Basin-Scale, Real-Time Salinity Management Using Telemetered Sensor Networks and Model-Based Salt Assimilative Capacity Forecasts

Author(s):  
Nigel W.T. Quinn ◽  
Roberta Tassey ◽  
Jun Wang

This chapter describes a new approach to environmental decision support for salinity management in the San Joaquin Basin that focuses on Web-based data sharing using tools such as YSI Econet and continuous data quality management using an enterprise-level software tool WISKI. These tools offer real-time Web-access to sensor data as well as providing the owner full control over the way the data is visualized. The same websites use GIS to superimpose the monitoring site locations on maps of local hydrography and allow point and click access to the data collected at each environmental monitoring site. This information technology suite of software and hardware work together with a watershed simulation model WARMF-SJR to provide timely, reliable, and high quality data and forecasts of river salinity that can used by stakeholder decision makers to ensure compliance with state water quality objectives.

Author(s):  
Nigel W.T. Quinn ◽  
Roberta Tassey ◽  
Jun Wang

This chapter describes a new approach to environmental decision support for salinity management in the San Joaquin Basin that focuses on Web-based data sharing using tools such as YSI Econet and continuous data quality management using an enterprise-level software tool WISKI. These tools offer real-time Web-access to sensor data as well as providing the owner full control over the way the data is visualized. The same websites use GIS to superimpose the monitoring site locations on maps of local hydrography and allow point and click access to the data collected at each environmental monitoring site. This information technology suite of software and hardware work together with a watershed simulation model WARMF-SJR to provide timely, reliable, and high quality data and forecasts of river salinity that can used by stakeholder decision makers to ensure compliance with state water quality objectives.


AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 65 ◽  
Author(s):  
Clint R. Bidlack ◽  
Michael P Wellman

Recent advances in enterprise web-based software have created a need for sophisticated yet user-friendly data quality solutions. A new category of data quality solutions are discussed that fill this need using intelligent matching and retrieval algorithms. Solutions are focused on customer and sales data and include real-time inexact search, batch processing, and data migration. Users are empowered to maintain higher quality data resulting in more efficient sales and marketing operations. Sales managers spend more time with customers and less time managing data.


Author(s):  
D. Hein ◽  
S. Bayer ◽  
R. Berger ◽  
T. Kraft ◽  
D. Lesmeister

Natural disasters as well as major man made incidents are an increasingly serious threat for civil society. Effective, fast and coordinated disaster management crucially depends on the availability of a real-time situation picture of the affected area. However, in situ situation assessment from the ground is usually time-consuming and of limited effect, especially when dealing with large or inaccessible areas. A rapid mapping system based on aerial images can enable fast and effective assessment and analysis of medium to large scale disaster situations. This paper presents an integrated rapid mapping system that is particularly designed for real-time applications, where comparatively large areas have to be recorded in short time. The system includes a lightweight camera system suitable for UAV applications and a software tool for generating aerial maps from recorded sensor data within minutes after landing. The paper describes in particular which sensors are applied and how they are operated. Furthermore it outlines the procedure, how the aerial map is generated from image and additional gathered sensor data.


Author(s):  
Bo Chen ◽  
Wenjia Liu ◽  
Jinjiang Wang ◽  
Justin Slepak

This paper presents a Web-based data inquiry and real-time control of sensor’s operating mode for structural health monitoring sensor networks. The main objective of the presented system is to provide a Web interface for real-time sensor data visualization, sensor-level damage diagnosis, and control of sensor’s operating mode. Web services are available both on distributed sensor nodes and a data repository machine. Users can request Web pages hosted on the sensor nodes or the data repository machine by specifying corresponding sensor IDs. The ability of directly accessing data on sensor nodes via internet allows users to monitor a structure’s performance in a timely manner. The damage diagnosis algorithms implemented on the sensor nodes help users to assess the structural health conditions without the need of transmitting sensor data to a central data station. The presented system also provides the capability of dynamically changing sensor’s operating mode through the Web interface. This feature greatly enhances the flexibility of the system to accommodate different sensing needs and achieve a long lifespan. The system has been tested in the Laboratory to validate its capabilities.


2021 ◽  
Vol 10 (3) ◽  
pp. 1669-1677
Author(s):  
Prisma Megantoro ◽  
Brahmantya Aji Pramudita ◽  
P. Vigneshwaran ◽  
Abdufattah Yurianta ◽  
Hendra Ari Winarno

This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.


Aerospace ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 64
Author(s):  
Sarah Malik ◽  
Rakeen Rouf ◽  
Krzysztof Mazur ◽  
Antonios Kontsos

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.


2018 ◽  
Author(s):  
Yuanyuan Huang ◽  
Mark Stacy ◽  
Jiang Jiang ◽  
Nilutpal Sundi ◽  
Shuang Ma ◽  
...  

Abstract. Predicting future changes in ecosystem services is not only highly desirable but also becomes feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyberinfrastructure) are converging to transform ecological research to quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD) into models. EcoPAD is a web-based software system that automates data transfer and processes from sensor networks to ecological forecasting through data management, model simulation, data assimilation, and visualization. It facilitates interactive data-model integration from which model is recursively improved through updated data while data is systematically refined under the guidance of model. EcoPAD relies on data from observations, process-oriented models, DA techniques, and web-based workflow. We applied EcoPAD to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment at North Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into Terrestrial ECOsystem (TECO) model, and recursively forecast responses of various biophysical and biogeochemical processes to five temperature and two CO2 treatments in near real-time (weekly). The near real-time forecasting with EcoPAD-SPRUCE has revealed that uncertainties or mismatches in forecasting carbon pool dynamics are more related to model (e.g., model structure, parameter, and initial value) than forcing variables, opposite to forecasting flux variables. EcoPAD-SPRUCE quantified acclimations of methane production in response to warming treatments through shifted posterior distributions of the CH4:CO2 ratio and temperature sensitivity (Q10) of methane production towards lower values. Different case studies indicated that realistic forecasting of carbon dynamics relies on appropriate model structure, correct parameterization and accurate external forcing. Moreover, EcoPAD-SPRUCE stimulated active feedbacks between experimenters and modelers so as to identify model components to be improved and additional measurements to be made. It becomes the first interactive model-experiment (ModEx) system and opens a novel avenue for interactive dialogue between modelers and experimenters. EcoPAD also has the potential to become an interactive tool for resource management, to stimulate citizen science in ecology, and transform environmental education with its easily accessible web interface.


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