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1. You are designing an IoT network to monitor hallway activity at the local college. Whenever there is movement in the hallway, you will want to track it to determine lighting, temperature, and classroom use.
What assumption can you make while developing your network?
Data will arrive in bursts with periods of low or no activity as well as periods of high activity.
Data will arrive at regular intervals with predictable periods
It will be easy to track individual student movement.
To train a model to predict activity, you will need data over several semesters.
2. You are designing an IoT network that monitors hallway activity in all schools in New York city.
What assumptions you can make while developing your network?
Due to the massive amounts of data that will flow during high activity times, the system needs to be designed to handle the maximum flow.
Massive amounts of data need to be stored. The network needs inexpensive storage capacity with fast access to recent data as well as access to older data.
During periods of high activity messages may come into the network at the same time, potentially leading to data loss. To prevent this you will need a system that can guarantee message delivery.
3. You have successfully designed an IoT network for a school district. You have also created a machine learning model that makes inferences about student activity in the hallways.
What considerations should you keep in mind as you deploy your network?
The machine learning model will need to be regularly trained with new data.
You will have to keep track of changes in the holiday schedules for the schools, as this can affect the ML model.
If the number of schools and students increase, it may be necessary to consider doing some of the data processing on the Edge, rather than in the cloud.
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