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Ubiquitous Wi-Fi Infrastructure for Indoor Positioning |
NEWS |
The IEEE 802.11mc standard, published in 2016, introduced a new positioning technique for Wi-Fi enabled devices. Superior to existing Received Signal Strength Indicator (RSSI)-based methods, Wi-Fi positioning estimated using Round Trip Time (RTT) leverages Fine Time Measurement (FTM), a feature supported in a select number of Wi-Fi Access Points (APs) from Aruba and Google, as well as newer Android devices, helping to achieve positioning accuracies between one and two meters. Further improvements in Wi-Fi positioning accuracy, as well as features such as ranging are expected with the release of the IEEE 802.11az standard later this year, are bringing Wi-Fi positioning closer in line with other competitive indoor positioning technologies.
Wi-Fi Positioning in the Larger Location Ecosystem |
IMPACT |
With Global Navigation Satellite System (GNSS) positioning not able to function under a roof, alternative solutions are needed for indoor positioning and navigation. Wayfinding in public spaces such as shopping centers, transport hubs, and hotels commonly leverage a mix of positioning technologies available on smartphones, compounding dead reckoning using Inertial Measurement Units, Bluetooth beacon-based proximity measurements, and Wi-Fi positioning. As a ubiquitous technology within these spaces, leveraging existing infrastructure (Wi-Fi APs and connectivity with all smartphones) to utilize Wi-Fi for indoor positioning is a common option but holds many drawbacks, namely the high-power usage and limited accuracy. FTM RTT aims to remedy the latter, using precise time measurements to accurately record the time taken for signals to travel between AP and device. This propagation time can be used to estimate a distance between the two, and with three or more AP’s, the position of the device can be determined through trilateration. Existing solutions using FTM RTT can achieve accuracies of 1.2 meters for static measurements and 1.3 meters for moving positions.
Despite widespread support from chipset vendors such as Qualcomm, u-blox, and Intel, support for the technique has seen limited adoption since its inception. Initially, it was driven by many of Google’s Wi-Fi efforts including their Nest devices (2020) and their Pixel Phones. On the infrastructure side, Compulab offers a single modified device which supports FTM RTT and, as of 2020, Aruba supports the technique across their range of Wi-Fi 6 access points as part of a push towards modern Wi-Fi positioning, which also includes APs with in-built GPS receivers to contribute to location. On the device side, FTM RTT is available to Android devices using OS version 9.0 upwards with support from many key Smartphone vendors such as Samsung, Xiaomi, Google, and LG as well as many enterprise devices from Zebra Technologies.
With the IEEE 802.11az standard, expect many future improvements to Wi-Fi positioning performance, referred to as Next Generation Positioning (NGP). Support for secure ranging for relative positioning from device to device, improved time-of-flight calculations for improved capabilities, and adjustments which align with Wi-Fi 6 features all promise to bolster not only the performance but flexibility of Wi-Fi positioning systems, addressing many requirements traditionally met by more precise Bluetooth Low Energy (BLE) or Ultra-Wideband (UWB) systems. Qualcomm’s investigations into Wi-Fi positioning suggest that further improvements to positioning and ranging can be acquired from iterating on the bandwidth, signal strength, sensitivity, and array size of antennae.
Limited Ecosystem Support Will Hamper Adoption |
RECOMMENDATIONS |
With the limited but increasing support for Wi-Fi RTT in the recent years, the question becomes whether the rest will follow. Unfortunately, this includes some of the largest vendors and industry trendsetters: Cisco and Apple, who have yet to introduce any support for the technology. Furthermore, issues with Wi-Fi positioning still remain, such as high-power usage and limited range, especially at 5GHz, which has traditionally hampered Wi-Fi positioning adoption. Nevertheless, ABI Research expects Wi-Fi to be a large proponent of indoor location with 1.13 million Wi-Fi positioning implementations worldwide by 2030 (see ABI Research’s Indoor Location/RTLS market data (MD-RTLS 104)).
With the ubiquitous nature of Wi-Fi installations within indoor environments and widespread support within devices, changes and improvements in Wi-Fi positioning will have profound effects on the ecosystem. Improvements found in 802.11az will increase the number of use-cases where Wi-Fi can compete with standalone BLE and UWB deployments, with FTM RTT providing accuracy more suitable for indoor navigation, asset tracking, proximity-based marketing, and more. Additionally, device to device ranging opens the possibility for Wi-Fi to be used for personal trackers, access control, and Vehicle-to-Everything (V2X) applications. Decision makers are expected to opt for the technology with not only familiarity but established infrastructure and device support for scalable deployments, though it remains to be seen whether the enhanced capability of NGP and this new standard will bolster the Wi-Fi positioning market. With the finalization of 802.11az expected at the end of this year, the impact of the standard will only be seen a few years from now.
With alternative solutions on the horizon, FTM RTT, and more broadly Wi-Fi positioning, solutions may fall to the wayside in favor of more available, efficient, and capable solutions. The 5G network open many possibilities which should be supported by smartphones and IoT devices across the board. Native 5G NR positioning, which is expected to achieve sub-meter accuracies with release 17 and 18, can be leveraged indoors using private deployments, such as Huawei’s pilot deployment in Suzou Station. Other solutions such as Polte’s cloud-based cellular and Polaris Wireless hybrid location technique all support widespread compatibility, seamless indoor-outdoor positioning, cloud-based computation for power efficiency, and comparable reliability and accuracy.