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ردیف | عنوان | نوع |
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1 |
LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring
پیاده سازی سیستم اینترنت اشیاء مبتنی بر LoRaWAN برای نظارت بر کیفیت هوای خارج از منزل در محدوده بلند-2022 This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality in-
formation and update it on the cloud. The developed long-range wide area network (LoRaWAN)-
based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-
IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness.
The system is composed of multiple sensors (NO2, SO2, CO2, CO, PM2.5, temperature, and hu-
midity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network
(TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a
rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable
operation. Our system simultaneously gathers the considered air quality information by using the
smart sensing unit. Then, the system transmits the information through the gateway to the TTN
platform, which is integrated with the ThingSpeak IoT server. This action updates the collected
data and displays these data on a developed Web-based dashboard and a Graphical User Interface
(GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily
accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-
AQMS are validated by comparing them with experimental results based on the high-
technology Aeroqual air quality monitoring devices. Our system can reliably monitor various
air quality indicators and efficiently transmit the information in real time over the Internet. keywords: پایش کیفیت هوا | Air quality monitoring | Iot lora lorawan | TTN ThingSpeak Virtuino |
مقاله انگلیسی |
2 |
A stochastic model of particulate matters with AI-enabled technique-based IoT gas detectors for air quality assessment
مدل تصادفی ذرات معلق با ردیاب های گاز IoT مبتنی بر تکنیک هوش مصنوعی برای ارزیابی کیفیت هوا-2020 Monitoring air quality in urban and industrial environments and estimating exposure to particulate matter (PM)
pollution concentrations are critical issues that affect human health. Because of aerosols (suspended particles),
PM is mostly observed near the surface and thus can be inhaled. To predict the modeling of micro-to-nano-sized
particle suspensions, this study presents a stochastic model in environmental dynamics with internet of things
(IoT) gas detectors based on an artificial intelligence (AI)-enabled technique; the model can determine floating
fine PM dispersion in a city to assess and monitor air quality. The factors that influence the prediction are
weather- and air pollution-related data, such as humidity, temperature, wind, PM2.5, and PM10. In this study,
these factors have been considered at 7 measuring stations across the urban region in Taipei City, Taiwan, from
2013 to 2018. A nonlinear autoregressive network with exogenous inputs model is constructed using estimated
states to investigate approaches for identifying PM; the model can be a state–space self-tuning stochastic model
for predicting unknown nonlinear sampled data. The results indicate that a satisfactory agreement was obtained
using a normalized root mean square deviation, with small values of 0.0504 and 0.0802 for PM2.5 and PM10,
respectively. Accordingly, this study presents that the time-domain causality between PM and the atmospheric
environment can be constructed using discrete-time models that can be satisfactorily implemented in developing
different air quality monitoring systems for the long-term prediction of air pollution. Keywords: Particulate matter | Micro-to-nano-sized particle suspensions | Modeling | Micropollutants | Artificial intelligence | Atmospheric environment |
مقاله انگلیسی |
3 |
Design of Low Cost, Energy Efficient, IoT Enabled, Air Quality Monitoring System with Cloud Based Data Logging, Analytics and AI
طراحی کم هزینه ، انرژی کارآمد ، اینترنت اشیا ، سیستم نظارت بر کیفیت هوا با ثبت داده مبتنی بر ابر ، تجزیه و تحلیل و هوش مصنوعی-2020 This paper presents a design of real-time Air Quality
Monitoring System (AQMS) which incorporates Internet of Things
(IoT) and cloud computing. AQMS utilizes solar panel and battery
pack for independent and autonomous operation, thus, making it
self-powered and sustainable. AQMS is based on AVR
Microcontroller (Atmega32) and GSM modem (Sim900) for
connectivity with the cloud application. The design is made low cost
and scalable so that around 50nos. of such systems can be installed on
roundabouts of market places, residential and industrial areas. The
AQMS monitors the air quality with the help of a miniature suction
pump (5volt DC) which establishes a controlled and constant stream
of air-flow through a manifold that encapsulates electromechanical
sensors, thus measuring the concentration of O2, CO, CO2, SO / SO2
(SOx), NO/ NO2 (NOx), Hydrocarbon (CxHx), temperature, humidity
and noise. By default, the air sampling is carried out once in an hour
which may be changed depending on the change in air quality, i.e.
making it adoptive for energy conservation and extending the sensor’s
life. The data collected at the cloud application will be processed using
data analytics and Artificial Intelligence (AI) for getting insights of
data (data mining) regarding the potential locations where the
emissions are critical and disastrous for environmental, thus, leading
to prevent any mishap. The design is mapped over a metropolitan city
of Pakistan, i.e. Karachi, thus initiating the transformation of Karachi
to a smart city. Keywords: Air Quality Monitoring System | Internet of Things | Cloud Computing | Data Analytics | Artificial Intelligence |
مقاله انگلیسی |