An IoT-based Framework to Forecast Indoor Air Quality using ANFIS-DTMC model

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Published Mar 1, 2020
https://doi.org/10.47164/ijngc.v11i1.173
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Volume 11, Issue 1, March 2020

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KRATI RASTOGI
Department of Computer Science & Engineering, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India
DIVYA LOHANI
Department of Computer Science & Engineering, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India

Abstract

As humans spend around 90% of their time indoors, Indoor Air Quality (IAQ) is a subject of major concern for the physical and mental well-being of humans. According to the United States Environment Protection Agency (US EPA), even in centrally air-conditioned buildings, indoor air is much more polluted than outdoor air, mainly due to changes in occupancy patterns, old or ill-maintained ventilation systems and dust. Therefore, it becomes important to measure and analyze IAQ. In this work, an end to end IoT system has been developed to sense and analyze indoor environmental parameters: Temperature (T), relative humidity (RH), carbon dioxide (CO2), carbon monoxide (CO), particulate matter (PM10 and PM2.5). For analysis purpose, a new index, namely, State of Indoor Air (SIA) has been proposed using adaptive neuro-fuzzy inference system (ANFIS). ANFIS model serves as a basis for constructing a set of fuzzy rules, to generate a specified pair of input-output with appropriate membership functions. SIA categorizes the state of indoor air as satisfactory, moderate or poor. Finally, a DTMC model has been used to forecast the change in SIA states by generating transition matrix and computing return periods of each SIA state. The accuracy of the proposed model is found to be satisfactory with a low average absolute prediction error of 2.60%.

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How to Cite
KRATI RASTOGI, & DIVYA LOHANI. (2020). An IoT-based Framework to Forecast Indoor Air Quality using ANFIS-DTMC model. International Journal of Next-Generation Computing, 11(1), 76–97. https://doi.org/10.47164/ijngc.v11i1.173
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