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Fig. 2. System Flowchart for the IoT-Based framework for air and sound pollution monitoring and control system Fig. 7 and Fig. 8 illustrate the monitoring and control system’s data analysis for gas and sound pollution. The processed sensor data are transmitted through the Wi-Fi module to the internet and display on the thingspeak.com website. As can be observed, the sensor data for the air and sound pollution are analyzed, visualized and presented. The user can analyze it with the help of the plotted graphs. The plotted graphs make it very easy to verify the levels of air and sound pollution in a smart environment and plan for a healthy living.

Figure 2 System Flowchart for the IoT-Based framework for air and sound pollution monitoring and control system Fig. 7 and Fig. 8 illustrate the monitoring and control system’s data analysis for gas and sound pollution. The processed sensor data are transmitted through the Wi-Fi module to the internet and display on the thingspeak.com website. As can be observed, the sensor data for the air and sound pollution are analyzed, visualized and presented. The user can analyze it with the help of the plotted graphs. The plotted graphs make it very easy to verify the levels of air and sound pollution in a smart environment and plan for a healthy living.

Related Figures (11)

Fig. 1. An loT-Based framework for air and sound pollution monitoring and control system. An integration of several environmental sensor networks within the framework of the IDEA project was presented. The main aim was to measure noise and air quality, pollution levels in urban areas in Belgium, making use of a low-cost sensor. The work presented the IDEA environmental measurement cloud as a proof-of-concept. The provision of Data-as- a-Service (DaaS) cloud platform that combines environmental sensor networks with a sensor web. The The proposed IoT-Based framework for air and sound pollution monitoring and control system consists of six units, namely, the user interface; the microcontroller, Wi-Fi module; gas sensor; sound sensor; and liquid crystal display units as shown in Fig. 1. The user interface unit provides the means by which the users can interact with the system through a personal computer (PC), or mobile devices.
Fig. 1. An loT-Based framework for air and sound pollution monitoring and control system. An integration of several environmental sensor networks within the framework of the IDEA project was presented. The main aim was to measure noise and air quality, pollution levels in urban areas in Belgium, making use of a low-cost sensor. The work presented the IDEA environmental measurement cloud as a proof-of-concept. The provision of Data-as- a-Service (DaaS) cloud platform that combines environmental sensor networks with a sensor web. The The proposed IoT-Based framework for air and sound pollution monitoring and control system consists of six units, namely, the user interface; the microcontroller, Wi-Fi module; gas sensor; sound sensor; and liquid crystal display units as shown in Fig. 1. The user interface unit provides the means by which the users can interact with the system through a personal computer (PC), or mobile devices.
Fig. 3. Schematics Block Design of An IJoT-Based framework for air and sound pollution monitoring and control system.
Fig. 3. Schematics Block Design of An IJoT-Based framework for air and sound pollution monitoring and control system.
Fig. 4. The implemented prototype.
Fig. 4. The implemented prototype.
Fig. 5. Data logged for air and sound pollution for the month of January 2018.
Fig. 5. Data logged for air and sound pollution for the month of January 2018.
Fig. 7. Variations of gas and sound pollution with respect to time. 5. Case Study
Fig. 7. Variations of gas and sound pollution with respect to time. 5. Case Study
Fig. 6. Data logged for air and sound pollution for the month of June 2018.
Fig. 6. Data logged for air and sound pollution for the month of June 2018.
Fig. 8. Variations of gas and sound pollution with respect to time.
Fig. 8. Variations of gas and sound pollution with respect to time.
Fig. 9. The variations of sound and gas pollution from kerosene stove and mobile phone sources.
Fig. 9. The variations of sound and gas pollution from kerosene stove and mobile phone sources.
Fig. 10. The variations of sound and gas pollution from a motorbike as a source.
Fig. 10. The variations of sound and gas pollution from a motorbike as a source.
Fig. 11. The variations of sound and gas pollution from car exhaust engine.
Fig. 11. The variations of sound and gas pollution from car exhaust engine.
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