Another solution in the age of portable electronic devices: detect presence based on active DHCP leases. You can let then people register themselves by MAC on some web portal so they can be visible by name if they're comfortable with that.
Or Bluetooth. There are some problems (like MAC randomization) to get a real tracking with BLE, but to have a rough estimation, it's a good technique.
And you can do it with just an ESP32.
The idea is to scan every minutes for BLE MAC Addresses and send to a backend.
On the backend you can identify what is the manufacturer of every MAC addresses and make some correlation.
A very simple idea is to filter only the phone MAC addresses and use as presence counter estimator; it's a bit unlikely that a university student will have more than one phone.
A better method would try to correlate not random MAC addresses (headphones, BLE trackers, wearebles, etc) that are frequently together and mark as a single person.
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Another solution in the age of portable electronic devices: detect presence based on active DHCP leases. You can let then people register themselves by MAC on some web portal so they can be visible by name if they're comfortable with that.
Or Bluetooth. There are some problems (like MAC randomization) to get a real tracking with BLE, but to have a rough estimation, it's a good technique. And you can do it with just an ESP32.
Hmmm... interesting. How would that work? Just count the number of BT signals in a room?
How many BT devices is the average student or faculty member toting around with them at a given point?
Could conceivably be 4+ with some combination of: phone, smartwatch, tablet, BT speaker, game device, etc.
The idea is to scan every minutes for BLE MAC Addresses and send to a backend. On the backend you can identify what is the manufacturer of every MAC addresses and make some correlation. A very simple idea is to filter only the phone MAC addresses and use as presence counter estimator; it's a bit unlikely that a university student will have more than one phone.
A better method would try to correlate not random MAC addresses (headphones, BLE trackers, wearebles, etc) that are frequently together and mark as a single person.