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COMP4336/9336 Mobile Data Networking
This is the complete specification of the term project. You are encouraged to discuss the project or
any questions in the Project Forum in Moodle.
Topic
Location Identification with WiFi Fingerprinting
Aim
Distance-dependent path loss of wireless signals, together with dense deployment of WiFi in public
spaces enable WiFi to be used as a tool for localising people in indoor environments. In this project
the students will develop and implement algorithms that will enable location identification using
signals available from in-situ public WiFi infrastructure.
Background on WiFi Fingerprinting
As GPS does not work inside buildings, indoor localisation remains a challenge. Given that WiFi is
densely deployed in public spaces, it could be potentially used as a free localisation solution when
indoors. There are many different techniques to use WiFi signals for localisation, but the one that is
widely pursued is based on a technique called WiFi Fingerprinting.
As we know, wireless signals are affected by distance (attenuation) as well as reflecting objects in
the environment (multipath). We have learned that the multipath structure is very sensitive to the
location of the receiver, a small move can cause a small-scale fading, which will ultimately affect the
received signal strength (RSS) due to constructive or destructive interference with the original signal.
We have also learned and observed that Tx-Rx distance directly affects the RSS. Thus, if the receiver
(WiFi Client) changes its location, the Tx-Rx distance may also change, causing changes in RSS.
Consequently, WiFi RSS can help fingerprinting a location, i.e., we can potentially expect that a given
location can be uniquely identified by a unique RSS value.
However, we have also learned that RSS is unstable and fluctuates a lot even for the same distance
and location due to many random interferences in the environment. Thus, in practice, its challenging
to identify locations uniquely using RSS, especially if using a single WiFi AP as a reference for RSS
measurements. The goal of this project is to explore the potential of using multiple WiFi APs for
more reliable identification of locations, i.e., WiFi fingerprinting will be based on RSS data from
multiple Aps, instead of one. This is possible given the dense deployment of WiFi in urban
environment. For example, it is common to receive beacons from tens of WiFi APs in a shopping
mall or a university campus.
Hardware Requirements
This project can be completed using a laptop. WiFi RSS data can be collected using Wireshark as
learned in the lab experiments.
Programming Requirements
Some basic programming is involved to process WiFi RSS data and implement algorithms that can
identify locations from the WiFi RSS data. There are no restrictions on the choice of programming
language (Python, MATLAB etc. are all OK).