Hello everyone, thanks for attending our talk.
I am Shengjie from Peking University, supervised by Prof. Zhang.
I am glad to be here to introduce our work,
WiBorder: Precise Wi-Fi based boundary Sensing via Through-wall Discrimination.
Toady, Wi-Fi signal is ubiquitous in our daily environments.
Lots of Wi-Fi based device-free human sensing applications have been proposed.
One of the most important Wi-Fi based applications is localization.
In recent years, many studies have been conducted to improve the localization accuracy.
However, so far, the maximum error of Wi-Fi based device-free localization can be as large as 2 meters.
Such localization error(accuracy) is insufficient for obtaining a Wi-Fi sensing boundary.
However, in real word scenarios,
many applications require a precise boundary
to determine whether a human target has entered the sensing areas.
For example, for intrusion detection,
an intrusion alarm needs to be raised immediately after someone entered a house.
It is also important for such application to avoid false alarms caused by human motion outside the house.
Thus, we would like to draw a clear line as boundary
to determine whether a human has entered a sensing house.
As another example, smart home applications need to know which area the sensing target is in,
so as to provide room-based services, like turning on light when a user enters the bathroom.
Therefore, such applications require us to determine several boundaries
to detect which area a human has entered.
In this work, different from previous localization work,
WiBorder aims to solve the critical problem of accurately determining a precise boundary.
The inspiration of WiBorder comes from the observation that,
in typical indoor environments, sensing areas are usually at room level, divided by walls.
These walls naturally form the boundaries between different spaces.
Take intrusion detection as an example,
the surrounding walls of a targeted house can be utilized to determine the sensing boundaries.
As for area detection in a smart home，
the partition wall between different rooms can be used to determine the sensing boundaries.
so the question is :
The Wi-Fi signal can penetrate walls,
How can we discriminate between inside-wall and outside-wall activities?
To answer the question, please consider the scenario that we have a pair of Wi-Fi devices.
When the human target takes activities inside the wall, he will introduce a direct reflected signal to the receiver.
If he moves to the outside, because of the wall blockage, such signals will turn into indirect signals.
To differentiate these two scenarios,
we have extracted the effective metric which indicates the human reflected signal strength.
By observing the metric inside or outside a wall,
we find that this metric drops significantly when the person moves from inside to outside.
Thus, by measuring the metric continuously in real-time,
we can determine whether a person is inside or outside a precise wall boundary.
Based on WiBorder, we have developed two case studies. The first one is an intrusion detection system.
We have deployed our system in a real nursing home.
This is the layout of one studio in the nursing home.
With a clear boundary, our system could give an alert as soon as the intrusion happens.
Apart from accurate detection, motions outside the area will not cause any false alarms for our systems.
Only the intruder enters the room, our system reports an alarm.
The second case study is an accurate area detection system.
The nursing studio are divided by walls into three areas;
Our system can detect whether the human is moving or still and in which area he is located.
The human’s activity log is also recorded automatically for the long-term monitoring.
We monitor a volunteer and illustrate his one-day activity log detected by our system.
The different colors represent different areas.
Combining with the time clock, we can infer that this volunteer went to bed at 10pm.
We can also induce his meal time according to his kitchen activities.
In conclusion,
We propose WiBorder to determine the sensing boundary
We propose a novel metric for through-wall signal discrimination
We also developed two case studies in real-life scenarios to demonstrate the effectiveness of WiBorder
Thanks for your listening.
I am happy to take any questions.