An IoT Based Air Quality Measurement and Warning System for Ambient Assisted Living

Year 2019, Issue: 16, 960 - 968, 31.08.2019

Mehmet Taştan

https://doi.org/10.31590/ejosat.607434

Cited By: 2

Abstract

Indoor air
quality parameters are extremely important for creating an efficient and
healthy Ambient Assisted Living (AAL) environment, but mostly indoor air
quality parameters are well above the values defined as healthy. We spend most
of our lives indoors. Detecting air quality problems and improving air quality
is only possible by monitoring air quality in real time. Today, smart home
automation has become a popular trend, and consumers are increasingly aware of
new technologies developed in this area, hence the demand for smart homes is
growing. In this study, with the IoT-based indoor air quality measurement and
warning system, an AAL system was proposed to help especially elderly and
children, to  live safely in their homes.
The proposed AAL system consists of a ESP32 controller with a new generation
embedded system architecture and low cost different air quality sensors. In
addition, the AAL system provides real-time monitoring of indoor air quality
parameters such as CO₂, CO, PM₁₀, NO₂,
temperature and humidity via the mobile user interface developed with the Blynk
IoT platform. The mobile application sends warning notifications to users if
the indoor air quality parameters exceeded the specified threshold values.
Thanks to these notifications, households can take the necessary measures as
soon as possible against the factors that threaten the health of the elderly
and children with simple measures such as natural ventilation. The results
showed that the proposed measurement system can contribute significantly to AAL
systems with its low cost, open source technology, easy installation and
mobility.

Keywords

Internet of things, Ambient Assisted Living, indoor air quality, ESP-32, Blynk

Ortam Destekli Yaşam için IoT Tabanlı Hava Kalitesi Ölçüm ve Uyarı Sistemi

Abstract

İç hava kalitesi parametreleri, verimli ve sağlıklı
bir Ortam Destekli Yaşam (ODY) ortamı oluşturmak için son derece önemlidir,
ancak çoğunlukla iç hava kalitesi parametreleri, sağlıklı olarak tanımlanan
değerlerin oldukça üzerindedir. Hayatımızın büyük bölümünü kapalı alanlarda geçiriyoruz.
Hava kalitesi problemlerini tespit etmek ve hava kalitesini iyileştirmek ancak
hava kalitesinin gerçek zamanlı olarak izlenmesiyle mümkündür. .
Günümüzde, ev otomasyonu popüler bir trend haline gelmiştir
ve akıllı evlere olan talep , tüketicilerin bu alanda
geliştirilen yeni teknolojilerden daha fazla haberdar olmalarıyla giderek
artmaktadır. Bu çalışmada, IoT tabanlı iç mekan hava kalitesi ölçüm ve uyarı
sistemi ile özellikle yaşlıların ve çocukların evlerinde güvenle yaşamasına
yardımcı olmak için bir AAL sistemi önerilmiştir. Önerilen AAL sistemi, yeni
nesil gömülü sistem mimarisine sahip ESP32 denetleyiciden ve düşük maliyetli
farklı hava kalitesi sensörlerinden oluşur. Ayrıca  AAL sistemi, Blynk IoT platformuyla
geliştirilen mobil kullanıcı arayüzü aracılığıyla CO₂, CO, PM₁₀, NO₂ , sıcaklık ve nem gibi iç mekan hava kalitesi
parametrelerinin gerçek zamanlı izlenmesini sağlar. İç mekan hava kalitesi
parametreleri belirtilen eşik değerlerin üzerine çıktığında mobil uygulama
kullanıcılara uyarı bildirimleri gönderir. Bu bildirimler sayesinde hane halkı,
doğal havalandırma gibi basit önlemlerle yaşlı ve çocukların sağlığını tehdit
eden faktörlere karşı en kısa sürede gerekli önlemleri alabilir. Sonuçlar,
önerilen ölçüm sistemin düşük maliyetli, açık kaynaklı teknoloji, kolay kurulum
ve taşınabilir özellikleriyle AAL sistemlerine önemli ölçüde katkıda
bulunabileceğini göstermiştir.

Keywords

Nesnelerin İnterneti, Ortam Destekli Yaşam, İç Hava Kalitesi, ESP-32, Blynk

References

• Air quality in Europe, 2018 report, European Environment Agency https://www.eea.europa.eu/publications/air-quality-in-europe-2018/download, (accessed date, 04.08.2019).
• Benammar, M., Abdaoui, A., Ahmad, S., Touati, F., & Kadri, A. (2018). A modular IoT platform for real-time indoor air quality monitoring. Sensors, 18(2), 581.
• Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., & De Munari, I. (2019). IoT Wearable Sensor and Deep Learning: an Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment. IEEE Internet of Things Journal.
• Bröring, A., Schmid, S., Schindhelm, C. K., Khelil, A., Kabisch, S., Kramer, D., López, E. (2017). Enabling IoT ecosystems through platform interoperability. IEEE software, 34(1), 54-61.
• Cho, H. (2017). An Air Quality and Event Detection System with Life Logging for Monitoring Household Environments. In Smart Sensors at the IoT Frontier (pp. 251-270). Springer, Cham.
• Darwish, M., Senn, E., Lohr, C., & Kermarrec, Y. (2014). A comparison between ambient assisted living systems. In International Conference on Smart Homes and Health Telematics (pp. 231-237). Springer, Cham.
• ESP32 Series Microcontrollers, Version 3.1 Espressif Systems, https://www.espressif.com/sites/default/files/documentation/esp32_datasheet_en.pdf, (accessed date, 04.08.2019).
• Fioccola, G. B., Sommese, R., Tufano, I., Canonico, R., & Ventre, G. (2016). Polluino: An efficient cloud-based management of IoT devices for air quality monitoring. In 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) (pp. 1-6). IEEE.
• Gupta, R., & Singh, K. K. (2018). IOT Door Monitoring System using Android Application. Trends in Opto-Electro and Optical Communications, 7(3), 21-24.
• Harper, R. (Ed.). 2006. Inside the smart home. Springer Science & Business Media.
• Hassan, M., Bermak, A., Ali, A. A. S., & Amira, A. (2015). Gas identification with spike codes in wireless electronic nose: A potential application for smart green buildings. In 2015 SAI Intelligent Systems Conference (IntelliSys) (pp. 457-462). IEEE.
• Keswani, B., Mohapatra, A. G., Mohanty, A., Khanna, A., Rodrigues, J. J., Gupta, D., & de Albuquerque, V. H. C. (2019). Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Computing and Applications, 31(1), 277-292.
• Kulkarni, A., & Mukhopadhyay, D. (2018). Internet of Things Based Weather Forecast Monitoring System. Indonesian Journal of Electrical Engineering and Computer Science, 9(3), 555-557.
• Lynggaard, P., & Skouby, K. E. (2016). Complex IoT Systems as Enablers for Smart Homes in a Smart City Vision. Sensors, 16(11), 1840.
• Makhlouf, A., Boudouane, I., Saadia, N., & Cherif, A. R. (2019). Ambient assistance service for fall and heart problem detection. Journal of Ambient Intelligence and Humanized Computing, 10(4), 1527-1546.
• Seguel, J. M., Merrill, R., Seguel, D., & Campagna, A. C. (2017). Indoor air quality. American journal of lifestyle medicine, 11(4), 284-295.
• Taştan, M. (2019-1). Internet of Things based Smart Energy Management for Smart Home. KSII Transactions on Internet & Information Systems, 13(6).
• Taştan, M. (2019-2), Real-Time Monitoring of Indoor Air Quality with Internet of Things Based E-nose. Applied Sciences, 9(15), 2019.
• Taştan, M. 2018. IoT Based Wearable Smart Health Monitoring System. Celal Bayar University Journal of Science, 14(3), 343-350.
• WHO. Air Pollution Levels Rising in Many of the World’s Poorest. http://www.who.int/mediacentre/news/releases/2016/air-pollution-rising/en/#.WhOPG9ANlBk.mendeley (accessed date, 06.08.2019).