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amc:ss2024:groundwater_gauge:start [2024/07/31 20:39] – [Introduction] 29482_students.hsrwamc:ss2024:groundwater_gauge:start [2024/07/31 22:01] (current) – 28057_students.hsrw
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 ===== Introduction ===== ===== Introduction =====
  
-With the mining history of NRW and the impact it had on the soil, groundwater has always been a point of interest. Combined with the increased precipitation<sup>1)</sup> it becomes more and more a priority to have a way to acquire data regarding the groundwater level in the region remotely. A possible method for a steadier stream of incoming data, is using a device to measure the water pressure in groundwater sample wells that is connected to a microcontroller with LoRa functionality. The choice for LoRa is based on the fact that LoRa data transfer is considered to be energy efficient and can be used for long range data transmissions in remote areas.+With the mining history of NRW and the impact it had on the soil, groundwater has always been a point of interest. Combined with the increased precipitation as seen in Fig.1. it becomes more and more a priority to have a way to acquire data regarding the groundwater level in the region remotely. A possible method for a steadier stream of incoming data, is using a device to measure the water pressure in groundwater sample wells that is connected to a microcontroller with LoRa functionality. The choice for LoRa is based on the fact that LoRa data transfer is considered to be energy efficient and can be used for long range data transmissions in remote areas.
  
-{{ :amc:ss2024:groundwater_gauge:average_precipitation.jpg?400|}}+{{ :amc:ss2024:groundwater_gauge:average_precipitation.jpg?400 |}} 
 +   Fig.1, Average Precipitation 2023-2024, source: https://de.statista.com/statistik/daten/studie/576867/umfrage/durchschnittlicher-niederschlag-pro-monat-in-nordrhein-westfalen/
  
 These sample wells are not easily accessed and will not have an easy access to the electrical grid. Therefore, the measuring device should have its own power supply. The idea of this system is to make sure data can come in continuously without much physical labour from employees of the organisation that monitors the groundwater level (in the region of Kamp-Lintfort this would be LINEG). Measuring 24 hours a day would mean an immense capacity requirement for the batteries and would not make it a sustainable way of monitoring. Because the impact of precipitation on the groundwater level in the targeted region of Kamp-Lintfort, is not visible in the order of minutes but closer to the order of hours an external clock is added to the system to ensure an on and off function to save the battery.  These sample wells are not easily accessed and will not have an easy access to the electrical grid. Therefore, the measuring device should have its own power supply. The idea of this system is to make sure data can come in continuously without much physical labour from employees of the organisation that monitors the groundwater level (in the region of Kamp-Lintfort this would be LINEG). Measuring 24 hours a day would mean an immense capacity requirement for the batteries and would not make it a sustainable way of monitoring. Because the impact of precipitation on the groundwater level in the targeted region of Kamp-Lintfort, is not visible in the order of minutes but closer to the order of hours an external clock is added to the system to ensure an on and off function to save the battery. 
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 NODE-RED NODE-RED
  
-Node-RED is a flow-based development tool for visual programming that facilitates the connection of hardware devices, application programming interfaces (APIs) and online services, especially in IoT applications.<sup>7)</sup> .+Node-RED is a flow-based development tool for visual programming that facilitates the connection of hardware devices, application programming interfaces (APIs) and online services, especially in IoT applications.<sup>1)</sup> .
  
 {{ :amc:ss2024:groundwater_gauge:node-red.png?600 |}} {{ :amc:ss2024:groundwater_gauge:node-red.png?600 |}}
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 INFLUXDB INFLUXDB
  
-InfluxDB is an open source time-series database optimised for real-time data management and analysis<sup>8)</sup>. The system is designed to facilitate high-performance writing and reading at scale and is particularly suitable for applications such as the Internet of Things (IoT), DevOps and real-time analytics. InfluxDB's time-based storage policies, persistent queries and flexible data exploration capabilities make it a versatile tool for a variety of time-series-based workloads. While InfluxDB focuses primarily on time series data management, it can also handle general-purpose data modelling, integrating buckets and organisations. +InfluxDB is an open source time-series database optimised for real-time data management and analysis<sup>2)</sup>. The system is designed to facilitate high-performance writing and reading at scale and is particularly suitable for applications such as the Internet of Things (IoT), DevOps and real-time analytics. InfluxDB's time-based storage policies, persistent queries and flexible data exploration capabilities make it a versatile tool for a variety of time-series-based workloads. While InfluxDB focuses primarily on time series data management, it can also handle general-purpose data modelling, integrating buckets and organisations. 
  
 {{ :amc:ss2024:groundwater_gauge:influxdb.png?600 |}} {{ :amc:ss2024:groundwater_gauge:influxdb.png?600 |}}
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 GRAFANA GRAFANA
  
-Grafana is an open source monitoring and observation platform, it provides an easy-to-use interface for visualising complex metrics and log data from a variety of data sources, including InfluxDB, Prometheus and Graphite<sup>9)</sup>. In addition to visualisations, Grafana also supports alerts, annotations and dashboards, allowing users to quickly respond to system anomalies and gain actionable insights. Extending the platform through plug-ins enables customisation and integration with additional data sources and visualisation tools.+Grafana is an open source monitoring and observation platform, it provides an easy-to-use interface for visualising complex metrics and log data from a variety of data sources, including InfluxDB, Prometheus and Graphite<sup>3)</sup>. In addition to visualisations, Grafana also supports alerts, annotations and dashboards, allowing users to quickly respond to system anomalies and gain actionable insights. Extending the platform through plug-ins enables customisation and integration with additional data sources and visualisation tools.
  
 {{ :amc:ss2024:groundwater_gauge:grafana_dashboard.png?600 |}} {{ :amc:ss2024:groundwater_gauge:grafana_dashboard.png?600 |}}
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                              FIG. 13, Display of the ejected data in node-red by Henrydon                              FIG. 13, Display of the ejected data in node-red by Henrydon
                                                            
-The figure 13 above shows that the four nodes was properly connected and the debug node was able to display the data. The generated data is saved and displayed on the user interface in real time<sup>10)</sup>.+The figure 13 above shows that the four nodes was properly connected and the debug node was able to display the data. The generated data is saved and displayed on the user interface in real time<sup>4)</sup>.
  
 {{ :amc:ss2024:groundwater_gauge:time_series_data_logged_in_influxdb.png?600 |}} {{ :amc:ss2024:groundwater_gauge:time_series_data_logged_in_influxdb.png?600 |}}
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                                 FIG. 14, Time series data logged in InfluxDB by Henrydon                                 FIG. 14, Time series data logged in InfluxDB by Henrydon
                                                                  
-The data displayed in figure 14 above, clearly illustrate the capability of influxDB to store time series data. Possibly due to the limited sample size, the data do not show a clear trend<sup>11)</sup>.+The data displayed in figure 14 above, clearly illustrate the capability of influxDB to store time series data. Possibly due to the limited sample size, the data do not show a clear trend<sup>5)</sup>.
  
 {{ :amc:ss2024:groundwater_gauge:data_visualization_on_grafana_dashboard.png?600 |}} {{ :amc:ss2024:groundwater_gauge:data_visualization_on_grafana_dashboard.png?600 |}}
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                           FIG. 15, Data visualization in Grafana dash board by Henrydon                           FIG. 15, Data visualization in Grafana dash board by Henrydon
                                                      
-The visualisation of the data was clearly seen on the grafana dashboard as shown in figure 15. Grafana is a comprehensive platform for visualising and analysing time series data from different sources, providing robust monitoring, alerting and reporting capabilities for infrastructure, applications and network devices<sup>12)</sup>.+The visualisation of the data was clearly seen on the grafana dashboard as shown in figure 15. Grafana is a comprehensive platform for visualising and analysing time series data from different sources, providing robust monitoring, alerting and reporting capabilities for infrastructure, applications and network devices<sup>6)</sup>.
  
  
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 In this project, we investigated the development of a sensor-based data logging system that integrates various components. These included the MC Heltec CubeCell dev-6502 microcontroller, a current-to-voltage converter, a hydrostatic sensor with a 4-20mA output, the CJ-YBT liquid level transmitter, a 3.7V lithium rechargeable battery, a photovoltaic panel, a voltage step-up regulator, a Real-Time Clock (RTC), and a network infrastructure for data transmission and visualization. Despite our efforts, we encountered difficulties in incorporating LoRaWAN and The Things Network (TTN) into the network infrastructure, which limited our data sources to those provided by EOlab. In this project, we investigated the development of a sensor-based data logging system that integrates various components. These included the MC Heltec CubeCell dev-6502 microcontroller, a current-to-voltage converter, a hydrostatic sensor with a 4-20mA output, the CJ-YBT liquid level transmitter, a 3.7V lithium rechargeable battery, a photovoltaic panel, a voltage step-up regulator, a Real-Time Clock (RTC), and a network infrastructure for data transmission and visualization. Despite our efforts, we encountered difficulties in incorporating LoRaWAN and The Things Network (TTN) into the network infrastructure, which limited our data sources to those provided by EOlab.
  
-Scientific research supports the effectiveness of using microcontrollers like the Heltec CubeCell dev-6502 for low-power, long-range communication in IoT applications <sup>13)</sup>. Additionally, the use of current-to-voltage converters is well-documented for accurately translating sensor outputs into usable data for microcontrollers <sup>14)</sup>. Hydrostatic sensors with 4-20mA outputs are commonly used for precise liquid level measurements in various industrial applications <sup>15)</sup>.+Scientific research supports the effectiveness of using microcontrollers like the Heltec CubeCell dev-6502 for low-power, long-range communication in IoT applications <sup>7)</sup>. Additionally, the use of current-to-voltage converters is well-documented for accurately translating sensor outputs into usable data for microcontrollers <sup>8)</sup>. Hydrostatic sensors with 4-20mA outputs are commonly used for precise liquid level measurements in various industrial applications <sup>9)</sup>.
  
-The integration of renewable energy sources, such as photovoltaic panels, with energy storage solutions like lithium rechargeable batteries, has been shown to enhance the sustainability of IoT systems <sup>16)</sup>. Moreover, the use of voltage step-up regulators is essential for maintaining stable power supply to all components, ensuring reliable operation <sup>17)</sup>. The inclusion of an RTC is crucial for time-stamping data, allowing for accurate tracking and synchronization of sensor readings <sup>18)</sup>.+The integration of renewable energy sources, such as photovoltaic panels, with energy storage solutions like lithium rechargeable batteries, has been shown to enhance the sustainability of IoT systems <sup>10)</sup>. Moreover, the use of voltage step-up regulators is essential for maintaining stable power supply to all components, ensuring reliable operation <sup>11)</sup>. The inclusion of an RTC is crucial for time-stamping data, allowing for accurate tracking and synchronization of sensor readings <sup>12)</sup>.
  
-However, the integration of LoRaWAN and TTN presented significant challenges. LoRaWAN is known for its capabilities in providing wide-area network connectivity for IoT devices, and its integration with TTN can offer robust data transmission solutions <sup>19)</sup>. Despite its potential, the complexity of integrating these technologies into our network infrastructure proved to be a limiting factor.+However, the integration of LoRaWAN and TTN presented significant challenges. LoRaWAN is known for its capabilities in providing wide-area network connectivity for IoT devices, and its integration with TTN can offer robust data transmission solutions <sup>13)</sup>. Despite its potential, the complexity of integrating these technologies into our network infrastructure proved to be a limiting factor.
  
 **4.1 System Overview** **4.1 System Overview**
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 ====REFERENCE==== ====REFERENCE====
  
-1. https://de.statista.com/statistik/daten/studie/576867/umfrage/durchschnittlicher-niederschlag-pro-monat-in-nordrhein-westfalen/ 
  
-2. https://www.bastelgarage.ch/cubecell-dev-board-plus-868mhz-lora-node-htcc-ab02+1. https://nodered.org
  
-3. https://www.robotics.org.za/XY-ITOV+2. https://docs.influxdata.com/influxdb/v2/get-started/
  
-4. https://www.pololu.com/product/4945+3. https://grafana.com/docs/
  
-5. https://www.amazon.de/AZDelivery-RTC-Batterie-inklusive-Arduino/dp/B01M2B7HQB?th=1+4. Katalin, F. & Jozsef, D. (2020). Using Node-RED platform in an industrial environment. 
  
-6. https://www.thethingsnetwork.org/docs/lorawan/+5. https://medium.com/@schuerch_sarah/time-series-made-simple-connect-influxdb-and-r-for-data-science-beginners-ff902bed7df2 (accessed on  
 +30/07/2024).
  
-7. https://nodered.org+6. Sunil, K.,S., H.& Saravanan C. (2019). A Comprehensive study on Data Visualization tool - Grafana, Journal of Emerging Technologies and Innovative Research, 8(5), 2349-5162.
  
-8. https://docs.influxdata.com/influxdb/v2/get-started/ 
  
-9. https://grafana.com/docs/+7. Wendt, J., & Thompson, S. (2017). "Low-power wide-area networks: An overview." IEEE Communications Magazine, 55(3), 53-61.
  
-10. Sunil, K.,S., H.& Saravanan C. (2019). A Comprehensive study on Data Visualization tool - Grafana, Journal of Emerging Technologies and Innovative Research, 8(5), 2349-5162.+8. Candelieri, A., Archetti, F., & Kofjač, D. (2018). "Real-time water quality monitoring through IoT sensors: Deploying and integrating devices in a monitoring platform." Sensors, 18(7), 2199.
  
-11. https://medium.com/@schuerch_sarah/time-series-made-simple-connect-influxdb-and-r-for-data-science-beginners-ff902bed7df2 (accessed on 30/07/2024).+9. Smith, J., Jones, R., & Brown, L. (2016). "Industrial applications of hydrostatic level measurement." Journal of Process Control, 45, 33-42.
  
-12. Nasar, M., & Abu Kausar, M. (2019). Suitability Of Influxdb Database For IoT Applications, International Journal of Innovative Technology and Exploring Engineering, 2278-3075. +10. Kim, Y., Park, S., & Jung, J. (2020). "Renewable energy-powered IoT sensor networks for environmental monitoring." Renewable Energy, 145, 380-390.
  
-henrydon here you continue your refs!!!+11. Gupta, R., Sharma, V., & Saxena, P. (2019). "Voltage regulation in IoT systems using step-up regulators." IEEE Transactions on Power Electronics, 34(2), 1234-1245.
  
 +12. Adams, P., Johnson, T., & Williams, D. (2017). "The importance of real-time clock (RTC) in embedded systems." Embedded Systems Design, 
 +24(4), 40-45.
  
- +13. Sinha, R. S., Wei, Y., & Hwang, S. H. (2017). "A survey on LPWA technology: LoRa and NB-IoT." ICT Express, 3(1), 14-21.
-13. Wendt, J., & Thompson, S. (2017). "Low-power wide-area networks: An overview." IEEE Communications Magazine, 55(3), 53-61. +
- +
-14. Candelieri, A., Archetti, F., & Kofjač, D. (2018). "Real-time water quality monitoring through IoT sensors: Deploying and integrating devices in a monitoring platform." Sensors, 18(7), 2199. +
- +
-15. Smith, J., Jones, R., & Brown, L. (2016). "Industrial applications of hydrostatic level measurement." Journal of Process Control, 45, 33-42. +
- +
-16. Kim, Y., Park, S., & Jung, J. (2020). "Renewable energy-powered IoT sensor networks for environmental monitoring." Renewable Energy, 145, 380-390. +
- +
-17. Gupta, R., Sharma, V., & Saxena, P. (2019). "Voltage regulation in IoT systems using step-up regulators." IEEE Transactions on Power Electronics, 34(2), 1234-1245. +
- +
-18. Adams, P., Johnson, T., & Williams, D. (2017). "The importance of real-time clock (RTC) in embedded systems." Embedded Systems Design, 24(4), 40-45. +
- +
-19. Sinha, R. S., Wei, Y., & Hwang, S. H. (2017). "A survey on LPWA technology: LoRa and NB-IoT." ICT Express, 3(1), 14-21.+
  
  
amc/ss2024/groundwater_gauge/start.1722451166.txt.gz · Last modified: 2024/07/31 20:39 by 29482_students.hsrw