Hello everyone, I’m Yili Ren. I am here to present my work, LiquidSense, it is a joint
work done with my advisor Professor Jie Yang and other group members.
First, I’d like to introduce the background of WiFi applications in a smart home, especially,
the liquid level sensing.
In recent years, WiFi is becoming more and more intergraded into our daily life.
For example, the WiFi can connect the smart refrigerator, smart light, voice control system
and smart container to a smart device via a WiFi network.
Among those emerging services, sensing the liquid level in the containers has gained
increasing attention. Because liquid sensing provides the information
of when and how much the liquid content has been consumed each day.
It can help us to estimate and track the calories ingested, help patients to manage the medicines
and provide the expiration notification and even automatic reordering.
There are some research works in liquid level sensing. In general, the approaches can be
divided into four categories
First, capacitive-based approach. The capacitance sensor has very good sensitivity in liquids.
But they usually require the sensors to be immersed in the liquid.
Second, the camera-based approach, it could capture many containers at the same time.
But it only works for transparent containers and suffers from none-line-of-sight (NLOS)
or poor lighting scenarios.
Next approach is using pressure sensors. High accuracy can be achieved
But, these systems require containers to have flat bottoms and need calibration.
The last approach is acoustic-based. It requires specialized acoustic hardware to acquire the
accurate measurement.
In addition, the above-mentioned approaches all require additional communication hardware
for each container.
Now, I will introduce our solution LiquidSense, a new approach for sensing the liquid level.
The basic idea is as follow:
We reuse the existing home WiFi networks to sense the resonance frequency of the container-liquid
system for liquid level detection.
Our system uses a low-cost transducer attached to the container surface to generate a vibration.
The well-designed vibration allows the container to have high-frequency microvibrations at
its resonance frequencies.
Such microvibrations will introduce subtle changes to WiFi signals
Through detecting the subtle changes in the WiFi signals, LiquidSense extracts the resonance
frequency of the container for liquid level detection.
Now, I will discuss why WiFi signals can sense microvibrations of a container.
Assuming we have a transmitter and a receiver. We put a container at a proper distance and
the container is vibrating. Because of the vibration, there will be a small displacement
on the surface.
When the displacement is changing, the reflected path of WiFi is also changing. In particular,
it changes from Path-P to Path-P hat.
When Path-P becomes Path-P hat, the path length increases.
And the signal amplitude decreases. As long as the signal propagates one wavelength,
the phase changes by 2pi. So, the phase will also change.
So, we can conclude that both amplitude and phase of WiFi signals can indicate how the
signal paths are changed by the vibration.
Channel State Information describes how the WiFi signals are changed in the physical space.
So, changes in amplitude and phase of CSI for each OFDM subcarriers indicate changes
in amplitude and phase of WiFi signals.
But for our system, we focus on analyzing the phase component. This is because the phase
offset is more sensitive to subtle vibrations.
To better capture the feature, we adopt PCA by combining the all the subcarriers’ phase
information. We select the first principle component.
In order to extract accurate resonance frequency, we need to conduct STFT on the first principle
component.
We can see both the first and the second resonance frequency. But, the first one is much stronger.
We select the first resonance frequency as the feature.
Now, we are going to build two models. One is continuous, another one is discrete.
For continuous prediction, we adopt curve fitting. To achieve the best curve fitting,
we adopt the piecewise polynomial interpolation and cubic polynomial.
For discrete prediction, we adopt a SVM with linear function kernel.
We conduct experiments with two laptops. One is the receiver and the other one is the transmitter.
We use 5G WiFi and the packet rate is 2000.
We connect the transducer to the control board to adjust the transducer
We choose four commonly used containers in daily life. They are made up of metal, glass,
and ceramic.
The experiments are conducted in a typical home environment with six different types
of liquids.
Now, we show the overall performance for continuous model.
The first figure shows the CDF of errors. We can observe that 90% of the errors are
less than 6%.
In the second figure, the overall accuracy is great than 97%.
This shows our system is highly accurate in a continuous manner.
Our system also performs well in a discrete manner.
In particular, our system can achieve better performance at higher liquid levels. And the
overall precision, recall and F-score are larger than 0.96.
All in all, we propose a novel liquid sensing system.
Thank you for listening.