A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms
Harmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifyin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/5/942 |
| _version_ | 1797264680020869120 |
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| author | Jesús Chacón Giordy A. Andrade Jose L. Risco-Martín Segundo Esteban |
| author_facet | Jesús Chacón Giordy A. Andrade Jose L. Risco-Martín Segundo Esteban |
| author_sort | Jesús Chacón |
| collection | DOAJ |
| description | Harmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifying hazards and preventing or at least minimizing their impact. Traditional monitoring approaches often fail to provide the real-time, high-resolution data needed for effective early warnings. The integration of Internet of Things (IoT) technologies offers a promising avenue to address these limitations by creating a network of interconnected sensors capable of continuously collecting and transmitting data from various aquatic environments. In this paper, we present DEVS-BLOOM-WebUI, an advanced web application that extends the capabilities of the DEVS-BLOOM framework, providing a user-friendly interface that supports different user roles. The application includes an interface to manage users and permissions, dashboards to inspect data (from sensors, Unmanned Surface Vehicles (USVs), weather stations, etc.), train AI models, explore their predictions, and facilitate decision-making through notification of early warnings. A key feature of DEVS-BLOOM-WebUI is the Scenario Configuration Editor (SCE). This interactive tool allows for users to design and configure the deployment of monitoring infrastructure within a water body, enhancing the system’s adaptability to user-defined simulation scenarios. This paper also investigates the practical implementation of an IoT-based EWS, discussing design considerations, sensor technologies, and communication protocols essential for seamless data integration and effective operation of the EWS. |
| first_indexed | 2024-04-25T00:32:45Z |
| format | Article |
| id | doaj.art-e8b1ebe5daa74eb8925ab43480955af6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-25T00:32:45Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e8b1ebe5daa74eb8925ab43480955af62024-03-12T16:42:42ZengMDPI AGElectronics2079-92922024-02-0113594210.3390/electronics13050942A Bleeding Edge Web Application for Early Detection of Cyanobacterial BloomsJesús Chacón0Giordy A. Andrade1Jose L. Risco-Martín2Segundo Esteban3Department of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, SpainHarmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifying hazards and preventing or at least minimizing their impact. Traditional monitoring approaches often fail to provide the real-time, high-resolution data needed for effective early warnings. The integration of Internet of Things (IoT) technologies offers a promising avenue to address these limitations by creating a network of interconnected sensors capable of continuously collecting and transmitting data from various aquatic environments. In this paper, we present DEVS-BLOOM-WebUI, an advanced web application that extends the capabilities of the DEVS-BLOOM framework, providing a user-friendly interface that supports different user roles. The application includes an interface to manage users and permissions, dashboards to inspect data (from sensors, Unmanned Surface Vehicles (USVs), weather stations, etc.), train AI models, explore their predictions, and facilitate decision-making through notification of early warnings. A key feature of DEVS-BLOOM-WebUI is the Scenario Configuration Editor (SCE). This interactive tool allows for users to design and configure the deployment of monitoring infrastructure within a water body, enhancing the system’s adaptability to user-defined simulation scenarios. This paper also investigates the practical implementation of an IoT-based EWS, discussing design considerations, sensor technologies, and communication protocols essential for seamless data integration and effective operation of the EWS.https://www.mdpi.com/2079-9292/13/5/942web applicationInternet of ThingsHarmful Algal and Cyanobacterial Bloomautomated measurementearly warning system |
| spellingShingle | Jesús Chacón Giordy A. Andrade Jose L. Risco-Martín Segundo Esteban A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms Electronics web application Internet of Things Harmful Algal and Cyanobacterial Bloom automated measurement early warning system |
| title | A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms |
| title_full | A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms |
| title_fullStr | A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms |
| title_full_unstemmed | A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms |
| title_short | A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms |
| title_sort | bleeding edge web application for early detection of cyanobacterial blooms |
| topic | web application Internet of Things Harmful Algal and Cyanobacterial Bloom automated measurement early warning system |
| url | https://www.mdpi.com/2079-9292/13/5/942 |
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