Maria E. Presa-Reyes, B. Bogosian, Bradley Schonhoff, Christopher Jerauld, Christian Moreyra, P. Gardinali, Shu‐Ching Chen
{"title":"近实时浮标传感器水质研究平台","authors":"Maria E. Presa-Reyes, B. Bogosian, Bradley Schonhoff, Christopher Jerauld, Christian Moreyra, P. Gardinali, Shu‐Ching Chen","doi":"10.1109/IRI49571.2020.00048","DOIUrl":null,"url":null,"abstract":"Maintaining environmental sustainability relies on continuously monitoring environmental conditions. Water is an environmental component essential to the survival of all living organisms; hence, to prevent contamination and ensure proper water treatment, persistent observations and measurements of water quality are crucial. Traditionally, the procedure for testing the quality of water involved traveling to designated testing sites, manually collecting surface samples, transporting said samples to a laboratory for analysis, analyzing chemicals and microbial contaminants, and publishing the findings with the community. The technological advances in wireless sensor networks bring forth the opportunity for remote measurement and monitoring of water samples. Not only is the presence of the scientist no longer mandatory on the testing site, but the data can also be automatically collected, visualized, monitored, and shared through sensor recordings. These transitions exhibit a much fine-grained level of spatio-temporal information collection and allow for more comprehensive and long-term studies. Three research buoys, designed to be deployed in both shallow freshwater ecosystems and near-shore marine environments, were launched in different locations of South Florida to tackle complex challenges of environmental contamination. The research presented here designs and deploys a water quality monitoring platform for allowing the scientists to analyze better the near-real-time data collected by the buoys and generate insights. We further demonstrate two engaging near-real-time visualization methods developed to disseminate data trends and findings to a wide range of audiences from diverse backgrounds.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"18 1","pages":"287-294"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Water Quality Research Platform for the Near-real-time Buoy Sensor Data\",\"authors\":\"Maria E. Presa-Reyes, B. Bogosian, Bradley Schonhoff, Christopher Jerauld, Christian Moreyra, P. 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Not only is the presence of the scientist no longer mandatory on the testing site, but the data can also be automatically collected, visualized, monitored, and shared through sensor recordings. These transitions exhibit a much fine-grained level of spatio-temporal information collection and allow for more comprehensive and long-term studies. Three research buoys, designed to be deployed in both shallow freshwater ecosystems and near-shore marine environments, were launched in different locations of South Florida to tackle complex challenges of environmental contamination. The research presented here designs and deploys a water quality monitoring platform for allowing the scientists to analyze better the near-real-time data collected by the buoys and generate insights. We further demonstrate two engaging near-real-time visualization methods developed to disseminate data trends and findings to a wide range of audiences from diverse backgrounds.\",\"PeriodicalId\":93159,\"journal\":{\"name\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"volume\":\"18 1\",\"pages\":\"287-294\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. 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A Water Quality Research Platform for the Near-real-time Buoy Sensor Data
Maintaining environmental sustainability relies on continuously monitoring environmental conditions. Water is an environmental component essential to the survival of all living organisms; hence, to prevent contamination and ensure proper water treatment, persistent observations and measurements of water quality are crucial. Traditionally, the procedure for testing the quality of water involved traveling to designated testing sites, manually collecting surface samples, transporting said samples to a laboratory for analysis, analyzing chemicals and microbial contaminants, and publishing the findings with the community. The technological advances in wireless sensor networks bring forth the opportunity for remote measurement and monitoring of water samples. Not only is the presence of the scientist no longer mandatory on the testing site, but the data can also be automatically collected, visualized, monitored, and shared through sensor recordings. These transitions exhibit a much fine-grained level of spatio-temporal information collection and allow for more comprehensive and long-term studies. Three research buoys, designed to be deployed in both shallow freshwater ecosystems and near-shore marine environments, were launched in different locations of South Florida to tackle complex challenges of environmental contamination. The research presented here designs and deploys a water quality monitoring platform for allowing the scientists to analyze better the near-real-time data collected by the buoys and generate insights. We further demonstrate two engaging near-real-time visualization methods developed to disseminate data trends and findings to a wide range of audiences from diverse backgrounds.