The objective is to predict router fail/no fail (1/0) in a future time window with all the data collected over the last hour (i.e. binary target)
The data is received at two different levels:
- Router metrics: Memory, Temperature, CPU Usage, Idle time, etc..
- Connected devices metrics: Data collected from N different connected devices - received rates, signals, etc..
Every row in the training set should be a snapshot of data representing the hub + an aggregation of the N-different devices state (note that N may be different for every row).
Min, max and percentiles of the distribution of the connected device features could be added as new features. Are there other smarter techniques to preserve all the information in the connected clients?