Sustainable IoT Vision Low-Energy Sensor for Environmental Prognosis
Language: English
Published by Classicsbooks, 2023
- Softcover
- Used
Condition: Used
£ 38.68
Quantity: 1 available
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Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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- Title
- Sustainable IoT Vision Low-Energy Sensor for Environmental Prognosis
- Author
- Bharatkumar
- Publisher
- Classicsbooks
- Publication year
- 2023
- Condition
- Hervorragend
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798223494119
- Seller catalogs
- Bücher
In recent years, increased transportation, removal of trees for making
buildings, establishment of new industries, are the main sources of increased
pollution. Increased pollution is one of the major challenge faced by all
countries as it affects environment and human health. On of the way to deal
with this challenge is monitoring the environment quality and taking
corrective steps for the same. The conventional instruments used for
environment monitoring are accurate but costly, time consuming, requires
human intervention and lacking in terms of portability. Internet of Things
(IoT) enabled wireless sensor node is one of the ideal solutions for real time
monitoring of environment in today's urban ecosystems. We have developed
a low power IoT enabled wireless sensing and monitoring platform for
simultaneous monitoring, real time data of ten different environmental
parameters such as Temperature, Relative Humidity, Light, Barometric
Pressure, Altitude, Carbon dioxide (CO2), Volatile Organic Compounds
(VOCs), Carbon Monoxide (CO), Nitrogen Dioxide (NO2) and Ammonia
(NH3). We have tried to achieve low power through modification in sensor
node hardware architecture and developing prediction model which
eliminates the need of power hungry sensor. The proposed hardware
architecture for wireless sensor node helps in reducing power and number of
interfacing pins required from the microcontroller. The proposed wireless
sensor node architecture is also adaptable for any other applications after
replacement or removal of sensors and/or modification of supply. The
developed system consists of the transmitter node and the receiver node.
The data received at the receiver node is monitored and recorded in an excel
sheet in a personal computer (PC) through a Graphical User Interface
(GUI), made in LabVIEW. An Android application has also been developed
through which data is transferred from LabVIEW to a smartphone and
enables IoT. The system is validated through experiments and deployment
for real time monitoring. For the proposed system reliability of transmission
achieved is 97.4%. Power consumption of the sensor node is quantified
which is equal to 25.67mW and can be varied by varying the sleep time or
sampling time of the node. Battery life of approximately 31 months can be
achieved for the measurement cycle of 60 secs. PM2.5 is one of the
important pollutants for measuring air quality. Existing methods and
instruments used for the measurement of PM2.5 are more laborious, not
applicable for both online and offline, having response time from a few
minutes to hours and lacking in terms of portability. In this work we
present the correlation study of PM2.5 with other pollutants based on the
data received by Central Pollution Control Board (CPCB) online station at
N 23 ◦ 0' 16.6287, E 72◦ 35' 48.7816. Based on the correlation results, CO,
NO2, SO2 and VOC parameters (Benzene, Toluene, Ethyl Benzene, M+P
Xylene, O-Xylene) are selected as predictors for developing PM2.5
prediction model. PM2.5 prediction model is developed using Artificial
Neural Network (ANN), resulting in a simple analytical equation. Since the
proposed model is expressed in simple mathematical equation, it can be
deployed on a wireless sensor node enabling online monitoring of PM2.5.
Closeness of predicted and actual values of PM2.5 are verified through
processing derived model equations using low cost processing tool (e.g. excel
sheet), thereby eliminating the need for proprietary tools.
"Synopsis" may belong to another edition of this title.
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