2,759 research outputs found
In-Network Distributed Solar Current Prediction
Long-term sensor network deployments demand careful power management. While
managing power requires understanding the amount of energy harvestable from the
local environment, current solar prediction methods rely only on recent local
history, which makes them susceptible to high variability. In this paper, we
present a model and algorithms for distributed solar current prediction, based
on multiple linear regression to predict future solar current based on local,
in-situ climatic and solar measurements. These algorithms leverage spatial
information from neighbors and adapt to the changing local conditions not
captured by global climatic information. We implement these algorithms on our
Fleck platform and run a 7-week-long experiment validating our work. In
analyzing our results from this experiment, we determined that computing our
model requires an increased energy expenditure of 4.5mJ over simpler models (on
the order of 10^{-7}% of the harvested energy) to gain a prediction improvement
of 39.7%.Comment: 28 pages, accepted at TOSN and awaiting publicatio
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Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products
We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of â10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20â25 cm to 10â15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions
Integrated approach of remote sensing and micro-sensor technology for estimating evapotranspiration in Cyprus
Papadavid George1,2, Hadjimitsis Diofantos1(1. Cyprus University of Technology, Cyprus; 2. Agricultural Research Institute, Cyprus) Abstract: The objective of this research project is to describe and apply a procedure for monitoring and improving the performance of on-demand irrigation networks, based on the integration of remote sensing techniques and simulation modeling of irrigation water in Cyprus, which is facing a severe drought in the last five years. Multi-spectral satellite images are used to infer crop potential evapotranspiration, which is the main input for water balance simulations. The need for estimating ET in Cyprus is imposed in order to determine the exact quantity of irrigated water needed for each specific crop. The overuse of water for irrigation has resulted in eliminating the water resources in the whole island. The determination of ET for irrigation purposes will be used as a vital tool for supporting the decision-making process in the management of water resources, on a technocratic level, and on the other hand will have a positive effect on the rest of water resources of Cyprus. The integrated method applied, consisting of Remote Sensing techniques and micro-sensor technology, has shown that it can be a useful tool in the hands of agri-policy makers for sustainable irrigation.Keywords: remote sensing, wireless sensors, irrigation management, sustainability Citation: Papadavid George, Hadjimitsis Diofantos. Integrated approach of remote sensing and micro-sensor technology for estimating evapotranspiration in Cyprus. Agric Eng Int: CIGR Journal, 2010, 12(3): 1-11.  
A Review of the Enviro-Net Project
Ecosystems monitoring is essential to properly understand their development
and the effects of events, both climatological and anthropological in nature.
The amount of data used in these assessments is increasing at very high rates.
This is due to increasing availability of sensing systems and the development
of new techniques to analyze sensor data. The Enviro-Net Project encompasses
several of such sensor system deployments across five countries in the
Americas. These deployments use a few different ground-based sensor systems,
installed at different heights monitoring the conditions in tropical dry
forests over long periods of time. This paper presents our experience in
deploying and maintaining these systems, retrieving and pre-processing the
data, and describes the Web portal developed to help with data management,
visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers'
comments v1: 38 pages, 8 figure
A Method for Upscaling In Situ Soil Moisture Measurements to Satellite Footprint Scale Using Random Forests
Geophysical products generated from remotely sensed data require validation to evaluate their accuracy. Typically in situ measurements are used for validation, as is the case for satellite-derived soil moisture products. However, a large disparity in scales often exists between in situ measurements (covering meters to 10 s of meters) and satellite footprints (often hundreds of meters to several kilometers), making direct comparison difficult. Before using in situ measurements for validation, they must be âupscaledâ to provide the mean soil moisture within the satellite footprint. There are a number of existing upscaling methods previously applied to soil moisture measurements, but many place strict requirements on the number and spatial distribution of soil moisture sensors difficult to achieve with permanent/semipermanent ground networks necessary for long-term validation efforts. A new method for upscaling is presented here, using Random Forests to fit a model between in situ measurements and a number of landscape parameters and variables impacting the spatial and temporal distributions of soil moisture. The method is specifically intended for validation of the NASA soil moisture active passive (SMAP) products at 36-, 9-, and 3-km scales. The method was applied to in situ data from the SoilSCAPE network in California, validated with data from the SMAPVEX12 campaign in Manitoba, Canada with additional verification from the TxSON network in Texas. For the SMAPVEX12 site, the proposed method was compared to extensive field measurements and was able to predict mean soil moisture over a large area more accurately than other upscaling approaches
ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems
This manuscript presents a novel state-of-the-art cyber-physical water
testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA).
ACWA is motivated by the need to advance water supply management using AI and
Cybersecurity experimentation. The main goal of ACWA is to address pressing
challenges in the water and agricultural domains by utilising cutting-edge AI
and data-driven technologies. These challenges include Cyberbiosecurity,
resources management, access to water, sustainability, and data-driven
decision-making, among others. To address such issues, ACWA consists of
multiple topologies, sensors, computational nodes, pumps, tanks, smart water
devices, as well as databases and AI models that control the system. Moreover,
we present ACWA simulator, which is a software-based water digital twin. The
simulator runs on fluid and constituent transport principles that produce
theoretical time series of a water distribution system. This creates a good
validation point for comparing the theoretical approach with real-life results
via the physical ACWA testbed. ACWA data are available to AI and water domain
researchers and are hosted in an online public repository. In this paper, the
system is introduced in detail and compared with existing water testbeds;
additionally, example use-cases are described along with novel outcomes such as
datasets, software, and AI-related scenarios
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