Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System
High cost, long-range communication, and anomaly detection issues are associated with IoT systems in water quality monitoring. Therefore, this work proposes a prototype for a water quality monitoring system (IoT-WQMS) based on IoT technologies, which include in the system architecture a LoRa repeate...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/15/7/1351 |
| _version_ | 1797606833620254720 |
|---|---|
| author | Armando Daniel Blanco Jáquez María T. Alarcon Herrera Ana Elizabeth Marín Celestino Efraín Neri Ramírez Diego Armando Martínez Cruz |
| author_facet | Armando Daniel Blanco Jáquez María T. Alarcon Herrera Ana Elizabeth Marín Celestino Efraín Neri Ramírez Diego Armando Martínez Cruz |
| author_sort | Armando Daniel Blanco Jáquez |
| collection | DOAJ |
| description | High cost, long-range communication, and anomaly detection issues are associated with IoT systems in water quality monitoring. Therefore, this work proposes a prototype for a water quality monitoring system (IoT-WQMS) based on IoT technologies, which include in the system architecture a LoRa repeater and an anomaly detection algorithm. The system performs the data collection, data storage, anomaly detection, and alarm sending remotely and in real-time for the information to be captured by the multisensor node. The LoRa repeater allowed the spatial coverage of the LoRa communication to extend, making it possible to reach a place where originally there was no coverage with a single LoRa transmitter due to topography and line of sight. The prototype performed well in terms of packet loss rate, transmission time, and sensitivity, extending the long-range wireless communication distance. Indoor multinode testing validation for 29 days of the mean absolute error for average relative errors of water temperature, pH, turbidity, and total dissolved solids (TDS) were 0.65%, 0.30%, and 14.33%, respectively. The anomaly detector identified all erroneous data events due to node sensor recalibration and water recirculation pump failures. The IoT-WQMS increased the reliability of monitoring through the timely identification of any sensor malfunctions and extended the LoRa signal range, which are relevant features in the scope of in situ and real-time water quality monitoring. |
| first_indexed | 2024-03-11T05:21:33Z |
| format | Article |
| id | doaj.art-1c06a4aa205941f6b2f79854e17f5586 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T05:21:33Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-1c06a4aa205941f6b2f79854e17f55862023-11-17T17:50:17ZengMDPI AGWater2073-44412023-04-01157135110.3390/w15071351Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring SystemArmando Daniel Blanco Jáquez0María T. Alarcon Herrera1Ana Elizabeth Marín Celestino2Efraín Neri Ramírez3Diego Armando Martínez Cruz4Centro de Investigación en Materiales Avanzados, Departamento de Ingeniería Sustentable, Calle CIMAV 110, Ejido Arroyo Seco, Durango 34147, MexicoCentro de Investigación en Materiales Avanzados, Departamento de Ingeniería Sustentable, Calle CIMAV 110, Ejido Arroyo Seco, Durango 34147, MexicoCONACYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, Camino a la Presa San José 2055, Col. Lomas 4ta Sección, San Luis Potosí 78216, MexicoFacultad de Ingeniería y Ciencias, Universidad Autónoma de Tamaulipas (UAT), Centro Universitario Victoria, Adolfo López Mateos S/N, Ciudad Victoria 87120, MexicoCONACYT-Centro de Investigación en Materiales Avanzados, S.C. Calle CIMAV 110, Ejido Arroyo Seco, Col. 15 de Mayo, Durango 34147, MexicoHigh cost, long-range communication, and anomaly detection issues are associated with IoT systems in water quality monitoring. Therefore, this work proposes a prototype for a water quality monitoring system (IoT-WQMS) based on IoT technologies, which include in the system architecture a LoRa repeater and an anomaly detection algorithm. The system performs the data collection, data storage, anomaly detection, and alarm sending remotely and in real-time for the information to be captured by the multisensor node. The LoRa repeater allowed the spatial coverage of the LoRa communication to extend, making it possible to reach a place where originally there was no coverage with a single LoRa transmitter due to topography and line of sight. The prototype performed well in terms of packet loss rate, transmission time, and sensitivity, extending the long-range wireless communication distance. Indoor multinode testing validation for 29 days of the mean absolute error for average relative errors of water temperature, pH, turbidity, and total dissolved solids (TDS) were 0.65%, 0.30%, and 14.33%, respectively. The anomaly detector identified all erroneous data events due to node sensor recalibration and water recirculation pump failures. The IoT-WQMS increased the reliability of monitoring through the timely identification of any sensor malfunctions and extended the LoRa signal range, which are relevant features in the scope of in situ and real-time water quality monitoring.https://www.mdpi.com/2073-4441/15/7/1351LoRaanomaly detectionwater qualityIoTreal-time monitoring |
| spellingShingle | Armando Daniel Blanco Jáquez María T. Alarcon Herrera Ana Elizabeth Marín Celestino Efraín Neri Ramírez Diego Armando Martínez Cruz Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System Water LoRa anomaly detection water quality IoT real-time monitoring |
| title | Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System |
| title_full | Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System |
| title_fullStr | Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System |
| title_full_unstemmed | Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System |
| title_short | Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System |
| title_sort | extension of lora coverage and integration of an unsupervised anomaly detection algorithm in an iot water quality monitoring system |
| topic | LoRa anomaly detection water quality IoT real-time monitoring |
| url | https://www.mdpi.com/2073-4441/15/7/1351 |
| work_keys_str_mv | AT armandodanielblancojaquez extensionofloracoverageandintegrationofanunsupervisedanomalydetectionalgorithminaniotwaterqualitymonitoringsystem AT mariatalarconherrera extensionofloracoverageandintegrationofanunsupervisedanomalydetectionalgorithminaniotwaterqualitymonitoringsystem AT anaelizabethmarincelestino extensionofloracoverageandintegrationofanunsupervisedanomalydetectionalgorithminaniotwaterqualitymonitoringsystem AT efrainneriramirez extensionofloracoverageandintegrationofanunsupervisedanomalydetectionalgorithminaniotwaterqualitymonitoringsystem AT diegoarmandomartinezcruz extensionofloracoverageandintegrationofanunsupervisedanomalydetectionalgorithminaniotwaterqualitymonitoringsystem |