Table 4 Summary of IAQ monitoring systems based on other architectures
From: A comprehensive review on indoor air quality monitoring systems for enhanced public health
Sr. No. | References | Year | Parameters considered | Architecture | Communication Interface | MCU | Data Access | Remarks |
|---|---|---|---|---|---|---|---|---|
1. | Wu et al. [33] | 2017 | PM | C-Air platform | Not available | Raspberry Pi A+ | Mobile app | Machine learning algorithm was used for particle detection and sizing |
2. | Zampolli et al. [34] | 2004 | NOx, CO, VOCs and RH | eNose architecture based solid-state sensor array | Custom-made electronic interface | ST52T301P | Simulation environment | Fuzzy pattern recognition algorithm was used |
3. | Pillai et al. [37] | 2010 | VOCs, CO, hydrogen | C-N based sensor network | CAN | AT89C51CC03 | LED DisplaThe e | Experiment was performed on breadboards in a lab environment |
4. | Cheng et al. [39] | 2014 | PM2.5 levels | Cloud-based engine | Bluetooth 0.4, 3G mobile data connection and Wi-Fi | Raspberry Pi | Mobile Apps, WeThe p | Prediction model was designed using Artificial Neural Network |
5. | Moreno-Rangel et al. [47] | 2018 | Fine PM2.5, CO2, VOCs, RH and temperature | Foobot FBT0002100 | Wi-Fi | Not available | Cloud System, Tablet | – |