Self-learning RTT fingerprinting in WiFi networks

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 a multilateration ranging-based solution. However, studies in the past reported the benefits in terms of positioning error when using fingerprinting instead of multilateration.

This Ph.D. is aimed at providing a thorough study of an RTT fingerprinting solution based on the time measurements reported by the IEEE 802.11mc solution, under a self-learning approach aimed at reducing or even removing the requirement for a previous off-line database setup and fed. 

Objectives

The goal of the Ph.D. is to analyze the 802.11mc facilities and define and implement an RTT fingerprinting solution that performs better than the multilateration approach being currently proposed.

Period

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

Funding

No funding is currently available for this position.

Participants

Supervisor: Israel Martin Escalona (GRXCA)

Student: To be assigned.

Further information

No additional information is provided.