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Proceedings ArticleDOI

Passive visible light networks: taxonomy and opportunities

21 Sep 2020-pp 42-47
TL;DR: In this survey, a taxonomy is proposed to analyze state-of-the-art contributions of artificial lighting and identifies the overarching principles, challenges, and opportunities of this new rising area.
Abstract: Artificial lighting has been used mainly for illumination for more than a century. Only recently, we have started to transform our lighting infrastructure to provide new services such as sensing and communication. These advancements have two key requirements: the ability to modulate light sources (for data transmission) and the presence of photodetectors on objects (for data reception). These requirements assume that the system has direct control over the transmitter and receiver, as in any traditional communication system. But not all lights can be modulated, and most objects do not have photodetectors. To overcome these limitations, researchers are developing novel networks that (i) exploit passive light sources that cannot be directly modulated, such as the sun, and (ii) leverage reflections from the external surfaces of objects to create a new generation of sensing and communication networks with visible light that is sustainable and does not require active control over the system. In this survey, we propose a taxonomy to analyze state-of-the-art contributions. We also identify the overarching principles, challenges, and opportunities of this new rising area.

Summary (3 min read)

1 INTRODUCTION

  • The Internet of Things (IoT) are enabling a new computing era, heavily depending on wireless communication and sensing.
  • Thus the authors can piggyback wireless communication on top of LED illumination almost for free.
  • This breakthrough has created a new range of exciting applications such as accurate indoor localization [9], high-speed Internet [16], interactive toys [20], etc. Limitations of active methods.
  • Visible light systems would be more impactful if the authors could interact with objects that do not have any photosensor.

2 TAXONOMY

  • To build passive visible light networks, researchers are studying methods for scenarios with (i) passive light sources, which do not modulate information; and (ii) passive objects, which do not have photodetectors.
  • To consolidate this nascent area, it is necessary to have a framework to identify the principles, challenges and opportunities of passive methods for exploiting visible light.

3.1 System Architecture and Applications

  • The architecture of passive VLS typically consists of three elements: light source, object, and receiver (RX).
  • Pulsar [31] uses two PDs (with different field-of-view) to exploit Angle-of-Arrival (AoA) methods.
  • If the RXs are placed in the environment and the light source can be modulated, Fig. 1(c), the information is obtained from the objects’ reflections.
  • Research studies show that many objects can be monitored with this type of architecture: fingers, cars and people, enabling applications such as trackpad, traffic monitoring, occupation detection, among others.
  • Besides monitoring people, passive VLS can also be used to monitor hand gestures.

3.2 Research Challenges

  • Based on their taxonomy and analysis of the SoA, below the authors describe the three most important challenges.
  • In these scenarios, the system performance is largely determined by the object’s properties, but objects have different shapes, sizes and reflection coefficients, as shown in Fig.
  • Passive VLS systems, on the other hand, cannot filter out these passive light sources because it relies on them for sensing (cases B and D).
  • Overall, loosing the ability to modulate a signal in passive VLS, creates challenges that can only be tackled with a more flexible and robust design at the reception end.
  • In scenarios with passive objects, the RXs have fixed locations and can only provide information when objects move under their limited field-of-view (FoV).

3.3 Research Opportunities

  • As discussed in the previous section, passive VLS systems expose unique challenges.
  • Below, the authors describe the research directions from the community to tackle these challenges.
  • There are research opportunities to reduce this training overhead (or even better, to remove it), without affecting sensing performance greatly.
  • A cross-like deployment of active ceiling lights is used to monitor human postures [10].
  • This approach is applied to Case B, where the light source is passive, but the object carries a photodetector.

4 BEYOND SENSING: PASSIVE VISIBLE LIGHT COMMUNICATION

  • Passive communication is more complex than passive sensing, because communication requires sending bits.
  • Thus, to achieve passive communication the authors need to find ways to modulate visible light without having control over the emitter.
  • The overarching vision of passive VLC is to have objects sense and process data, but instead of communicating this information actively, e.g. via a radio module, objects will adapt the reflective properties of their external surfaces according to the information they want to convey (like a chameleon).
  • Light waves impinging over the smart surfaces will create distinctive patterns, and photodetectors deployed in the surroundings will decode the reflected signals.

4.1 System Architecture and Applications

  • But there are two key differences: one on objects, the other on RXs.
  • RXs do not contain cameras, also known as Architectural Difference 2.
  • For the downlink, the active light transmits information to the surface with traditional VLC.
  • Sunlight communication [2, 3, 23], on the other hand, is a fully passive communication system for mobile objects.
  • In [2, 23], the objects are cars, whose roofs are covered with barcodes consisting of materials with different reflective properties.

4.2 Research Challenges

  • Passive VLC creates a new wireless channel that inherits many of the challenges encountered in passive sensing.
  • Compared to passive sensing, which exploits the default surface of objects, passive communication covers objects with surfaces having distinctive reflective properties.
  • Fine granularity is required to encode as much information as possible over the surfaces.
  • Compared to existing communication systems, passive VLC faces unique challenges due to the lack of TXs.
  • Second, changes in the object’s speed can distort symbols’ periods.

4.3 Research Opportunities

  • Passive communication with visible light is still in its infancy.
  • Below the authors describe the progress made by the community and the opportunities they foresee.
  • The design of smart surfaces requires a thorough analysis of various materials.
  • There are however other materials that could be used such as smart glasses or microblinds.
  • There are no solutions for the research problems introduced in Challenge.

6 CONCLUSION

  • Considering the increasing attention on exploiting visible light as a medium for sensing and communication, researchers are proposing novel passive monitoring methods to exploit the external surfaces of people, fingers, hands and cars.
  • In this paper the authors introduced a taxonomy to classify these various passive approaches.
  • The authors taxonomy allowed us to identify five macro challenges and eight general research directions in this nascent area.

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Delft University of Technology
Passive visible light networks
Taxonomy and opportunities
Wang, Qing; Zuniga, Marco
DOI
10.1145/3412449.3412551
Publication date
2020
Document Version
Final published version
Published in
LIOT 2020
Citation (APA)
Wang, Q., & Zuniga, M. (2020). Passive visible light networks: Taxonomy and opportunities. In
LIOT 2020 :
Proceedings of the 2020 Light Up the IoT, Part of MobiCom 2020
(pp. 42-47). Association for Computing
Machinery (ACM). https://doi.org/10.1145/3412449.3412551
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Passive Visible Light Networks: Taxonomy and Opportunities
Qing Wang
Delft University of Technology
Delft, the Netherlands
qing.wang@tudelft.nl
Marco Zuniga
Delft University of Technology
Delft, the Netherlands
m.a.zunigazamalloa@tudelft.nl
ABSTRACT
Articial lighting has been used mainly for illumination for more
than a century. Only recently, we have started to transform our
lighting infrastructure to provide new services such as sensing and
communication. These advancements have two key requirements:
the ability to modulate light sources (for data transmission) and the
presence of photodetectors on objects (for data reception). These
requirements assume that the system has direct control over the
transmitter and receiver, as in any traditional communication sys-
tem. But not all lights can be modulated, and most objects do not
have photodetectors. To overcome these limitations, researchers
are developing novel networks that (i) exploit passive light sources
that cannot be directly modulated, such as the sun, and (ii) leverage
reections from the external surfaces of objects to create a new gen-
eration of sensing and communication networks with visible light that
is sustainable and does not require active control over the system.In
this survey, we propose a taxonomy to analyze state-of-the-art con-
tributions. We also identify the overarching principles, challenges,
and opportunities of this new rising area.
CCS CONCEPTS
Networks Cyber-physical networks.
KEY WORDS
Passive visible light sensing, passive visible light communication,
taxonomy, applications, opportunities
ACM Reference Format:
Qing Wang and Marco Zuniga. 2020. Passive Visible Light Networks: Tax-
onomy and Opportunities. In Light Up the IoT (LIOT’20), September 21,
2020, London, United Kingdom. ACM, New York, NY, USA, 6 pages. https:
//doi.org/10.1145/3412449.3412551
1 INTRODUCTION
The Internet of Things (IoT) are enabling a new computing era,
heavily depending on wireless communication and sensing. These
wireless interactions mostly rely on the radio-frequency (RF) band.
We argue that in this new era, the visible light spectrum could play
a far greater role than that it is currently playing. To achieve this
goal, we must investigate new methods for passive visible light
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must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specic permission and/or a
fee. Request permissions from permissions@acm.org.
LIOT’20, September 21, 2020, London, United Kingdom
© 2020 Association for Computing Machinery.
ACM ISBN 978-1-4503-8099-7/20/09.
https://doi.org/10.1145/3412449.3412551
sensing and communication. Visible light is present everywhere
and is gaining signicant interest as a medium to connect things.
Thanks to advances in visible light communication (VLC), LEDs
can now be modulated to transmit data without aecting the il-
lumination. Thus we can piggyback wireless communication on
top of LED illumination almost for free. This breakthrough has cre-
ated a new range of exciting applications such as accurate indoor
localization [
9
], high-speed Internet [
16
], interactive toys [
20
], etc.
Limitations of active methods.
The above applications are
transforming the role of our lighting infrastructure, but they as-
sume two key requirements: light sources can be modulated to transmit
information and objects have photodetectors to receive that informa-
tion. These requirements limit how visible light can be exploited
for sensing and communication.
1) Limitation at the transmitter (TX) side. Many light sources
cannot be directly modulated, for example the sun , but it would be
transformative if we could leverage sunlight for communication.
Most of the optical radiation in our environments remains largely
unused, not only sunlight but also plenty of articial lighting. Cur-
rently, we mainly exploit passive optical radiation to harvest energy,
but we should also exploit it to convey information.
2) Limitation at the receiver (RX) side. Most objects do not have
photodetectors. Furthermore, even objects with photodetectors,
such as smartphones, are only useful when held with line-of-sight
(LOS) toward luminaries. This limitation is not present in RF sys-
tems. Visible light systems would be more impactful if we could
interact with objects that do not have any photosensor.
Focus on simple photosensors and incoherent light.
There
are two main types of sensors used for applications related to vis-
ible light: photodiodes and cameras. The focus of this paper is on
systems working with simple photodiodes. Cameras are more pow-
erful devices, however, they are power hungry, more expensive,
and pose threats to users’ privacy. Another focus of this paper is on
incoherent visible light. Coherent light, such as laser light, has been
exploited for sensing, such as Lidar. However, generating coherent
light is more expensive compared to exploiting existing incoherent
ambient light and articial light from LEDs. Therefore, there is an
increasing interest in the system’s community to develop low-end
IoT systems with simple photosensors and incoherent light.
2 TAXONOMY
To build passive visible light networks, researchers are studying
methods for scenarios with (i) passive light sources, which do not
modulate information; and (ii) passive objects, which do not have
photodetectors. These eorts however are loosely connected. To
consolidate this nascent area, it is necessary to have a framework
to identify the principles, challenges and opportunities of passive
methods for exploiting visible light.
42

LIOT’20, September 21, 2020, London, United Kingdom Qing Wang and Marco Zuniga
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

Figure 1: A taxonomy of visible light networks:
(a) full-active;
(b) passive-src;(c)passive-obj;(d)full-passive.
We propose a taxonomy that arranges all the eorts related to
visible light into
four cases
. This taxonomy allows us to identify the
main challenges and research opportunities of dierent applications
that exploit visible light for passive sensing and communication.
Next, we rst describe the traditional scenario (active light sources
and objects), and then describe unique properties of passive and
semi-passive scenarios. Our taxonomy is illustrated in Fig. 1, where
we refer to active sources as TXs, and passive sources as emitters.
Case A:
full-active (active source, active object). This is the most
popular VLC application. The goal is to transmit information from
an LED to an object. This goal is simple to attain because the light
source can be modulated and the object can decode this data reliably
thanks to having a photodetector with LOS toward the source.
Case B:
passive-src (
passive source
, active object). Passive light
sources change the problem fundamentally. Since we cannot modu-
late them as in Case A, the goal is not to transmit information from
LEDs to the object. The goal now is for the object to get information
about the environment by measuring uncontrolled changes in illumi-
nation. The information can still be measured directly by the object
because it has a photodetector, but the amount of information is
limited and depends solely on the dynamics of the scenario at hand.
Case C:
passive-obj (active source,
passive object
). Passive ob-
jects also change the problem fundamentally. Objects can no longer
get information about the environment as in Case B, because they
have no photodetectors. With passive objects, the photodetectors
need to be placed in the environment. Thus, the goal changes: in-
stead of having objects getting information about the environment,
now the environment gets information about objects. Having active
sources means that we can send ne grained pulses to get infor-
mation about objects, but these pulses are reected by the objects’
external surfaces, and thus, the received signals will be noisy. There
is no-line-of-sight (NLOS) between light xtures and RXs.
Case D:
full-passive (
passive source
,
passive object
). This is the
most complicated case. The presence of passive objects (photode-
tectors in the environment, not on the object), means that the goal
is the same as in Case C: get information about the object. However,
we don’t have an active source to modulate information. Thus, the
only source of information are the reections caused by the object’s
surface. This scenario leads to a compound problem: a noisy gener-
ation of information, because there is no active source; and a noisy
reception of information, because the signals are reected (NLOS).
In this paper, we refer to the cases passive-src, passive-obj, and full-
passive as
passive
systems. Furthermore, we classify them into two
groups based on their main objective: sensing or communication.
Next, we will rst discuss
Visible Light Sensing (VLS)
and then
Communication (VLC).
Table 1: Applications based on passive VLS
Active light source Passive light source
Active object - Indoor localization [9]
- Indoor localization [30, 31]
Passive object
- Localization [24]
- Virtual trackpad [29]
- Reconstruction of
users’ skeleton [10, 11]
- Localization [4, 18, 19, 32]
- Occupancy detection [27]
- Gesture detection [13]
- Event detection [5, 6]
3 PASSIVE VISIBLE LIGHT SENSING
3.1 System Architecture and Applications
The architecture of passive VLS typically consists of three elements:
light source, object, and receiver (RX). The light source can be any-
thing: an LED (which can modulate information), an incandescent
bulb (which cannot modulate information) or natural light sources
such as the sun (uncontrollable). The objects can be of any form: peo-
ple, cars, etc. The RX is a tiny box containing simple photodetectors,
such as photodiodes. Passive VLS is enabling many applications,
see Table 1. We describe them below based on our taxonomy.
Case B: passive-src. If the RX is placed on the object, Fig. 1(b),
the information is obtained from default changes in the light inten-
sity of emitters. A typical application in this case is passive indoor
localization, where there is no need to modify existing articial
illumination infrastructures. For example, Pulsar [
31
] uses two
PDs (with dierent eld-of-view) to exploit Angle-of-Arrival (AoA)
methods. The dierential response between the two PDs follows a
nonlinear function with the AoA, and that function can be used to
derive the PDs’ relative location to the emitters. Pulsar can achieve
a median error of 5 cm for 2D localization.
Case C: passive-obj. If the RXs are placed in the environment
and the light source can be modulated, Fig. 1(c), the information
is obtained from the objects’ reections. Research studies show
that many objects can be monitored with this type of architecture:
ngers, cars and people, enabling applications such as trackpad,
trac monitoring, occupation detection, among others. In Okuli [
29
],
the goal is to track a nger over a pad. A small LED (active source)
and two photodiodes are placed at one side of the pad, and the
system maps the location of the nger based on its reected light
intensity with a median error of 0.7cm. Passive VLS systems are
also being used to monitor people [
10
,
11
]. In this case the RXs
are embedded in the oor and ceiling luminaries send modulated
signals. Based on the distortions measured at the RXs, the system
can reconstruct a person’s posture.
Case D: full-passive. Like the previous case, in this system, the
information comes from reections. But the information is less
accurate because the system does not use modulated light sources,
Fig. 1(d). Still, researchers are developing interesting applications
leveraging people and hands as passive objects, enabling applica-
tions such as gesture recognition, event detection, occupation detection,
among others. CeilingSee can estimate the occupancy of rooms us-
ing ceiling luminaries that also act as receivers [
27
]. The changes
in reection perceived at the ceiling indicate the number of peo-
ple present in a room. With a similar approach, we could track a
single person within a room by deploying a grid of receivers on
the ceiling [
5
,
6
]. Besides monitoring people, passive VLS can also
be used to monitor hand gestures. SolarGest exploits ambient light
and transparent solar panel on a smartwatch to recognize six hand
gestures with an average accuracy of 96% [13].
43

Passive Visible Light Networks: Taxonomy and Opportunities LIOT’20, September 21, 2020, London, United Kingdom
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  
Figure 2: Challenge 1.
The object’s shape and reection properties
determine the direction and intensity of reected light, (a-b); its
size determines the amount of light blocked over the oor, (c-d).
3.2 Research Challenges
Based on our taxonomy and analysis of the SoA, below we describe
the three most important challenges.
Challenge 1:
No control over the objects’ shape, implies no one-
size-ts-all solutions. In active scenarios, it is customary to use
predened modulation methods to communicate with any object
that carries photodetectors. However, in scenarios where objects do
not carry a photodetector (cases C and D), we can only rely on the
object’s external surface for sensing. In these scenarios, the system
performance is largely determined by the object’s properties, but
objects have dierent shapes, sizes and reection coecients, as
shown in Fig. 2. The object’s shape determines the direction of
reected light; its size determines the amount of blocked light; and
its reection coecient, or even cleanness, aect how much light is
reected to the RX. There is no ‘standard’ object to be sensed. Thus,
before designing a passive VLS system, it is central to gain a deep
understanding about the object at hand, to design a tailored system.It
is challenging to design one-size-ts-all solutions that can monitor
dierent objects accurately.
Challenge 2:
No control over the emitters, requires designing more
exible and robust methods for reception. In scenarios with active
light sources, RXs are designed to focus on the specic range of fre-
quencies and intensity-levels modulated by the active light xtures.
The eect of other (passive) light sources is ltered out via hardware
or software. Passive VLS systems, on the other hand, cannot lter
out these passive light sources because it relies on them for sensing
(cases B and D). But we cannot control the intensity, location or any
other property of emitters. Thus, receivers in passive VLS need to
work well under a wider range of optical frequencies and intensities.
Furthermore, similar to Challenge 1, where the lack of control over
objects requires a deeper understanding of reections; in this case,
the lack of control over emitters requires a deeper understanding
of the expected illumination conditions (to ne-tune the design
of algorithms). Overall, loosing the ability to modulate (control) a
signal in passive VLS, creates challenges that can only be tackled
with a more exible and robust design at the reception end.
Challenge 3: Monitoring passive objects, requires a high-density
of receivers. In scenarios where photodetectors are placed on top
of objects, the RX moves along with the object, and thus, can pro-
vide continuous sensing. In scenarios with passive objects, the
RXs have xed locations and can only provide information when
objects move under their limited eld-of-view (FoV). To cover a
large sensing area and/or provide ne-grained results, more RXs
are indispensable. These denser deployments require not only a
careful analysis to reduce the number of RXs while guaranteeing
a minimum performance level, but also require designing more
energy-ecient RXs to minimize the overall energy footprint.
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
Figure 3: Map the research opportunities to the challenges
3.3 Research Opportunities
As discussed in the previous section, passive VLS systems expose
unique challenges. Below, we describe the research directions from
the community to tackle these challenges. Fig. 3 summarizes our
ndings by mapping challenges to research directions.
Research Direction 1:
Train the system based on the particular
shape of the object-of-interest with low overhead. This approach is
applied to Cases C and D, where the object is passive. To cope
with the unique reecting properties of dierent objects (Chal-
lenge 1), researchers rst create a training database. For example,
in CeilingSee [
27
], the authors analyze the correlations between
the number of people present in a room and the subtle light dis-
tortions they cause. These correlations are then used to estimate
occupancy levels. Similarly, SolarGest relies on training for hand
gesture recognition [
13
]. These two examples work on scenarios
with passive lights (Case D). But scenarios with active lights (Case
C) can also benet from a training phase. Okuli [
29
] exploits the
fact that ngers have circular shapes and reect light uniformly,
to create a database mapping RSS with 2D locations on a pad. The
main limitation of training is the overhead involved in creating
and maintaining training sets. There are research opportunities to
reduce this training overhead (or even better, to remove it), without
aecting sensing performance greatly.
Research Direction 2:
Design and deploy light sources in a smarter
manner. This approach is applied to Case C, where the light is ac-
tive but the object is passive. To compensate for the lack of control
over the passive object (Challenge 1), the design and deployment of
active lights can be tailored to improve the system performance. For
instance, a cross-like deployment of active ceiling lights is used to
monitor human postures [
10
]. And not only can standard luminaries
benet from a smart design and deployment. In Okuli [
29
], an LED
light is mounted on a custom-made structure to control the light
reected by a nger on a tracking pad. There are two important as-
pects to be considered with these approaches: (i) the overhead and
costs associated with modied light xtures, and (ii) the balance
between sensing and illumination. Contrary to RF systems where
changes in output powers are not perceived by users, the changes
of lighting infrastructure must not aect user experience.
Research Direction 3:
Train the system based on the inherent
properties of some passive emitters. This approach is applied to Case
B, where the light source is passive, but the object carries a pho-
todetector. Some light sources have inherent properties that can be
sensed by the object’s photodetector. LiTell uses this approach to
achieve sub-meter indoor localization based on unmodied (pas-
sive) uorescent bulbs [
30
]. First, photodiodes are used to measure
the specic frequency of each uorescent bulb in an indoor space.
44

Citations
More filters
01 Jan 2009
TL;DR: This paper presents an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment and shows that the recall of the DfP system increases by more than 10% when using the proposed MLE without affecting the system's precision.
Abstract: Device-free Passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades significantly when tested in a real environment. We present an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment. Our results show that the recall of the system increases by more than 10% when using the proposed MLE without affecting the system's precision.

164 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an approach for performing the tasks of identification and sensing, applying visible light sensing (VLS) based on light emitting diode (LED) illumination and utilizing retroreflective foils mounted on a moving object.
Abstract: Identification and sensing are two of the main tasks a wireless sensor node has to perform in an Internet of Things (IoT) environment. Placing active powered nodes on objects is the most usual approach for the fulfillment of these functions. With the expected massive increase of connected things, there are several issues on the horizon that hamper the further deployment of this approach in an energy efficient, sustainable way, like the usage of environmentally hazardous batteries or accumulators, as well as the required electrical energy for their operation. In this work, we propose a novel approach for performing the tasks of identification and sensing, applying visible light sensing (VLS) based on light emitting diode (LED) illumination and utilizing retroreflective foils mounted on a moving object. This low cost hardware is combined with a self-developed, low complex software algorithm with minimal training effort. Our results show that successful identification and sensing of the speed of a moving object can be achieved with a correct estimation rate of 99.92%. The used foils are commercially available and pose no threat to the environment and there is no need for active sensors on the moving object and no requirement of wireless radio frequency communication. All of this is achievable whilst undisturbed illumination is still provided.

6 citations

Journal ArticleDOI
TL;DR: In this article, the rotation of a robotic arm can be accurately monitored by VLS simply by equipping the robotic arm with sequences of colored retroreflective foils, and the sensing task is compatible with a modulation of the light.
Abstract: With the rise of LED (light-emitting diode)-based luminaires, artificial lighting has become a technology platform, which, besides providing illumination, also provides communication and positioning functionalities. Apart from this, most recently Visible Light Sensing (VLS), in which lighting is used for sensing purposes, emerged as another embodiment of functionalities lighting could take over in the future. Here we show that machine learning assisted VLS has promising potentials to become a meaningful enabler for the Industrial Internet of Things. We show that the rotation of a robotic arm can be accurately monitored by VLS simply by equipping the robotic arm with sequences of colored retroreflective foils. Moreover, we show that the sensing task is compatible with a modulation of the light. This paths the way that sensing and communication tasks can be performed in parallel with one and the same low-complexity infrastructure, that apart from this also could take over the task of the obligatory room lighting. We demonstrate the capability of the approach even if the overall illumination conditions change. Therewith, VLS accentuates as an alternative option for industrial robot monitoring in combination with optical wireless communication.

5 citations

Proceedings ArticleDOI
08 Jul 2021
TL;DR: In this paper, the authors present a novel approach for establishing human-system interaction without the necessity for any active sensor or component to be worn by the user by employing a retroreflective foil attached to the wrist of the user in combination with a low-complexity electronic transceiver unit.
Abstract: By utilizing the technology of Visible Light Sensing in a non Line-of-Sight setup, we present a novel approach for establishing human-system interaction without the necessity for any active sensor or component to be worn by the user. In our proposed system, different wrist postures can be determined by employing a retroreflective foil attached to the wrist of the user in combination with a low-complexity electronic transceiver unit. Our solution approach is verified in two experimental setups and the achieved results depict that our solution cannot only determine the wrist postures with a fine resolution in terms of rotation angle, but that furthermore the rotation direction of the wrist as well as the speed of the rotation movement can be ascertained. In an exemplary real world application, we demonstrate how our system can be used to control the dimming of the room lighting. Moreover, our proposed solution approach combines the obligatory room illumination task of luminaires with a precise sensing functionality and can therefore be easily implemented with low installation effort.

3 citations

Journal ArticleDOI
TL;DR: In this article , a dual-cell liquid crystal shutter (DLS) was proposed for data transmission as a green option for wireless communications, which adopted time division multiplexing and polarization-based modulation to boost the data rate and eliminate the flickering effect.
Abstract: Solar energy is widely used for electricity generation, heating systems, and indoor environment daytime illumination. Indeed, large amounts of sunlight energy remain insufficiently used. In this work, we aim at employing sunlight energy for data transmission as a green option for wireless communications. Being emitted by an uncontrollable source, taming the sunlight is a challenging task that requires appropriate technologies to manipulate incident light. Liquid crystal devices are switchable glass technologies that have adequate response time and contrast characteristics for such an application. In this regard, we design a novel dual-cell liquid crystal shutter (DLS) by stacking two liquid crystal cells that operate in opposite manners, and we build our sunlight modulator with an array of DLSs. Then, we adopt time division multiplexing and polarization-based modulation to boost the data rate and eliminate the flickering effect. In addition, we provide mathematical modeling of the system and study its performance in terms of communication and energy consumption. Finally, we introduce some numerical results to examine the impact of multiple parameters on the system's performance and compare it with the state-of-the-art, which showed that our system features higher data rates and extended link ranges.

2 citations

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409 citations


"Passive visible light networks: tax..." refers background in this paper

  • ...Active object - Indoor localization [9] - Indoor localization [30, 31]...

    [...]

  • ...This breakthrough has created a new range of exciting applications such as accurate indoor localization [9], high-speed Internet [16], interactive toys [20], etc....

    [...]

Journal ArticleDOI
Roy Want1
TL;DR: Radio frequency identification has attracted considerable press attention in recent years, and for good reasons: RFID not only replaces traditional barcode technology, it provides additional features and removes boundaries that limited the use of previous alternatives.
Abstract: Radio frequency identification has attracted considerable press attention in recent years, and for good reasons: RFID not only replaces traditional barcode technology, it also provides additional features and removes boundaries that limited the use of previous alternatives. Printed bar codes are typically read by a laser-based optical scanner that requires a direct line-of-sight to detect and extract information. With RFID, however, a scanner can read the encoded information even when the tag is concealed for either aesthetic or security reasons. In the future, RFID tags will likely be used as environmental sensors on an unprecedented scale.

395 citations


"Passive visible light networks: tax..." refers background in this paper

  • ...It is similar to the backscattering concept used by RFID, but more energy efficient because it piggybacks on lights that are already on, and more secure because light is more directional....

    [...]

  • ...These concepts aremainly inspired by backscatter communication where passive tags modulate the electromagnetic waves emitted by external sources, traditionally used in RFID [25] and recently applied to other radio technologies, e.g. Wi-Fi [7] and TV signals [12]....

    [...]

  • ...Recently, researchers have also been able to track multiple objects passively with existing radio signals [1, 17], and can even identify the material type and image the horizontal cut of targeted object with RFID signal [21]....

    [...]

  • ...These concepts aremainly inspired by backscatter communication where passive tags modulate the electromagnetic waves emitted by external sources, traditionally used in RFID [25] and recently applied to other radio technologies, e....

    [...]

Proceedings Article
04 May 2015
TL;DR: WiTrack2.0 is presented, a multi-person localization system that operates in multipath-rich indoor environments and pinpoints users' locations based purely on the reflections of wireless signals off their bodies.
Abstract: We have recently witnessed the emergence of RF-based indoor localization systems that can track user motion without requiring the user to hold or wear any device. These systems can localize a user and track his gestures by relying solely on the reflections of wireless signals off his body, and work even if the user is behind a wall or obstruction. However, in order for these systems to become practical, they need to address two main challenges: 1) They need to be able to operate in the presence of more than one user in the environment, and 2) they must be able to localize a user without requiring him to move or change his position. This paper presents WiTrack2.0, a multi-person localization system that operates in multipath-rich indoor environments and pinpoints users' locations based purely on the reflections of wireless signals off their bodies. WiTrack2.0 can even localize static users, and does so by sensing the minute movements due to their breathing. We built a prototype of WiTrack2.0 and evaluated it in a standard office building. Our results show that it can localize up to five people simultaneously with a median accuracy of 11.7 cm in each of the x/y dimensions. Furthermore, WiTrack2.0 provides coarse tracking of body parts, identifying the direction of a pointing hand with a median error of 12.5°, for multiple users in the environment.

307 citations


"Passive visible light networks: tax..." refers background in this paper

  • ...Recently, researchers have also been able to track multiple objects passively with existing radio signals [1, 17], and can even identify the material type and image the horizontal cut of targeted object with RFID signal [21]....

    [...]

Proceedings ArticleDOI
Tianxing Li1, Chuankai An1, Zhao Tian1, Andrew T. Campbell1, Xia Zhou1 
07 Sep 2015
TL;DR: This work designs light beacons enabled by VLC to separate light rays from different light sources and recover the shadow pattern cast by each individual light, and designs an efficient inference algorithm to reconstruct user postures using 2D shadow information with a limited resolution collected by photodiodes embedded in the floor.
Abstract: We present LiSense, the first-of-its-kind system that enables both data communication and fine-grained, real-time human skeleton reconstruction using Visible Light Communication (VLC). LiSense uses shadows created by the human body from blocked light and reconstructs 3D human skeleton postures in real time. We overcome two key challenges to realize shadow-based human sensing. First, multiple lights on the ceiling lead to diminished and complex shadow patterns on the floor. We design light beacons enabled by VLC to separate light rays from different light sources and recover the shadow pattern cast by each individual light. Second, we design an efficient inference algorithm to reconstruct user postures using 2D shadow information with a limited resolution collected by photodiodes embedded in the floor. We build a 3 m x 3 m LiSense testbed using off-the-shelf LEDs and photodiodes. Experiments show that LiSense reconstructs the 3D user skeleton at 60 Hz in real time with 10 degrees mean angular error for five body joints.

212 citations


"Passive visible light networks: tax..." refers background or methods in this paper

  • ...users’ skeleton [10, 11] - Localization [4, 18, 19, 32]...

    [...]

  • ...This high density enables the required granularity to track the movements of limbs [10]....

    [...]

  • ...instance, a cross-like deployment of active ceiling lights is used to monitor human postures [10]....

    [...]

  • ...Passive VLS systems are also being used to monitor people [10, 11]....

    [...]

Frequently Asked Questions (2)
Q1. What are the contributions mentioned in the paper "Passive visible light networks: taxonomy and opportunities" ?

Only recently, the authors have started to transform their lighting infrastructure to provide new services such as sensing and communication. In this survey, the authors propose a taxonomy to analyze state-of-the-art contributions. 

The authors envision in the future, passive sensing and communication with light will enable a new generation of IoT systems, one that will connect everyday objects with the vast number of passive light sources in their environments.