I am trying to determine which room a person is in based on WiFi measurements. I currently have WiFi measurements for 3 rooms. The measurement consists of 100 WiFi scans collected from different locations inside each room. Each scan shows the number of WiFi APs visible at at some location inside the room. The number of APs seen in each of the 100 scans is variable. Here is one such scan:
1.SSID: wireless, BSSID: 00:21:6c:63:cc:dd, ss: -54, frequency: 2437 2.SSID: visitor, BSSID: 00:24:6c:61:aa:bb, ss: -58, frequency: 2462 3.SSID: visitor, BSSID: 00:24:6e:6d:ab:ab, ss: -60, frequency: 2437
ss: signal strength
This is taken from a single scan out of the 100 scans collected for each room.
Each scan is like one feature set for the room. I need to somehow train my system using this data so that it is able to classify a scan (may have multiple APs) taken during testing, to a particular room. What supervised training algorithm can I use? I already tried SVMs but they don't allow variable feature set.