2,759 research outputs found

    In-Network Distributed Solar Current Prediction

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    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

    Using Wireless Sensor Networks for Precision Irrigation Scheduling

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    Integrated approach of remote sensing and micro-sensor technology for estimating evapotranspiration in Cyprus

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     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.   &nbsp

    A Review of the Enviro-Net Project

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    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

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    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 “upscaled” 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

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    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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