Wi-Fi positioning system

A Wi-Fi positioning system (WPS, WiPS, or WFPS) is a way to find out where a device is located using nearby Wi‑Fi access points. This helps when satellite navigation like GPS cannot be used, such as inside buildings where signals may be blocked or take too long to find.

Wi‑Fi positioning is very useful in cities where many wireless access points are available. It works by measuring how strong the signal is from these access points and comparing this to a database of known locations. Each access point has a unique identifier, which helps figure out the device's position.

The accuracy of this system depends on how correct the database is and how many access points can be detected nearby. To build the database, information from mobile devices using other location systems, like GPS, is used along with the Wi‑Fi access point details. This system can help guide people in places where GPS does not work well, such as inside large buildings or underground areas.

Motivation and applications

Accurate indoor location tracking is becoming more important for Wi‑Fi devices because of the growing use of augmented reality, social networking, health care monitoring, personal tracking, inventory control, and other location-aware apps.

In wireless security, Wi‑Fi helps find and map unusual network points called rogue access points. The popularity and low cost of Wi‑Fi cards make it a great choice for localization systems, and researchers have studied this for over 15 years.

Problem statement and basic concepts

The challenge of using Wi‑Fi for indoor location is figuring out where a device is compared to nearby access points. There are several ways to do this, and they can be grouped by the four main things they look at: received signal strength indication (RSSI), fingerprinting, angle of arrival (AoA), and time of flight (ToF).

Usually, the first step is to find out how far the device is from a few access points. Once you know these distances, trilateration can help find the device's spot using the known places of the access points. Sometimes, looking at the angles where signals arrive at the device can also help find its location using triangulation methods.

Mixing these methods together can make the location finding even more exact.

Techniques

Signal strength

RSSI localization measures the rough signal strength from several Wi-Fi access points to estimate distances. This method is simple but not very precise, usually within 2 to 4 meters, because signal strength can change with the environment.

Cisco uses RSSI to find devices through its access points, updating locations on its cloud service called Cisco DNA Spaces.

Linear array of antennas receiving a signal. The phase-shift difference of the received signal arriving at antennas equally separated by a "d" distance is used to compute the angle of arrival of the signal.

Monte Carlo sampling

Monte Carlo sampling is a statistical method used to estimate the location of devices indoors. It creates signal strength maps and uses special calculations to improve accuracy, often achieving sub-room precision with just one access point.

Fingerprinting

Fingerprinting records signal strengths from multiple access points and stores them with known locations. When a device is tracked, its current signal strengths are compared to stored data to estimate its location. This can be very accurate but requires updates if the environment changes.

Figure showing a measuring station sending a DATA frame to a client station and waiting until receiving the ACK. δ {\displaystyle \delta } is the scheduling delay (offset) originated at the target client device, and it depends on how much time it takes for the ACK to be scheduled. T_P is the signal propagation time between transmitter and receiver, and is usually assumed to be the same on the way to the target and back. T_ACK is the time needed to transmit the ACK frame. The time of flight corresponds to the T_MEASURED. Picture reproduced from

Angle of arrival

With MIMO Wi-Fi, which uses multiple antennas, it’s possible to estimate the direction of signals to calculate device locations. Systems like SpotFi and ArrayTrack use this method.

Time of flight

Time of flight (ToF) uses timestamps from wireless signals to calculate distances. This method can achieve accuracy within about 2 meters and is useful for tagging assets in buildings.

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Since 2019, French law requires drones heavier than 800 grams to broadcast their GPS coordinates via Wi-Fi. This data can help determine the position of nearby devices.

Privacy concerns

When using Wi-Fi to find out where someone is, there are some privacy worries. To help with this, Google suggested a way for owners of Wi-Fi access points to choose not to be part of finding locations. This can be done by adding "_nomap" to the name of the Wi-Fi network. The Mozilla Location Service also allows owners to choose not to take part by using the "_nomap" method.

Public Wi-Fi location databases

There are several public Wi-Fi location databases that are currently active and available.

Name	Unique Wi-Fi networks	Observations	Free database download	SSID lookup	BSSID lookup	Data License	Opt-out	Cell ID database	Bluetooth database	Coverage map	Comment
beaconDB	>120,000,000		No	No	Yes	Proprietary	_nomap	Yes	Yes	Map	Based on crowd-sourced data. Plans to publish data with public domain license.
Combain Positioning Service	>2,400,000,000	>67,000,000,000	No	Yes	Yes	Proprietary	_nomap	Yes	No	Map Wayback Machine
Unwired Labs Location API	>4,370,000,000		No	No	Yes	Proprietary	No	Yes	No	Map
Mylnikov GEO	860,655,230		Yes	No	Yes	MIT	—N/a (aggregator)	Yes	No	Map Wayback Machine
Navizon	480,000,000	21,500,000,000	No	No	Yes	Proprietary	No	Yes	No	Map Wayback Machine	Based on crowd-sourced data.
radiocells.org	13,610,728		Yes	No	Yes	ODbL	_nomap	Yes	No	Map Wayback Machine	Based on crowd-sourced data. Includes raw data.
WiGLE	1,205,634,974	16,460,980,303	No	Yes	Yes	Proprietary	_nomap, request	Yes	Yes	Map