Indoor air pollution is more dangerous for residents. So, it is necessary to monitor the quality of indoor air and take some preventive steps to reduce the possible dangers to the health of the inhabitants. The cost and maintenance factors of air quality (AQI) systems lead the researchers to model, design, and implement low-cost indoor AQI monitoring systems. In this research, we proposed an indoor AQI monitoring system with a data-driven model to predict the AQI through the Neural Network Algorithm and Block-chain. The Internet of Things (IoT) connects and processes data, and low-cost sensors collect the data from the environment. The Indoor Air Quality system consists of temperature, humidity, Carbon Di Oxide, Particulate Matter, Carbon Mono Oxide, and LPG. The data are collected from five different sensors, and the NN decision-making model is used to predict the AQI to prevent harmful situations. The suggested IoT-based smart block-chain technology plays a vital role by imparting scalability, privacy, and reliability. This study will work effectively with ease of use, cost-effectiveness, and maintenance of the entire system.
Indoor air pollution is more dangerous for residents. So, it is necessary to monitor the quality of indoor air and take some preventive steps to reduce the possible dangers to the health of the inhabitants. The cost and maintenance factors of air quality (AQI) systems lead the researchers to model, design, and implement low-cost indoor AQI monitoring systems. In this research, we proposed an indoor AQI monitoring system with a data-driven model to predict the AQI through the Neural Network Algorithm and Block-chain. The Internet of Things (IoT) connects and processes data, and low-cost sensors collect the data from the environment. The Indoor Air Quality system consists of temperature, humidity, Carbon Di Oxide, Particulate Matter, Carbon Mono Oxide, and LPG. The data are collected from five different sensors, and the NN decision-making model is used to predict the AQI to prevent harmful situations. The suggested IoT-based smart block-chain technology plays a vital role by imparting scalability, privacy, and reliability. This study will work effectively with ease of use, cost-effectiveness, and maintenance of the entire system.
Indoor air pollution is more dangerous for residents. So, it is necessary to monitor the quality of indoor air and take some preventive steps to reduce the possible dangers to the health of the inhabitants. The cost and maintenance factors of air quality (AQI) systems lead the researchers to model, design, and implement low-cost indoor AQI monitoring systems. In this research, we proposed an indoor AQI monitoring system with a data-driven model to predict the AQI through the Neural Network Algorithm and Block-chain. The Internet of Things (IoT) connects and processes data, and low-cost sensors collect the data from the environment. The Indoor Air Quality system consists of temperature, humidity,
ppt_An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain (2).pptx
1. “An Intelligent and Secure Air Quality Monitoring
System Using Neural Network Algorithm and
Blockchain”
Submitted by
Batch no:
(NAME) (17NG1A0446)
x.xxxxxxxx (xxxxxxxxx xx)
x.xxxxxxxx (xxxxxxxxx xx)
x.xxxxxxxx (xxxxxxxxx xx)
Under the Esteemed Guidance of
Dr/Mr./Mr.'s (Guide name) (Qualification)
Designation
DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING
USHA RAMA COLLEGE OF ENGINEERING AND TECHNOLOGY
3. ABSTRACT
• The main objective of this project is to design an
indoor AQI monitoring system with a data-driven
model to predict the AQI through the Neural
Network Algorithm and Block-chain.
• IoT cloud enabled societal applications have
dramatically increased in the recent past due to the
thrust for innovations, notably through startup
initiatives, in various sectors such as agriculture,
healthcare, industry, and so forth. This poject
proposes a blockchain enabled IoT cloud
implementation to tackle the existing issues in smart
cities.
5. • For each and every sensor, corresponding
hardware sensor is used.
• Inly live values can be measure d and
observed by the client.
• This module is a low-power, compact, easily
configurable SBC which is powered by an ARM
Cortex-A53 processor . The Raspberry Pi
module encompasses features such as a Micro
SD port for external storage access.
6. Drawbacks
• 1. More hardware cost
• 2. No pre-estimation
• 3. No security
• 4. More processing time
8. • IAQ is a low-cost AQI monitoring system
developed using Arduino, Humidity,
Temperature, Carbon monoxide, Particulate
Matter, Carbon dioxide, liquefied petroleum
gas sensor, which is a gas leakage detection
sensor.
• Five types of sensors are used that are
connected using IoT and the block-chain
system that communicates with Neural
Network Algorithm (NNA) model that does
processing on data taken from the sensor as
input and predicts the result
9. • This block-chain module was executed in
MATLAB as blocks contents in a hash that is a
distinctive identifier; each block can calculate
a block hash, and the SHA-256 hash is
calculated from it.
10. EXPECTED OUTCOMES
• The IAQ system based on the NN decision-
making component is intelligent and smart.
Intelligent IAQ systems are a novel idea due to
their usage and design. To test the proposed
system’s performance, five measures were
collected from a room: Carbon Mono Oxide,
Carbon Di Oxide, LPG, Particulate Matter,
temperature, and humidity.
• Accurate calibration and prediction are
expected from above theory.
12. ADVANTAGES
• 1. Less cost
• 2. more reliable and accurate
• 3. Super fast
• 4. More security
13. APPLICATIONS
• Air velocity measurement system’
• Whether forecasting system
• Air quality measurement
• Pharmaceutical industries
• Semi conductor manufacturing
14. REFERENCES
• 1. C. Amuthadevi, D. Vijayan, and V. Ramachandran, “Development of air
quality monitoring (AQM) models using different machine learning
approaches,” J. Ambient. Intell. Humaniz. Comput., 1–13, 2021,
https://doi.org/10.1007/ s12652-020-02724-2.
• 2. H. Zhang, R. Srinivasan, and V. Ganesan, “Low cost, multipollutant
sensing system using raspberry pi for indoor air quality monitoring,”
Sustainability, Vol. 13, no. 1, pp. 370, 2021. DOI:10.3390/su13010370
• 3. D. Zhang, and S. S. Woo, “Real time localized air quality monitoring and
prediction through mobile and fixed IoT sensing network,” IEEE. Access.,
Vol. 8, pp. 89584–89594, 2020. DOI:10.1109/ACCESS.2020.2993547
• 4. A. Ibrahim. “A System for Monitoring and Managing Indoor Air Quality
and Environmental Conditions,” 2016. [Online]. Available:
https://scholarworks.boisestate.edu/ td/1156/.
