Smart City Gnosys
Smart city article details
| Title | Air Quality Monitoring System With Effective Traffic Control Model For Open Smart Cities Of India |
|---|---|
| ID_Doc | 7166 |
| Authors | Singh S.; Ananthanarayanan V. |
| Year | 2021 |
| Published | Lecture Notes in Electrical Engineering, 711 |
| DOI | http://dx.doi.org/10.1007/978-981-15-9019-1_36 |
| Abstract | Industry is growing rapidly these days; the greatest problem for any nation would be the environmental protection. Industries and vehicles which are producing poisonous gases are creating challenges. Predicting the presence and density of harmful gases, finding the right predictive value, and raising notifications in real-time are the biggest challenges. Smart cities use a lot of real-time systems; the data generated from these systems can be better utilized if it is exchanged with other organizations. Sharing the real value data of air quality with the surveillance systems in several smart cities will help in developing a solution for air pollution. Many sensors are available for sensing the poisonous gases in air. Implementing machine learning algorithm along with the collected sensor data could help in predicting the air quality with high accuracy. One-third of the total pollution comes from traffic due to the congestion of vehicles there. Hence, a smart traffic management which operates on the expected value of the air quality is necessary to control air pollution. MQ series sensors have been used for collecting the poisonous gas data present in the air. A wireless network model (WSN) is used for communication with the gateway. WSN will also provide a service protocol to send the data from one place to another (José Luis Herrero Agustın, Wireless sensor network for air quality monitoring and control). Implementing communication protocol LORA (short for long range) will provide more advantages compared to other protocols in long-range communication. Collected data from sensors can be easily sent on a private cloud and the machine learning algorithm can also use the value for further prediction. The user can access any information from the cloud through an open API and library. With such a model, the society can develop an understanding of air quality in real time. Smart traffic management with the support of the recurrent neural network will help in protecting the future generation and will provide a solution for the challenges. © 2021, Springer Nature Singapore Pte Ltd. |
| Author Keywords | Gateway; Long-short term memory (LSTM); LORA; MQ series sensor; Node MCU; Recurrent neural network; Recursive algorithm (RA) |
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