Real-time monitoring of cabin air quality is of paramount interest. The proposed system employs supervised ML techniques on simulated data to monitor air quality and suitably mitigate the anomaly as much as possible. The CO2 values and TPH parameters are monitored, and a neural network is trained to classify anomalies to various kinds to facilitate efficient mitigation.
- Develop data management architecture for airplane Internet of Things (IoT) applications
- Airplanes IoT: unique constraints
- Limitations of on-board network (cost, power, weight)
- Highly constrained off-board links.
- Hierarchical data management architecture
- Flexibly access data as required
- Investigate IoT approaches/strategies that may be applied to airplanes.