Publication: Visible light communication networks for IoT and its applications
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2020-05
Defense date
2020-06-03
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Abstract
Visible Light Communication(VLC) has emerged in the last years as a new way to
communicate. Using the existing lighting infrastructure, it has great potential to provide
high bandwidth and communication security, making it a strong alternative against
conventional Radio Frequency (RF) communications. Although VLC addresses several
of the problems that RF communications have for specific scenarios, its potential for
Internet-of-Things (IoT) applications must still be unleashed.
IoT deployments are, by nature, limited in some way. The limitation could be given by
the hardware used, as the cost may need to be minimal to have dense realistic deployments;
the energy available, which depends on the battery size of the device; and computing
power available, which is given by the available energy and the processing power of
the device, among others. Therefore, there is an interest in studying the advantages,
drawbacks, and limitations of integrating VLC in IoT scenarios with the constraints
mentioned above. This is especially necessary in the case of real-life deployments, mainly
if the devices used are multi-purpose, and they need to perform other tasks, such as
sensing, in addition to communicating.
First of all, VLC deployments for IoT use the available dense lighting infrastructure to
achieve communication on top of illumination in indoor scenarios. The advantage of such
an approach is that it allows reusing the existing infrastructure, improving the coverage,
and the energy consumed. Although VLC is energy efficient, it consumes more than just
illuminating. If the luminaries are not correctly controlled, energy could be wasted by
transmitting from a luminary with little or no effect into the receiver. In DenseVLC, we
explore the energy consumption of luminaries in dense deployments, and we propose an
approach to optimize the Signal-to-Interference-plus-Noise Ratio (SINR) given an energy
budget. In order to do so, we propose to coordinate the transmission done by several
independent devices concurrently. We introduce a novel synchronization method that
uses the Non Line-Of-Sight (NLOS) component of the signal to tackle this problem. Our
approach can improve the average system throughput by 45%, or improve the average
power efficiency by 2.3 times, compared to existing solutions.
Secondly, even if the required infrastructure follows the design presented above, IoT
deployments still need to face one fundamental problem: power management at deployed mobile devices. Having batteries increases the price of the device, its size, the maintenance
required, and the ecological impact that the product has. Removing the battery while
still being able to operate under realistic circumstances would be desired. In this work,
we study what limitations such a system has. We then propose a new communication
scheme, combining VLC and RF backscattering, that allows having continuous end-toend
communication with a custom-designed battery-free device. We design the hardware,
software, and protocol that optimizes each aspect of the system to decrease the power
requirement of each component. Finally, we evaluate our system and show that it can
run with consumptions as low as 95μW, transmit continuously at 500 bits/second, and
achieve more than 20 meters on backscattering distance, even with blockage elements as
glass and walls covering the Line-Of-Sight (LOS).
Thirdly, we explore one of the multiple applications that the designed VLC systems
for IoT allow to implement; device positioning. The majority of the literature requires
to have multiple transmitters and/or receivers to achieve localization. The objective of
this work is to localize with the minimum amount of necessary hardware, which is critical
for IoT applications. We investigate how VLC systems could be used for positioning
in dynamic scenarios. Exploiting the fact that, in our scenario, the transmitter and
receiver are relatively moving, we propose a mathematical solution that, just using one
VLC transmitter and one receiver both equipped with a compass, computes the correct
relative position. We then implement our solution in a modified version of OpenVLC and
achieve accuracies with less than 5 cm of error. Nevertheless, in this work we assume that
the NLOS is non-existant, which may not always be the case.
Finally, we try to overcome the problem mentioned above of NLOS reflections for
device positioning with a low resource consumption NLOS component detector. Similar
work try to solve this problem computing the Channel Impulse Response (CIR), but for
systems with limited resources this is impractical because 1) The VLC front-end may
not be fast enough for acquiring required data for the CIR calculation or 2) IoT boards
are not able to run computationally expensive algorithms in real time. In this thesis,
we propose a solution that in complex environments, reduces the localization error using
LEDs up to 93%.
In order to perform experimental research in VLC for IoT, a research platform is
needed. In this thesis, we also present the latest version of an open-source, softwarebased,
VLC platform, OpenVLC. OpenVLC was first introduced as part of the thesis of
Dr. Qing Wang. During this thesis, the platform has been re-designed on both hardware
and software. The throughput improved more than 23 times and the transmission distance
increased by a factor of 4. In this thesis, OpenVLC, parts of it, or modified versions have
been used as a framework to create new IoT systems and explore the practical side of
VLC.
As a summary, in this work, we explore how VLC can be leveraged for IoT deployments. We study the features of such real-world deployments from different
perspectives in a variety of scenarios, and we show that realistic implementations of VLC
systems are not only possible but doable, enabling new features that IoT developer can
exploit.
Description
Mención Internacional en el título de doctor
Keywords
Visible Light Communication (VLC), Internet-of-Things (IoT), Signal-to-Interference-plus-Noise Ratio (SINR), Non Line-Of-Sight (NLOS), OpenVLC