate the existence of uncertainty in channel behavior. Comparison of various techniques is performed with metrics including accuracy, applicability, and computational efficiency. SDE- and TLM-based methods are validated using indoor and outdoor measurements. Fast, accurate techniques for modeling multipath fading in harsh environments is explored. Application of dynamic channel models is explored for improving QoS of wireless communication system. The TLM-based models provide accurate site-specific path loss calculations taking into consideration materials and propagation characteristics of propagating environment. The validation studies confirm the technique is comparable with existing channel models. The TLM-based channel models is extended to compute the site-specific multipath characteristics of the radio channel eliminating the need for experimental measurement. The TLM-based simulator is also integrated with packet-level network simulator to perform end to end-to-end site specific calculation of wireless network performance. The SDE-channel models provide accurate online estimations of the channel performance along with accurate one-step prediction of the signal strength. The validation studies confirm the accuracy of the technique. Application of the SDE-based models for adaptive antenna control is formulated using online recursive estimation

SkinnySensor: Enabling Battery-Less Wearable Sensors Via Intrabody Power Transfer

Tremendousadvancement inultra-low powerelectronics and radiocommunica tionshas significantly contributed towards the fabrication of miniaturized biomedical sensors capable of capturing physiological data and transmitting them wirelessly. However, most of the wearable sensors require a battery for their operation. The battery serves as one of the critical bottlenecks to the development of novel wearable applications, as the limitations offered by batteries are affecting the development of new form-factors and longevity of wearable devices. In this work, we introduce a novel concept, namely Intra-Body Power Transfer (IBPT), to alleviate the limitations and problems associated with batteries, and enable wireless, batteryless wearable devices. The innovation of IBPT is to utilize the human body as the medium to transfer power to passive wearable devices, as opposed to employingon-boardbatteries for each individual device. The proposed platform eliminates the on-board rigid battery for ultra-low power and ultra-miniaturized sensors such that their form-factor can be flexible, ergonomically designed to be placed on small body parts. The platform also eliminates the need for battery maintenance (e.g., recharging or replacement) for multiple wearable devices other than the central power source. The performance of the developed system is tested and evaluated in comparison to traditional Radio Frequency based solutions that can be harmful to human interaction. The system developed is capable of harvesting on average 217µW at 0.43V and provides an average sleep/high impedance mode voltage of 4.5V

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN