I'm trying to do an indoor locationing system based on my RSSI signal on my routers, I'm sniffing my network so I know what's the RSSI of my phone related to my routers antennas (I have 5 antennas all over my house).The iPhone is not always broadcasting probe requests so I only get know in-real time the RSSI signal related to the router the device is connected to and no the other routers. To make it simple to understand: I sometimes have the RSSI of a device related to the 5 routers and sometimes only to 3 and in the worst case escenario only to 1 so my data sometimes looks like this:room: 1, device_id: 1, rssi1: -80, rssi2: unknown, rssi3: -55, rssi4: unknown, rssi5: unknownor:room: 1, device_id: 1, rssi1: -80, rssi2: -95, rssi3: -55, rssi4: -102, rssi5: -96or:room: 1, device_id: 1, rssi1: -80, rssi2: unknown, rssi3: unknown, rssi4: unknown, rssi5: unknownIt is room based, so I'm not triangulating. I am trying to forecast in which room I am inside the house just by looking on the behaviour of the signals.Machine Learning would be perfect, but if the data was always there and not some values missing sometimes. What kind of data algorithm should I use for this scenarios when sometimes I have all the data and sometimes just a few.
Machine Learning with sometimes missing data
machine learning;neural network;clustering;k means;unsupervised learning