Data reinforcement control technique‑based monitoring and controlling of environmental factors for IoT applications - Mehbodniya, Abolfazl; Haq, Mohd Anul; Kumar, Anil; Ismail, Mohd Erfy; Dahiya, Priyanka; Karupusamy, Sathishkumar (Springer, 2022)
In recent years, environmental monitoring is essential to minimize environmental problems. However, the need for more and more aquatic ecosystems from pollution, increasing climate change, and direct environment loss, so that they can continue to be thoroughly monitored for risks. In this system, the implementation of a data reinforcement control technique (DRCT) using the Internet of Things (IoT) systems is presented to aquatic inquiry environments. In particular, it describes an automated system using data from the design and implementation of recording environments that monitor sensors and installation parameters in lakes and rivers. Design and low-cost real-time monitoring, alert worker development, polluted notification data are confirmed around. Various parameters such as air quality, temperature, humidity, and PIC controller sound intensity sensor, collection, and upload data to the cloud using the Wi-Fi module. It then forwards to a digital dashboard for confirmation on the web via a smartphone or cloud platform exception notification to alert the user. The industry will be the power of closed industrial electricity if they do not take any steps to reduce pollution within a specified period until they are paid under government regulations and pollution control and the amount of pollution in the city. The proposed system is simulated in the PROTEUS 8.1 simulator to analyze the system output.