SPIRI: Low Power IoT Solution for Monitoring Indoor Air Quality

Esquiagola, J.; Manini, M.; Aikawa, Arthur Sequeira; Yoshioka, Leopoldo Rideki; Zuffo, Marcelo Knörich

doi:10.5220/0006783002850290

Cited by 13 publications

(3 citation statements)

References 6 publications

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“…Monitoring IAQ in structures using IoT bias is necessary for maintaining a healthy terrain for inhabitants. likewise, the integration of IoT security systems, residency shadowing for space optimization, perpetration of smart lighting re-sults, waste operation through packing position monitoring, and deployment of immediate exigency response systems further enhance the overall functionality and safety of structures[ [32]. IoT platforms grease the collection and analysis of data, enabling the generation of practicable perceptivity and reports to support decision-making processes.…”

Section: Integration With Existing Building Management Systemsmentioning

confidence: 99%

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Qasim Flayyih,

Waleed,

M. Ibrahim

2024

IJCDS

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This exploratory disquisition delves into the world of Indoor Air Quality( IAQ) monitoring systems, using the solidarity of Artificial Intelligence( AI) and Internet of Effects ( IoT) technologies. Its overarching thing is to check the efficacity of these structures in regulating IAQ within structures, with a specific focus on mollifying pollutant degrees and their dangerous results on inhabitants. The study undertakes a comprehensive review of present literature and exploration trials, which depend upon AI and IoT algorithms for border monitoring, records analysis, and contrivance evaluation. also, it delves into the complications of machine armature, deployment ways, and functional efficiency. Furthermore, the exploration attracts different instructional budgets, including clever detectors and IoT bias stationed within the ambient surroundings. It elucidates the functionality of those instruments to accumulate real-time statistics, encompassing variables together with unpredictable natural composites, temperature oscillations, and moisture ranges. A vital aspect of this study is the disquisition of AI, contrivance getting to know Machine Learning ( ML), and Deep Learning ( DL) algorithms, showcasing their prophetic prowess within shadowing fabrics. also, they have a look at delving into the symbiotic dating among those algorithms, expounding their function in enhancing machine delicacy and optimizing energy intake. Moreover, the studies trials to delineate personalized health tips knitter-made to character inhabitants, decided from the wealth of records accrued through these structures. By integrating present-day technologies with empirical perceptivity, this takes a look at trials to pave the manner for better IAQ control strategies, fostering more healthy and lesser sustainable lodging surroundings.

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Section: Integration With Existing Building Management Systemsmentioning

confidence: 99%

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Qasim Flayyih,

Waleed,

M. Ibrahim

2024

IJCDS

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“…Poor air quality indoors poses a significant danger to human health, causing millions of deaths annually as a result [2]. Heating, cooling, ventilation, and indoor climate control systems used indoors can significantly contribute to indoor air pollution, which can be up to five times worse than outdoor pollution [3]. Monitoring indoor air quality and creating IoT-based decision-making systems can provide information on activities such as cooking, heating, disinfectant use, and indoor ventilation, which can benefit people with respiratory and similar illnesses [4].…”

Section: Introductionmentioning

confidence: 99%

Feedforward Neural Network-Based Indoor Air Quality Detection System

Akbaba,

Dişken

2023

ijamec

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Indoor air quality is crucial for the sustainability of human life quality. Therefore, improving indoor air quality is critical for enhancing life quality. In this study, an artificial intelligence-based indoor air quality monitoring system is designed. The system consists of two main parts, hardware and software. The hardware part includes a control card and various sensors. The software part includes a C-based IDE software and a feedforward network, a deep learning algorithm, for establishing the connection between the control card and the sensors. The temperature, humidity, and gas concentration values obtained from the sensors at certain intervals were fed to the feedforward network's input layer through the control card. The feedforward network consists of the input layer, hidden layer, and output layer, and the decision on whether the air quality is normal or not was made at the output. The system described in this study is intended to provide a real-time monitoring solution for indoor air quality. By using a feedforward neural network, the system is able to learn patterns in the sensor data and detect changes in air quality that may indicate a problem. The system can be customized to suit the needs of different environments and can be used in a variety of settings, including homes, offices, and public spaces. Ultimately, the goal of the system is to improve human health and well-being by ensuring that indoor air quality is at a safe and healthy level.

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“…The low-energy and highintegration properties of these sensors had led the writer to take advantage of this new technology. In a study conducted in Brazil, researchers combine several low-cost sensors into an embedded system that have been proven to be effective in mapping IAQ inside a building [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Indoor Air QualityBased on Low-Cost Sensors

Tahsiin¹,

Anggraeni²,

Chandra³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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According to WHO, indoor and/or outdoor air pollution is one of the main contributors to over two million premature deaths each year. As most of the human's life is spent indoor, Indoor Air Quality (IAQ)-an air quality inside of a building represented by pollutant concentration and thermal condition-is one factor that needs to be concerned to sustain healthy living. In this research, we developed an Internet of Things (IoT)-based IAQ monitoring device using low-cost sensors that measure the concentrations of Carbon Dioxide (CO 2), Oxygen (O 2), and Particulate Matter (PM 2.5). This device connects to an Android application to further observe these parameters inside two practicum laboratories in Telkom University, Bandung, for a total duration of six weeks. The location is surrounded by urban air pollution, particularly industrial activities, and residential waste burning. We also have sites of outdoor air quality monitoring system for simultaneous measurement. The environmental conditions were observed under no human activities, human intervention, and indoor plants' influence (i.e., Dieffenbachia sp.). Results show that pollutant concentrations are considerably influenced by outdoor conditions, occupancy level, and ventilation rate. Indoor plants can reduce CO 2 concentrations inside the room (21-47%). On the other hand, there is no clear evidence that PM 2.5 mass concentrations were affected by human activities. The bigger particles (PM >2.5 microns) probably were the ones induced by occupants during practicum. Therefore, using low-cost sensors is trustworthy to monitor IAQ for a better quality of life.

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SPIRI: Low Power IoT Solution for Monitoring Indoor Air Quality

Cited by 13 publications

References 6 publications

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Feedforward Neural Network-Based Indoor Air Quality Detection System

Analysis of Indoor Air QualityBased on Low-Cost Sensors

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