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, humidity and carbon dioxide. These sensed values are then used to compute Predicted Mean Vote (PMV) and Ventilation Rate (VR), which are further combined to develop an indicator of the current air quality and thermal comfort index, the Air Quality and Comfort Indicator (AQCI), using fuzzy inference. © 2019 IEEE.