Wi-Fi positioning system (WPS) is used where GPS is inadequate. The localization technique used for positioning with wireless access points is based on measuring the intensity of the received signal (received signal strength in English RSS) and the method of "fingerprinting". Typical parameters useful to geolocate the WiFi hotspot or wireless access point include the SSID and the MAC address of the access point. The accuracy depends on the number of positions that have been entered into the database. 

Each Wi-Fi access point, whether customer hotspot, router or Internet-capable point of sale system, transmits specific data. Using a RSSI (Received Signal Strength Indication) and MAC address (Media Access Control), an app can calculate the current location of the end user device (client-based positioning). This requires a database with information about the locations with which this data can be compared. This method is called fingerprinting. It only functions with Android devices due to technical restrictions. iOS devices cannot be used for Wi-Fi indoor navigation.

Existing Localization techniques:

1.RSSI and lateration based

RSSI localization techniques are based on measuring signal strength from a client device to several different access points, and then combining this information with a propagation model to determine the distance between the client device and the access points. Trilateration (sometimes called multilateration) techniques can be used to calculate the estimated client device position relative to the known position of access points.

Though one of the cheapest and easiest methods to implement, its disadvantage is that it does not provide very good accuracy (median of 2-4m), because the RSSI measurements tend to fluctuate according to changes in the environment or multipath fading.

2.fingerprinting based

Traditional fingerprinting is also RSSI-based, but it simply relies on the recording of the signal strength from several access points in range and storing this information in a database along with the known coordinates of the client device in an offline phase. This information can be deterministic or probabilistic.During the online tracking phase, the current RSSI vector at an unknown location is compared to those stored in the fingerprint and the closest match is returned as the estimated user location. Such systems may provide a median accuracy of 0.6m and tail accuracy of 1.3m

Its main disadvantage is that any changes of the environment such as adding or removing furniture or buildings may change the "fingerprint" that corresponds to each location, requiring an update to the fingerprint database. However, the integration with other sensor such as camera can be used in order to deal with changing environment.

Accurancy of WPS

Wi-Fi: Accuracy is 5-15 meters - less than with Bluetooth Low Energy.

Accuracy depends on multiple factors, for example the number of available networks, reflections for example in corridors and last but not least shielding through walls, ceilings and your own body. The accuracy of Wi-Fi used for indoor positioning varies from five to 15 meters – depending on the preconditions. Sensor fusion – this means the use of smartphone sensors – can even improve accuracy. A big advantage compared to GPS is that it is possible to determine the current floor level.

Pros:

• indoor positioning works without GPS
• existing Wi-Fi infrastructure can be used
• enabled Wi-Fi is sufficient
• there is a back channel to the client
• large range (up to 150m)
• detects floor level

Cons:

• relatively inaccurate (5-15m) compared to BLE/RFID
• Wi-Fi client based positioning is not possible with iOS devices – but BLE can be used as an alternative
• application required

Application areas of WPS;

Indoor navigation using Wi-Fi makes sense when Wi-Fi has to replace GPS in buildings. For example in complex infrastructures such as shopping malls, exhibition halls, railway stations, airports, hospitals, museums, office and industry buildings. An indoor navigation app improves visitor service and enables analyzing visitor flows. It is not suitable, if you have many iOS users among your audience.