Low-Cost Automatic Weather Stations in the Internet of Things
Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Thin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/4/146 |
| _version_ | 1797539626148167680 |
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| author | Konstantinos Ioannou Dimitris Karampatzakis Petros Amanatidis Vasileios Aggelopoulos Ilias Karmiris |
| author_facet | Konstantinos Ioannou Dimitris Karampatzakis Petros Amanatidis Vasileios Aggelopoulos Ilias Karmiris |
| author_sort | Konstantinos Ioannou |
| collection | DOAJ |
| description | Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally. |
| first_indexed | 2024-03-10T12:48:31Z |
| format | Article |
| id | doaj.art-8eef631e22f7457fbdeb3a72bac42c74 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T12:48:31Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-8eef631e22f7457fbdeb3a72bac42c742023-11-21T13:16:02ZengMDPI AGInformation2078-24892021-03-0112414610.3390/info12040146Low-Cost Automatic Weather Stations in the Internet of ThingsKonstantinos Ioannou0Dimitris Karampatzakis1Petros Amanatidis2Vasileios Aggelopoulos3Ilias Karmiris4Hellenic Agricultural Organization “DEMETER”, Forest Research Institute, Vasilika, 57006 Thessaloniki, GreeceIndustrial and Educational Embedded Systems Lab, Department of Computer Science, International Hellenic University, 65403 Kavala, GreeceIndustrial and Educational Embedded Systems Lab, Department of Computer Science, International Hellenic University, 65403 Kavala, GreeceIndustrial and Educational Embedded Systems Lab, Department of Computer Science, International Hellenic University, 65403 Kavala, GreeceHellenic Agricultural Organization “DEMETER”, Forest Research Institute, Vasilika, 57006 Thessaloniki, GreeceAutomatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally.https://www.mdpi.com/2078-2489/12/4/146AWSInternet of ThingsArtificial IntelligenceEdge computingLPWANLoRa |
| spellingShingle | Konstantinos Ioannou Dimitris Karampatzakis Petros Amanatidis Vasileios Aggelopoulos Ilias Karmiris Low-Cost Automatic Weather Stations in the Internet of Things Information AWS Internet of Things Artificial Intelligence Edge computing LPWAN LoRa |
| title | Low-Cost Automatic Weather Stations in the Internet of Things |
| title_full | Low-Cost Automatic Weather Stations in the Internet of Things |
| title_fullStr | Low-Cost Automatic Weather Stations in the Internet of Things |
| title_full_unstemmed | Low-Cost Automatic Weather Stations in the Internet of Things |
| title_short | Low-Cost Automatic Weather Stations in the Internet of Things |
| title_sort | low cost automatic weather stations in the internet of things |
| topic | AWS Internet of Things Artificial Intelligence Edge computing LPWAN LoRa |
| url | https://www.mdpi.com/2078-2489/12/4/146 |
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