Design of a Remote Real-time Groundwater Level and Water Quality Monitoring System for the Philippine Groundwater Management Plan Project

Recent technological advances allow us to utilize remote monitoring systems or real-time access of data. While the use of remote monitoring systems is not new, there are still numerous applications that can be explored and improved on, one such is groundwater level and quality monitoring. In the Philippines, the extraction of groundwater for both domestic use and industrial use are manually monitored by the government’s concerned agency and is done at least once per year. With this current setup, the real and significant state of the groundwater is not reflected in a way that is most valuable to the government and to the community. This project aims to design and develop a remote real-time groundwater level and quality monitoring system. It is intended to provide quantitative data for policy makers in addressing recurrent water shortages in the Philippines. This paper discusses the designed system composed of three modules: power module, sensors and control, and data visualization. These three modules provide real-time data from far-flung locations while being energy-sustainable. Dry runs of the system in a controlled environment yielded excellent results — average data accuracy of 96.63% for all six (6) groundwater quantity and quality parameters namely: pH, temperature, electrical conductivity, total dissolved solids, salinity, and static water level (SWL), and 90.63% data transmission reliability. Initial deployment of the system on one of the groundwater monitoring well in Metro Manila, Philippines returned a 91.16% data transmission reliability. The system is currently installed in 20 groundwater monitoring sites all-over the Philippines and is scheduled for more installations

Software and Data Visualization Platform for Groundwater Level and Quality Monitoring System

Rapid urbanization and increasing population come with the increased extraction and use of groundwater resources. To track the effect of these activities on groundwater level and quantity; a system for real-time monitoring is devised. In this paper; we present a software system design that enables a locally-developed groundwater level and water quality monitoring hardware setup to gather water quality parameter data; send it to a cloud server; and present organized data for better visualization. The hardware setup consists of an Arduino microcontroller. Upon deployment; the hardware setup is linked to an Android application that connects to the web-based platform

Wireless Sensor Network for Soil Monitoring

This Wireless Sensor Network (WSN) for Soil Monitoring is a system consisting of nodes with sensors and telemetry capabilities. This system is meant for deployment in agricultural applications, specifically, in banana plantations affected and unaffected by Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4). We focused on monitoring soil parameters such as pH, temperature and moisture. Other environmental parameters such as air temperature, air humidity and ambient light were gathered. Each sensor node uses a GSM data-transmission module for more stable and robust method even in far-flung areas in the Philippines. Raw data from the sensor nodes are stored in a web server for processing and data visualization. Reliability of daily transmission is 87.28%

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone\u27s safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time

Remote and Real-time Sensor System for Groundwater Level and Quality

The increasing demand for freshwater supply in the Philippines forces unsustainable extraction and unintentional contamination of the groundwater reservoirs. It is thus imperative that the groundwater sources be monitored for water level and quality. The Philippines has groundwater monitoring wells strategically installed in locations identified as critical areas. Accessibility of location and high cost of provisions and logistics prevent proper maintenance and continuous monitoring of these groundwater wells. This project addresses these by deploying a remote real-time groundwater level and quality sensor system. The groundwater wells initially monitored are located over three sites in Metro Manila, namely, Malabon, Marikina, and Alabang. Each site has a sensor system that collects and transmits seven parameters related to static water level and water quality and the deployed module\u27s power status. With a solar charging component, the deployed module can power itself for at least 6 months with minimal maintenance. The system delivers the maximum transmission reliability of 92% in the field with hourly sending rates and more than satisfies the required one data set per day minimum by the funding agency

Multiple Edge Computing Devices with Computer Vision for Social Distancing

Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to be protected from the risk of virus infection is to stay at least 1 meter apart from others while wearing a properly filtered mask. The study aims to design and develop a multiple edge computing system with computer vision capabilities to monitor the adherence of social distancing in multiple locations and in real time. An edge computing device uses a camera to process a stream of images. Graphical Processing Unit (GPU) was utilized for faster inference processing to detect people. The person\u27s location will undergo transformation to get a 2D perspective. Then, a distance calculation algorithm will be imposed to each pair of persons detected to detect breach of social distancing protocol. For every breach detected, location coordinates will be sent to the host database for visualization and monitoring. The use of multiple edge computing devices for computer vision application was compared to the IP camera system in monitoring multiple locations. It is found that utilization of multiple edge computing devices has significant advantages in terms of power consumption, data acquisition, image processing and inference, and setup cost

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone\u27s safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time

Design of an Automatic Temperature Screening System for Elevated Skin Temperature with Information Logging Capability

With the outbreak of the highly-contagious SARS-CoV-2 virus and its accompanying coronavirus disease 2019 (COVID-19), many government agencies adopted contact tracing to measure and mitigate the spread of the virus. Contact tracing aims to keep track of the individual\u27s movements and activities and identify all those who they come in contact with. This study is focused on designing a cost-effective, efficient, and accurate system for information logging and temperature screening with a complementary contact tracing feature. The system provides an automated, safe, and physical-distance-aware alternative to manual temperature measurement and data logging practiced by most commercial establishments. The system uses an Arduino and a Raspberry Pi, along with infrared temperature sensors utilizing proper calibration methods to yield temperature reading difference of 0.1 - 0.3 degree-Celsius taken at 10 cm distance. User identification is done by reading either specifically-registered RFID tags or system-generated identity-QR code. Temperature is subsequently read, date and time stamped, and logged into the system. This allows for automated and exact association of the user logged information with their corresponding temperature

Design of an Automatic Temperature Screening System for Elevated Skin Temperature with Information Logging Capability

With the outbreak of the highly-contagious SARS-CoV-2 virus and its accompanying coronavirus disease 2019 (COVID-19), many government agencies adopted contact tracing to measure and mitigate the spread of the virus. Contact tracing aims to keep track of the individual\u27s movements and activities and identify all those who they come in contact with. This study is focused on designing a cost-effective, efficient, and accurate system for information logging and temperature screening with a complementary contact tracing feature. The system provides an automated, safe, and physical-distance-aware alternative to manual temperature measurement and data logging practiced by most commercial establishments. The system uses an Arduino and a Raspberry Pi, along with infrared temperature sensors utilizing proper calibration methods to yield temperature reading difference of 0.1 - 0.3 degree-Celsius taken at 10 cm distance. User identification is done by reading either specifically-registered RFID tags or system-generated identity-QR code. Temperature is subsequently read, date and time stamped, and logged into the system. This allows for automated and exact association of the user logged information with their corresponding temperature