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

Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table

Alpine sensor network system for high-resolution spatial snow and runoff estimation

Monitoring the snowpack is crucial for water management, flood control and hydropower optimization. Traditional regression methods often result in low accuracy runoff predictions.Existing ground-based real-time measurement systems are in majority installed at low elevations with poor physiographic representation. This thesis presents a system for better Snow Water Equivalent (SWE) and runoff estimation. The autonomous end-to-end Wireless Sensor Network (WSN) that leverages the Internet of Things (IoT) technology provides mountain hydrology measurements in near real-time. At its core lies an ultra-low power, radio channel-hoping, and self-organizing mesh secured with a rugged weather-sealed design, data replication and remote network health monitoring. Three WSNs are installed throughout the North Fork of the Feather River in Northern California upstream of the Oroville dam. Elevation, aspect, slope and vegetation determine network locations. Data show considerable spatial variability of snow depth, and that existing operational autonomous systems are non-representative spatially, with biases reaching up to 50%. Combined with existing systems, WSNs better detect precipitation timing and phase, monitor sub-daily dynamics of infiltration and surface runoff, and inform hydro power managers about actual ablation and end-of-season date across the landscape. A wet and dry year exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Elastic Net regression shows that dominant features at the sub-km2 scale are site-dependent and differ from the watershed scale. Based on the Nearest Neighbor (NN) with a Landsat assimilated historical product, explanatory variables consistently explain up to 90% of the variance in the watershed-scale SWE for both years. Lagged cross correlation of snowmelt with stream flow measurements show improvement of up to 100% compared to existing systems. Ensemble Optimal Interpolation (EnOI) update of background SWE fields from Landsat and LiDAR products provide accurate high resolution estimates of spatial SWE for areas with parsimonious sensors. Results show a minimum RMSE of 22% and 30% at 90 m and 50 m resolutions respectively. Compared with SNODAS, reduction in error is up to 55% and 80%, with LiDAR as reference

Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b)

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems