Reducing the IEEE 802.11mc location traffic overhead by using the passive TDOA algorithm

Description

Nowadays, indoor positioning is a still an open issue. The main reason, though not the only one, is that location indoors require accurate measurements, i.e. positioning errors of 1 to 2 meters in average. There are technologies, such as those based in ultra wideband (UWB) that are able to provide positions with only centimeters of uncertainty. However, those technologies require custom network and user equipment to work, which often make them costly and hard to deploy.

Lots of efforts have been addressed to use communication networks for location purposes. However, the problem then is that positioning errors rise (noticeably) over the 2 meters.

In the recent past, the Task Group mc (TGmc) of the IEEE 802.11 Working Group (also referenced as the IEEE 802.11mc) proposed some enhancements to the 802.11 protocol so that it provided accurate time-of-flight measurements suitable for indoor positioning. This amendment was introduced in the 2016 revision of the protocol, but it haven’t been adopted by main manufacturers until now. Recently, Google have dramatically supported this approach by allowing fine RTT measurements in any smartphone running Android 9.0. Other main actors in the WiFi field (such as Intel or Cisco) have implemented Fine Time Measurements in their WiFi cards and access points as well.

RTT location in IEEE 802.11mc is proposed as an active multilateration ranging-based solution. This means that location traffic needs to be injected (FTM request and responses) so that the RTT can be properly computed. Active location solutions are known to be not scalable (the larger the amount of stations being located, the lower throughput available for data services).

A new location technique for WiFi networks was presented few years ago to face this issue: passive TDOA. This technique uses the concept of 2-way TOA to compute the time of flight of WiFi signals, but in a passive way (i.e. minimizing the location traffic injection).

This project is aimed at providing a thorough analysis of an eventually implementation of the Passive TDOA algorithm using the IEEE 802.11mc facilities.

Objectives

The goal of this project is to analyze the performance of the passive TDOA algorithm when it’s fed with IEEE 802.11mc time measurements. This goal would involve the following stages:

1. Describing the use of IEEE 802.11mc facilities to support the passive TDOA algorithm.
2. Computing passive-TDOA like measurements from IEEE 802.11mc data.
3. Implementing the passive TDOA algorithm and tools for performance analysis.
4. Analyzing the performance of the 802.11mc-based passive TDOA.

Period

Start date: --
End date: --
Status: Open

Funding

No funding is provided for this project.

Participants

Supervisor: Israel Martin Escalona (GRXCA)

Student: To be assigned.

Further information

No additional information is provided.