Hi my name is Zerina and I'm a PhD student at the University of Washington
Today I am going to be presenting our work called Glaze, which is all about how we can
overlay data on top occupied spectrum.
We are all aware of the vast amount of IoT deployments over recent years which are continuing
to rapidly increase. We see them in our homes cities, even large industries such as agriculture
And while they provide huge benefits there are also a few drawbacks
Specifically, around the cost and maintenance of IoT systems.
They can get expensive when we consider the cost of base station hardware and antennas.
Not to mention tower space and management that quickly bring up the cost.
The other challenge is that most IoT devices uses batteries and require routine maintenance.
So the question we ask is can we utilize existing infrastructure and minimize battery usage
to connect IoT devices?
Ambient backscatter is a relatively new technology and is a promising because it leverages existing
wireless signals, is low-power, and can enable both peer-to-peer and uplink communication.
In fact, it's so low power that you could even have sensor nodes that are completely
battery-less.
However, to have an end-to-end IoT system we need both uplink and downlink communication
Glaze helps to bridge this gap by providing a new solution to enable low-power downlink
communication for ambient backscatter systems.
Since TV transmissions already exist, they can be used to carry downlink data. Similarly,
FM transmissions or Wi-Fi could be used the same.
But we don't want to require power consuming Wi-Fi or FM receivers at the endpoint devices.
To transmit additional data, using Wi-Fi signals for example, without requiring a Wi-Fi receiver
at the endpoint device. We overlay low rate data transmissions on top of the signals.
The endpoint device then only needs a simple low-power radio to decode the low-rate Glaze
downlink message.
To avoid making changes in the transmitting radio hardware or firmware, we propose connecting
a Glaze module to the antenna port of wireless radios to overlay data on top of the RF signal.
A glaze module can connect to the antenna port of a Wi-Fi access point to encode downlink
data transmissions for ambient backscatter devices, while Wi-Fi packets are still being
transmitted and received by legacy receivers.
But, enabling this is non trivial.
We need to ensure that the data we overlay does not cause channel degradation
It needs to operate at low-power
Lastly, how will we overlay data on signals that use packetized protocols? For instance,
Wi-Fi which sends data in bursts.
We design the Glaze module to address this challenges which has four key components.
First, a signal detector to monitor for incoming transmissions
A parameter optimizer to optimizes encoding parameters based on signal type and channel
conditions.
A traffic predictor that predicts incoming bursts of traffic for Wi-Fi signals using
a two state Markov model
Lastly, the glaze embedder used to overlay data on top of the RF signals by introducing
small perturbations in the form attenuation. A 0 bit is encoded by introducing attenuation
and 1-bit is encoded by making no change to the signal.
So how do we receive the data encoded by the Glaze module at low-power?
We design a low-power Glaze receiver that has three key components
A completely passive envelope detector to get the baseband signal
A RC circuit to compute a dynamic threshold based on the input signal rather than using
a more power consuming ADC
Lastly a low power micro processing unit to decode the data
Here we can see the prototype implementations of the Glaze module and receiver, which we
use to evaluate the Glaze system for three different signals: TV, FM, and Wi-Fi.
The Glaze module prototype uses off the shelf components while the glaze receiver is a custom
built PCB
Next I will go over some results, but due time, will only cover a few things.
The first thing we evaluated is the impact towards the original signals. To perform this
evaluation we looked at a couple different metrics depending on the signal. For example,
the perceptual evaluation of speech quality for FM which is what I am going to show you
today.
Here is an example of evaluating FM audio quality after data embedding. We look at the
PESQ with respect to RSSI for demodulated FM audio after Glaze embedding compared to
a baseline that has no embedding.
The PESQ can range 0 to 4 and a score greater than 1 is considered sufficient for human
hearing.
We performed this evaluation by recording an FM broadcast signal using a software defined
radio. The signal was then retransmitted with the Glaze module connected to the antenna
port of the SDR to embedded data.
We can see that the PESQ remains very high for all amounts of attenuation and starts
to drop off around -70�75dBm, similar to the baseline signal
The impact glaze has on TV and and Wi-Fi is similar and these results can be found in
the paper
The final result I will show is the performance of Glaze itself.
We evaluating BER with respect to distance in meters for Wi-Fi, TV, and FM signals�
for a bit rate of 10kbps.� A SDR is used transmit each signal at 20dB. Wi-Fi was transmitted
at 2.4GHz and it was impractical to emulate FM and TV broadcast towers so we transmitted
at 915Mhz for those cases. The Glaze module is connected to the antenna port of the SDR
to embed data and the Glaze receiver is used to decode.
We can see that Wi-Fi has a range limitation of 4 meters while TV and FM work up to 10
meters. This makes sense and fundamentally, Wi-Fi has a shorter communication range in
comparison to FM and TV just by considering the carrier frequency.
We would also expect that in a real-life scenario the communication range for broadcast signals
like TV or FM would be significantly larger. An estimate communication range for FM and
TV broadcast scenarios would be 125km and 75km, respectively.
To conclude, I present a high level overview of the Glaze system which can be used to enable
a new channel for downlink communication. I recommend taking a look at the paper for
more details on our system
Thank you!