Figure 8 - uploaded by Mehmet Taştan
Content may be subject to copyright.
Contexts in source publication
Context 1
... Figure 6,7 and 8, weekly change graphs of climate parameters and gas concentrations are given. Figure 8 shows the weekly change graph of indoor climate parameters. One-week temperature and humidity change graph shows that the temperature varies between 26.1-30.1C ...
Similar publications
Semi-supervised Federated Learning over Wireless Edge
Citations
... Air IoT systems collect real-time data including the critical quality parameters such as Temperature (in °C or °F), Humidity (in %), CO₂ (in ppm), Pressure (in hPa), TVOC (=total concentration of volatile organic compounds such as asbestos, for example, in ppb), eCO₂ (=estimated concentration of CO₂ calculated from TVOC; in ppm), Particulate matter (in µ/m 2 ). The sensors can be altered based on the prioritization of the air quality parameters [19]. The application of IoT systems in air quality helps to identify the air toxins from various air polluting sources including stationary sources (such as industries, agriculture, commercial kitchens, etc.) and mobile sources (such as cars, trucks, ships, etc.) [20]. ...
... Air IoT systems collect real-time data including the critical quality parameters such as Temperature (in • C or • F), Humidity (in %), CO 2 (in ppm), Pressure (in hPa), TVOC (=total concentration of volatile organic compounds such as asbestos, for example, in ppb), eCO 2 (=estimated concentration of CO 2 calculated from TVOC; in ppm), Particulate matter (in µ/m 2 ). The sensors can be altered based on the prioritization of the air quality parameters [19]. The application of IoT systems in air quality helps to identify the air toxins from various air polluting sources including stationary sources (such as industries, agriculture, commercial kitchens, etc.) and mobile sources (such as cars, trucks, ships, etc.) [20]. ...
Sensor networks using the Internet of Things (IoT) are gaining momentum for real-time monitoring of the environment. Increased use of natural resources due to a rise in agriculture production, manufacturing, and civil infrastructure poses a challenge to sustainable growth and development of the global economy. For sustainable use of natural resources (including air, soil, and water), data-driven modeling is needed to understand and simulate contaminant transport and proliferation. Different logging devices are specifically designed to integrate with environmental sensors that send real-time data to the cloud using IoT systems for monitoring. The IoT systems use an LTE network or Wi-Fi to transmit air, water, and soil quality data to the cloud networks. This seamless integration between the logging devices and IoT sensors creates an autonomous monitoring system that can observe environmental parameters in real-time. Various federal organizations and industries have implemented the IoT-based sensor network to monitor real-time air quality parameters (particulate matter, gaseous pollutants), water quality parameters (turbidity, pH, temperature, and specific conductance), and soil parameters (moisture content, soil nutrients). Although several organizations have used IoT systems to monitor environmental parameters, a proper framework to make the monitoring systems reliable and cost-efficient was not explored. The main objective of this study is to present a framework that combines a sensing layer, a network layer, and a visualization layer, allowing modelers and other stakeholders to observe a progressive trend in environmental data while being cost-efficient. This efficient real-time monitoring framework with IoT systems helps in developing robust statistical and mathematical models. The sustainable development of smart cities while maintaining public health requires reliable environmental monitoring data that can be possible by the proposed IoT framework.
... Blynk supports a large number of controllers such as Arduino, ESP8266, ESP32, Raspberry Pi, Onion Omega, SparkFun, etc., which are widely used in IoT applications. Using this IoT platform, without the need to write codes, an iOS/Android mobile interface can be developed for IoT projects in a very short time using only Widgets [39,44,49,53,55]. ...
... As seen in Fig. 5(d), the threshold values of the measured air quality parameters can be adjusted via the user interface. In case the threshold values determined for pollutant concentration and climate parameters are exceeded, real-time notifications are sent to users through 2 different channels, via e-mail and mobile device [53,54]. Figure 5(e) shows the event editor window for notifications to be sent to users for different events. ...
Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.
