Learn more about Wi-Fi and how this omnipresent radio-frequency technology can be used to detect location and be easily activated to power a multitude of indoor positioning and location-based IoT applications.
Wi-Fi is a radio-frequency technology for wireless communication that can be leveraged to detect and track the location of people, devices and assets, and can be easily activated for indoor positioning with existing Wi-Fi access points (APs) and hotspots. Incredibly vital and widespread, the Wi-Fi ecosystem is ubiquitous all throughout our daily lives. Wi-Fi is everywhere, especially in our indoor spaces, used by nearly all wireless devices and network infrastructures - including smartphones, computers, IoT devices, routers, APs, and more. Like other communication protocols including BLE and UWB, Wi-Fi can be used to transmit data between devices using radio waves.
The first version of the 802.11 Wi-Fi protocol was released in 1997. Since then it has become one of the most important communication technologies in the world, serving as a backbone for global communication. A few years after the release of Wi-Fi, it emerged as one of the first go-to technologies for indoor positioning. As Wi-Fi’s popularity as an indoor positioning technology grew, companies like Apple, Google, and Microsoft began to combine Wi-Fi with GPS to provide location services in indoor spaces where GPS was not able to reach, using existing building APs.
To this day, Wi-Fi is still an effective option for indoor positioning, although it is typically less accurate than competing positioning technologies like BLE and UWB. Wi-Fi’s continued significance as a key technology for location-based applications, is largely in part because of the large number of Wi-Fi devices and already established Wi-Fi infrastructures throughout indoor spaces.
The recent release of the next generation of Wi-Fi, Wi-Fi 6, will further strengthen Wi-Fi’s role as one of the world’s most important technologies. Wi-Fi 6 is designed to be significantly quicker, more reliable, accurate, and secure than previous generations. It’s built for the new IoT landscape and will lead to more Wi-Fi devices that have cutting-edge capabilities for transformative use cases, many of which will draw on location.
Wi-Fi’s vast presence and accessibility makes it a very important standard for indoor positioning applications. It can be leveraged in many location-based use cases and offers options to easily get started with indoor positioning through existing Wi-Fi infrastructures.
The most commonly used Wi-Fi positioning techniques determine location by using a measure called received signal strength indicator (RSSI), primarily for device multilateration or fingerprinting calculations. These signal strength-based approaches are easy to implement and cost-effective, but don't achieve high degrees of accuracy since signal strength can be influenced by the surrounding environment. RSSI solutions are also susceptible to errors introduced by objects moving within the environment, particularly people. The addition of other less common, more cutting-edge techniques can lead to more accurate Wi-Fi positioning results including Angle of Arrival (AoA) and Time of Flight (ToF).
In RSSI-based applications, multiple existing Wi-Fi access points or Wi-Fi enabled sensors deployed in a fixed position will detect transmitting Wi-Fi devices and the received signal strength of the signal from the device. This location data collected by APs or sensors is sent to the central indoor positioning (IPS) or real-time location system (RTLS). The location engine analyzes the data and uses multilateration algorithms to estimate the location of transmitting devices. Alternatively, the signal strength of nearby APs relative to a wireless device can be used to determine the device’s location.
Using an RSSI-based method with multilateration is the most easily activated and low-cost option for Wi-Fi positioning. However, it doesn't deliver a high degree of positional accuracy because it is subject to signal attenuation, absorption, reflection and interference.
Fingerprinting is also a RSSI-based method. Wi-Fi positioning via fingerprinting involves using a database which records the location and signal strengths of surrounding APs , as well the coordinates of a Wi-Fi device, such as a smartphone or tracking tag in an inactive phase. To create the fingerprinting database, a time-consuming calibration process is necessary which may need to be repeatedly performed. While actively tracking a device, the RSSI values are compared to these fingerprints in the database to estimate the prior trained device’s location.
Like determining location via signal strength and multilateration, fingerprinting doesn’t deliver a high degree of positional accuracy, unless the system is continuously calibrated to reflect environmental changes. It is a low-cost method for Wi-Fi positioning, but requires continuous updating to the trained RF-patterns in the database. Fingerprinting approaches are also affected by signal attenuation (most influence), absorption, reflection and interference.
ToF is a highly accurate indoor positioning method, used by precision technologies like UWB. This advanced technique can precisely measure distance between Wi-Fi devices by calculating the time it takes for signals to travel between devices.
ToF can be leveraged to detect the precise location of a Wi-Fi device using multiple sensors or access points. This requires a dense deployment of APs or sensors to detect a Wi-Fi device, such as a smartphone or tracking tag. To work properly, the sensors or APs need to be accurately synchronized to the same master clock. The signals of the Wi-Fi device will be received by the APs or sensors in the communication range and time-stamped. All the time-stamped data is then sent to the central IPS or RTLS. The location engine will analyze each anchor’s data and the differences in arrival times to each anchor and use multilateration to accurately calculate the tag’s coordinates.
While ToF enables more accurate Wi-Fi positioning, deploying this approach comes with a greater level of complexity, and may not be cost-effective in scenarios when high accuracy is not required. Wi-Fi Round-Trip-Time (Wi-Fi RTT) is an emerging method to determine the location of Wi-Fi devices that uses ToF. Specified in the IEEE 802.11-2016 standard, Wi-Fi RTT enables devices to measure the distance between devices with the round trip time it takes for a signal to travel between the devices. This calculation is based on the path traveled by the signal and determined using the transmission speed of the electromagnetic wave and the speed of light. This can be used for ranging between two devices or for indoor positioning using multiple APs or sensors with multilateration.
AoA is an advanced method which can deliver Wi-Fi positioning with enhanced accuracy compared to more traditional techniques like fingerprinting and RSSI. This is possible due to Multiple Input Multiple Output (MIMO) Wi-Fi interfaces. To be able to find direction, a mobile asset, such as a tag or beacon, with a single antenna transmits to a fixed Wi-Fi sensor with a multi-antenna array. The phase shift of the multiple antennas, as a result of receiving the signal, is measured and calculated to determine the angle of the transmitting mobile device and create an area of certainty of the object to be located.
One advantage of an AoA approach is that it reduces the number of necessary reference points. Instead of a minimum of three sensors as required for any multilateration approach, you only need two to create an unambiguous determination of position. Additional reference points add to the accuracy and reliability of the calculated positions. While indoor positioning via AoA is more accurate than signal strength approaches, solutions that leverage this technique are only just entering the market.
