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Delft University of TechnologyMaster’s Thesis in Embedded Systems

Networked Indoor Lighting Controls withVisible Light Communication

Kevin Warmerdam

Networked Indoor Lighting Controls with Visible

Light Communication

Master’s Thesis in Embedded Systems

Embedded Software SectionFaculty of Electrical Engineering, Mathematics and Computer Science

Delft University of TechnologyMekelweg 4, 2628 CD Delft, The Netherlands

Kevin Warmerdam1505343

[email protected]

September 10, 2015

mailto:[email protected]

AuthorKevin Warmerdam ([email protected])

TitleNetworked Indoor Lighting Controls with Visible Light Communication

MSc presentationSeptember 23, 2015

Graduation CommitteeProf. Dr. Koen Langendoen (chair) Delft University of TechnologyDr. Zaid Al-Ars Delft University of TechnologyDr. Ashish Pandharipande Philips ResearchDr. Marco Zuniga Delft University of Technology

mailto:[email protected]

Abstract

Intelligent lighting systems employ dimmable luminaires, photosensors, andoccupancy sensors to adapt to daylight and user presence conditions in in-door environments. By providing the illumination required for users and nomore, significant energy savings can be made. The state of the art in theselighting systems currently relies on dedicated communication hardware suchas radio networking modules. Additionally, the state of the art relies on pa-rameters specific for the environment to be known called the optical channelgains. Although these may be measured in a calibration step while the sys-tem is offline, occupants interacting with the environment affect the opticalchannel gains. Currently, such environment changes can compromise thedesired control behavior of intelligent lighting systems.

Visible light communication (VLC) presents an alternative to radio com-munication in networked lighting control systems. It reuses the system’sluminaires as transmitters and its photosensors as receivers. This way, ded-icated communication hardware is no longer required. Furthermore, thereception of signals on the optical channel between luminaires and photo-sensors allows for the estimation of the optical channel gains. By estimatingthese during communication, the system becomes adaptable to changes inthe environment.

The proposed system is evaluated against the state of the art in radio-networked lighting control using simulations as well as an experimentaltestbed. The VLC-networked lighting control system is shown to be resilientagainst changes to the environment which the state of the art systems arecompromised by.

Preface

In the field of indoor lighting, energy efficiency and user comfort are the twoconflicting goals. An optimum between the two exists where the desired lightis present, composed of both daylight and just the right amount of artificiallight. Since it is not expected of the user to employ a dimming switch andconstantly minimize the artificial light, depending on the amount of sunlightentering his or her room, indoor environments are to this day often eitherfully lit with the maximum power output provided by its overhead lamps orthese are entirely turned off. One speculates to what degree users are evenwilling to switch off lights when leaving such rooms for any period of time.The desired optimum calls for the automation of lighting systems, whereluminaires are dimmed based on daylight and occupancy conditions. Thisthesis proposes that user comfort may be guaranteed while energy costs maybe minimized.

The work of this thesis was done at a company, namely Philips Research inEindhoven. The history of Philips can be traced back to the 19th century,when it began the production of incandescent lamps which would eventuallygive Eindhoven the identity of ‘Lichtstad’ (City of Light). Where better toexplore intelligent lighting systems for a master’s thesis than here?

Before the underlying challenges and novel solutions within intelligent light-ing systems are revealed, allow me to express my earnest gratitude to severalparties: to Ashish Pandharipande and Marco Zuniga for their supervisionfrom near and far, respectively; to my parents for their prolonged supportwhich has culminated into this conclusion of my studies; and to my girl-friend Lotte, who shared the move to Eindhoven with me as well as everyday since.

Kevin Warmerdam

Eindhoven, The NetherlandsSeptember 10, 2015

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Contents

Preface v

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 System considerations . . . . . . . . . . . . . . . . . . . . . . 1

1.2.1 Sensor placement . . . . . . . . . . . . . . . . . . . . . 11.2.2 Lighting control algorithms . . . . . . . . . . . . . . . 31.2.3 Optical channel gain . . . . . . . . . . . . . . . . . . . 41.2.4 Visible light communication . . . . . . . . . . . . . . . 4

1.3 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . 51.4 Structure and organization . . . . . . . . . . . . . . . . . . . 5

1.4.1 Note on generality . . . . . . . . . . . . . . . . . . . . 51.4.2 Thesis structure . . . . . . . . . . . . . . . . . . . . . 5

2 State of the art 72.1 Optical wireless communications . . . . . . . . . . . . . . . . 72.2 Intelligent lighting . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Daylight adaptation . . . . . . . . . . . . . . . . . . . 82.2.2 Occupancy adaptation . . . . . . . . . . . . . . . . . . 92.2.3 Networking . . . . . . . . . . . . . . . . . . . . . . . . 92.2.4 Environment changes . . . . . . . . . . . . . . . . . . 10

2.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 System model 133.1 Networked lighting control . . . . . . . . . . . . . . . . . . . . 133.2 Visible light communication . . . . . . . . . . . . . . . . . . . 14

3.2.1 Modulated signal . . . . . . . . . . . . . . . . . . . . . 143.2.2 Message interpretation . . . . . . . . . . . . . . . . . . 15

3.3 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Method 194.1 VLC link performance . . . . . . . . . . . . . . . . . . . . . . 194.2 Estimation of control variables . . . . . . . . . . . . . . . . . 20

4.2.1 Optical channel gain extraction . . . . . . . . . . . . . 20

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4.2.2 Daylight estimation . . . . . . . . . . . . . . . . . . . 224.3 Control algorithm . . . . . . . . . . . . . . . . . . . . . . . . 23

5 Results 275.1 Performance of VLC . . . . . . . . . . . . . . . . . . . . . . . 27

5.1.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 285.1.2 Experimental . . . . . . . . . . . . . . . . . . . . . . . 31

5.2 Performance of networked lighting control . . . . . . . . . . . 355.2.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 355.2.2 Experimental . . . . . . . . . . . . . . . . . . . . . . . 38

6 Conclusions and future work 456.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.2.1 Internet of Things application . . . . . . . . . . . . . . 47

A Convex objective function derivation 55

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Chapter 1

Introduction

1.1 Motivation

In the commercial sector, lighting is responsible for 19% of the total en-ergy consumption [1]. Consider the multitude of buildings in existence andhow their indoor lighting is regulated. On average 23% of the total elec-trical energy consumption in buildings has been shown to go to waste onpoor management of occupancy conditions [2], where environments are il-luminated while no one is present. The amount of energy that is spent onenvironments which are already illuminated by daylight is even greater [3].

Significant costs may be saved in indoor office environments with a light-ing solution which minimizes its expended energy while satisfying users’illumination requirements. Intelligent lighting systems address these issues.Lamps, hereafter called luminaires, may be connected with sensors to de-tect both occupancy and illumination conditions. A system may be designedwhich, based on the input of these sensors, adapts and dims the luminairesto a desired level of output illuminance and no more.

1.2 System considerations

In the following sections, several key aspects of the proposed intelligent light-ing system are introduced. They serve to illustrate concepts and challengeswhich are revisited in the chapters that follow.

1.2.1 Sensor placement

Desired conditions of illumination within workplaces have been addressedin European standards [4]. Minimum levels of illuminance (measured inlux) on the workplane level, for instance on desks, are defined in these stan-dards based on whether the region is occupied or unoccupied. Note thatfor the purposes of an intelligent lighting system, it is impractical to mount

1

luminairephotosensor

workplane level

1 32

Figure 1.1: Example intelligent lighting system configuration, showing thecontributions of daylight and a neighboring luminaire to a photosensor.

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photosensors on workplanes such as desks to measure this local illumina-tion. These could easily become obstructed in daily activities, for examplethrough shadows cast by moved equipment or by the occupants themselves.The photosensors may be placed elsewhere, for example adjacent to the lu-minaires at the ceiling, containing the workplane in their field of view. SeeFig. 1.1 for an illustration of this method of mounting. This way, the systembecomes less obtrusive and practical. Note however that the distribution oflight that has reached the workplane is not identical to what has reachedthe ceiling-mounted sensor. Hence, a translation will be required betweenthe sensor reading and the illumination of interest at the workplane.

1.2.2 Lighting control algorithms

Constrained optimization

Two types of control algorithms may be distinguished for the intelligentlighting system. In a classical proportional integral differential (PID) ap-proach, only the illuminance measured with a photosensor is used [5]. Insuch case of standalone control the error with respect to a reference illumi-nance is computed and it is corrected for by the luminaire corresponding tothat photosensor. This aims to achieve a decreasing error over consecutivecontrol cycles.

Alternatively, a constrained optimization problem may be solved to de-termine an optimal control action. In this case, the desired control behav-ior is expressed in a cost function which is to be minimized under a setof constraints. For example, the power consumption expressed in termsof the dimming level is minimized under the constraint that the minimumilluminance is achieved. The dimming level which minimizes the cost func-tion without violating the constraints then gives the optimal control action.This approach requires a mathematical model of the lighting behavior andits variables must be known in order to solve the optimization problem.Knowing only the illuminance sensed with a photosensor is insufficient. Es-timations will need to be provided for the variables used in the optimizationproblem formulation such as the component of daylight contribution at aphotosensor.

Networked control

Most environments will require multiple luminaires to provide lighting to itsentire surface area. In this case, the output light from one luminaire willcontribute to the total illuminance in multiple photosensors’ field of view,as illustrated in Fig. 1.1. Furthermore, based on occupancy conditions, thereference illuminance may differ across neighboring luminaires. A situationcould present itself where a luminaire is unable to reach its reference withoutthe aid of neighboring luminaires.

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In a networked control algorithm, these effects may be taken into account.Radio communication is an established method of networking in intelligentlighting control systems [6]. The luminaires may be made to communicatetheir dimming levels and detected occupancy state, for example. If all theluminaires communicate their information to a central point, it is possible toformulate a single optimization problem which takes into account the wholeenvironment.

1.2.3 Optical channel gain

Consider the contribution of artificial light luminaire 1 has on photosensor2 in Fig. 1.1. This component of the total sensed illuminance depends onthe environment. In this case, it depends largely on the color and size ofthe desk surface. A dimensionless variable called the optical channel gain isused to describe these factors between the luminaire and the photosensor.

The optical channel gain may be used in the formulation of an optimiza-tion problem. Taking into account the effect of all luminaires on all photosen-sors makes it possible to consider the total illuminance due to distributionsof dimming levels across the luminaires.

These optical channel gains may be measured with a manual calibrationwhile the control system is offline. This calibration may be performed byturning on luminaires one by one while there is no daylight and noting thecontribution at every sensor. Using these stored values makes the systemvulnerable to environment changes, however. By shifting furniture around,by placing or moving an object on a desk, or even by clearing or cleaning it,the calibrated values used in the control algorithm may no longer be correct.In this thesis, robustness is desired against environment changes.

1.2.4 Visible light communication

Visible light communication (VLC) is a method of wireless communicationusing modulated light from the visible spectrum. The light intensity froman artificial light source may be varied to encode a message. This modulatedlight may be detected with a photosensor. If the rate of communication isfast enough, the human eye will not be able to perceive the transmitter asa fluctuating light source [7].

VLC may be used within networked lighting control systems to provide thecommunication links between luminaires. In this case, the modulated lightundergoes the optical channel gain as well. This implies that if the originallytransmitted signal is compared with the received signal, the optical channelgain may be estimated from it. These estimations may allow the system tobecome adaptable to environment changes.

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1.3 Problem statement

Both networking and continuous estimations of environment parameters arerequired to attain the best performance in intelligent lighting systems. Thisthesis proposes a novel lighting control system which uses VLC to addressboth these issues. Networking is established by reusing luminaires as trans-mitters and photosensors as receivers. Additionally, VLC allows for theestimation of optical channel gains based on received messages. In order torealize the system, several challenges are overcome:

• A reliable communication scheme is established between luminaires.

• The extraction of optical channel gains from VLC messages is accu-rately performed.

• An optimization problem which takes constraints for VLC communi-cation into account is formulated to serve as a control algorithm.

• The effect of detected environment changes is translated into an adap-tation of the reference illuminance at the luminaires.

By addressing these problems, the lighting system is made adaptable notonly to daylight and occupancy conditions, but also to environment changes.Robustness against environment changes through VLC is a novel approachto the state of the art.

1.4 Structure and organization

1.4.1 Note on generality

It is important to note that different application environments may requiredifferent parameters from the lighting control system proposed in this thesis.Based on the desired duration of a control cycle, the topology and numberof luminaires in the environment, or the minimum required signal strengthfor communication, trade-offs must be made. Because of this, the proposedVLC-networked lighting control system is presented with a level of generalityin this thesis. The trade-offs are shown by measures of system performancedetailed in terms of these parameters. One example of parameter choicessuitable for a wide range of environments is introduced later for evaluations.

1.4.2 Thesis structure

The rest of this thesis is organized as follows. In Chapter 2, previous workon both intelligent lighting systems and optical wireless communication arediscussed. The state of the art is reviewed and the key contributions of thisthesis are noted. Chapter 3 presents a model of the considered system. This

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includes a description of the proposed networked lighting control system aswell as how visible light communication is accomplished. Next, Chapter 4presents an in-depth look at the algorithms and methods used in the pro-posed system. The reliability of the proposed communication method is an-alyzed, the estimations of key control variables used in the control algorithmare explored, and the optimization problem for the lighting control law itselfis detailed. Results obtained both in simulation and with an experimentaltestbed are discussed in Chapter 5. The quality of VLC networking and theaccuracy of control variable estimation are evaluated. The proposed controlalgorithm is implemented and its robustness against environment changesis demonstrated. Lastly, conclusions are drawn from the work in Chapter 6and possibilities for future work are listed.

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Chapter 2

State of the art

This chapter presents the state of the art in the two fields on which thisthesis builds, namely optical wireless communication and intelligent lightingsystems. The previous work in optical wireless communication, of whichvisible light communication is a subset, is detailed in Section 2.1. Section2.2 describes the previous work on intelligent lighting and shows how visiblelight communication may improve upon it. Lastly, the contributions of thisthesis are listed in the context of this state of the art in Section 2.3.

2.1 Optical wireless communications

As a technology, optical wireless communications (OWC) can be traced backto the photophone invented by Alexander Graham Bell [8]. Here, speech wasmodulated over a beam of light by sound waves acting upon a mirror. Acentury later, optical communication gained renewed interest, where the firstLED-based OWC was introduced in 1979 by Gfeller and Bapst [9]. Here,diffusely scattered infrared light was used in an indoor environment as abroadcast channel which did not require a line of sight between transmitterand receiver.

Besides infrared, OWC may also encompass the ultraviolet or the visiblewavelengths of light. Its applications include indoor area networking as wellas outdoor free space communication, where its advantages argue for analternative to radio communication or a hybridization of the two methods[10]. In the application environment of indoor lighting systems, the visiblelight spectrum may be used. LED luminaires used for lighting may bereused as transmitters. This subset of interest of OWC is called visiblelight communication (VLC). The term applies to short range communicationusing the light spectrum from 380 nm to 780 nm [11].

VLC has been considered for internet networking hybridization becauseof the high luminous intensity already required in indoor environments [12].By reusing sources of indoor lighting, high signal-to-noise ratios may be

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achieved while installing dedicated high-power transmitters would not benecessary.

The implementations of VLC with the highest data rates rely on the useof multiple independent LEDs within a single transmitter. For example,data rates of Gbit/s in VLC announced by Zeng et al. [13] used an array ofLEDs in line of sight communication. When common LED luminaires areused for VLC, relatively low data rates may be expected.

When VLC is applied with sources which must also act as luminaires forthe purposes of lighting, trade-offs arise. Communication signal strengthand bandwidth are constrained by the requirements of light quality and theproperties of commercial LEDs [14]. For example, it may be desired for theaverage illuminance perceived by users to remain constant throughout com-munication. Ntogari et al. [15] demonstrated a way to accomplish this byconsolidating advanced modulation schemes in VLC with pulse-width mod-ulation dimming support. An IEEE standard has since been developed todescribe a PHY and MAC layer protocol for VLC communication, deemingit suitable for short-range support of multimedia services [16]. Manchestercoding is suggested in this protocol to accomplish constant average illumi-nation in simpler amplitude modulation schemes. In Manchester coding,one bit is represented by two symbols in either the order ‘10’ for a ‘1’ bit or‘01’ for a ‘0’ bit [17]. This way, the average output power is made equal foreither bit representation.

This thesis explores whether the luminaires and photosensors used in light-ing control may be reused as transmitters and receivers for VLC. This way,costs are saved on dedicated communication hardware. Messages may beexpected to consist of a limited number of variables measured locally. Sincecontrol cycle durations need not be subsecond in order to satisfy lightingbehavior, communication for the purpose of lighting control will then re-quire relatively low data rates. Therefore, a simple amplitude modulationscheme using Manchester coding is considered. Note however that there isno line of sight between luminaires and photosensors in the ceiling-mountedconfiguration shown previously in Fig. 1.1. Despite this, communicationbetween luminaires must be reliable.

2.2 Intelligent lighting

2.2.1 Daylight adaptation

Even before the widespread adoption of LED lamps, demonstrations showedsignificant energy savings were possible by implementing lighting controlstrategies. With fluorescent lights, energy savings of over 50% were ac-complished in a commercial office building by Rubinstein et al. [18] withdimming schemes based on adaptation to daylight conditions. Photosensorswere used here to perform standalone closed-loop control on ceiling-mounted

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lighting fixtures.

In later years, the so-called “lighting revolution” introduced solid-statelighting. Without the implementation of intelligent dimming schemes, en-ergy consumption was reduced by 50% as well [19]. Since this time, thestate of the art in lighting control has been furthered using LED luminairesand various control strategies.

2.2.2 Occupancy adaptation

Aside from daylight conditions, occupancy conditions can also be taken intoaccount in control strategies. In this case, energy spent to illuminate emptyenvironments can be saved. Miki et al. [20] have considered seat sensors forthe automated detection of occupancy conditions. Here, an additional 30%increase in energy efficiency by accurately detecting user presence conditionsis reported. The measurement of occupancy conditions has further beenconsidered using ultrasound [21], passive infrared sensors [22], or wirelessuser-held devices [23].

2.2.3 Networking

Control strategies may implement cooperation between networked lumi-naires as opposed to a standalone algorithm for independent luminaires.Without networking, problems arising from a standalone controller havebeen shown [22]. Here, local underillumination follows from situations wheretwo neighboring zones have different illuminance goals because of differentoccupancy conditions. The occupied zone requires stronger contributionsfrom its neighboring luminaires but these output less power because theydetect no occupancy.

Wen and Agogino [24] [25] have considered a wireless sensor network tocollect information about workplane illuminance and user preference. Acentralized approach was used. In this case, a central controller receivesall information about the system through communication. It computes anew configuration of dimming levels based on all the available information.These dimming levels are then communicated to actuators.

A networked distributed approach was presented by Pandharipande andCaicedo [26] where information is shared across asynchronous luminaires.Here, each luminaire solves for an optimization independently while takingneighbors’ contributions and detected occupancy into account. The sameauthors have presented similar systems where control is instead centralizedby solving for an optimization which takes all sensors and luminaires into ac-count [22, 27, 28]. In these works, the power consumption of the distributedapproach is shown to be sub-optimal. Assuming a feasible solution to theproblem exists, the centralized optimization is able to find the optimum so-lution [26]. This thesis therefore builds on the centralized approach and will

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require networking capabilities.

Networking may be achieved in different ways. Work by Miki et al. [29]has shown the complexity of wiring networks between all luminaires. In-stead, wireless communication is attractive for this application. Wen andAgogino further argue for ease in retrofitting older lighting systems by em-ploying small sensors and actuators, each of which uses a radio communica-tion module [24] [25]. Pandharipande and Caicedo [26, 22, 27, 28] consid-ered systems with photosensors as well as occupancy sensors co-located withceiling-mounted LED luminaires. The impracticality and cost of mountingsensors on the workplane level is thereby taken into account and only oneradio communication module is required per sensor-luminaire pair.

As discussed previously, VLC may replace radio networking in intelligentlighting systems. By reusing luminaires and photosensors as transmittersand receivers, the costs of dedicated communication hardware are saved inthe system proposed by this thesis.

The effects of wireless networking in an intelligent lighting system havebeen investigated. A ZigBee wireless network was implemented and shownto cause delays in the settling time of luminaires’ dimming levels due topacket losses [27]. The quality of the VLC link will therefore be investigatedin this thesis and probability of packet errors quantified.

Miki et al. have previously considered using VLC in an intelligent lightingsystem [30, 31]. Here, VLC is used to identify and locate remote sensorsplaced on the workplane. These sensing devices can be moved by usersand may emit LED light to communicate desired local illumination to aluminaire above it. Instead, this thesis proposes the use of VLC while re-taining a practical configuration of luminaires and sensors mounted at theceiling. No additional devices or user input will be required. Furthermore,communication will not affect the perceived illumination in the room.

2.2.4 Environment changes

State of the art methods used in the above works introduce a cost functionto be minimized within the lighting system [22, 26, 27, 28]. Solving for theseoptimization problems forms the control actions in these systems. In thisformulation, a parameter of the environment is considered to be explicitlyknown, namely the optical channel gains between all luminaires and photo-sensors. These systems rely on a calibration step while the system is offlineto obtain these parameters and assume the environment remains unchanged.

A method has been suggested by Caicedo et al. [32] for estimating chang-ing optical channel gains during control operations of a distributed lightingsystem, under the assumption that daylight does not change across controlcycles. This method requires changes in dimmming levels to occur beforean estimation can be made.

By using VLC, this thesis explores the possibility of directly measuring

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the optical channel gains instead. The same optical channel relevant forcontrol is here used for communication. A method is devised for extractingthe optical channel gains from every message received at every luminaire.This way, the values used in an optimization problem may be updated basedon changes to the environment at every control cycle.

2.3 Contribution

This thesis proposes a networked lighting control system which implementsvisible light communication. Powerful potential VLC transmitters are al-ready present in the form of the system’s LED luminaires. By using mod-ulated light emitted from the luminaires themselves at a rate faster thanthe human eye can perceive, messages may be transmitted which can beinterpreted with the photosensors. This method fulfills the required com-munication for the system without any additional hardware.

A centralized control algorithm is proposed because this method has thepotential to find an optimal distribution of dimming levels for the wholeenvironment. Furthermore, the unobtrusive, practical and cost efficient con-figuration with ceiling-mounted sensors is considered. This implies no directline of sight is available between any source and destination in VLC. Thisthesis verifies that communication is reliable despite this.

Additionally, this thesis explores whether it is possible to use the receivedsignal in communication to extract the optical channel gains in the network.State of the art lighting control algorithms rely on accurate estimations ofthese values.

This thesis shows that the state of the art control behavior is compromisedwhen environment changes occur. By being able to measure the opticalchannel gains during control operations, the system proposed in this thesiscan detect environment changes. The reference illuminance attained at theceiling level may be adapted based on changes observed. This way, thesystem may adapt not only to occupancy and daylight conditions, but alsoto changes in the environment.

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Chapter 3

System model

In this chapter, the proposed system is introduced. The characteristics andsetup of the networked lighting control are detailed in Section 3.1. Sec-tion 3.2 describes how messages are modulated and demodulated using lu-minaires and photosensors. Lastly, Section 3.3 briefly describes how thenetwork may be scheduled.

3.1 Networked lighting control

Consider an indoor office space with N ceiling-mounted luminaires whosedimming levels may be independently adapted through a local embeddedcomputer. Jointly placed at each luminaire is a photosensor with a downward-facing field of view. Under fluctuating daylight conditions, the desired con-trol behavior guarantees a constant minimum illuminance at the workplanelevel below the luminaires while minimizing their power consumption. Thisbehavior may be approximated by adapting the dimming levels based oninformation from the photosensors at the ceiling.

A centralized control algorithm is considered. In this setup, a central con-troller receives messages from each luminaire before computing the optimaldimming levels for the next cycle. This controller then communicates backto all the luminaires these dimming levels to be used.

The desired level of illumination at the workplane level may vary based onoccupancy conditions. European norms recommend an average illuminanceof 500 lux for occupied zones in office environment, and 300 lux for unoccu-pied zones [4]. To incorporate this, passive infrared (PIR) occupancy sensorsare used in the lighting control system to detect local occupancy conditions.One PIR sensor is mounted at each luminaire with a field of view similar tothe co-located photosensor. For the two individual levels of desired averageworkplane illuminance, a step of manual calibration is required to translatethis requirement to a reference illuminance measured at each sensor.

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3.2 Visible light communication

Visible light communication is implemented as the means of communicationbetween luminaires. By modulating its own emitted light intensity, a lumi-naire can encode messages which may be interpreted with the photosensorslocated at other luminaires. Note that during transmission, the average lu-minous power output of a luminaire must remain constant, correspondingto the current desired dimming level. Amplitude modulation is then con-sidered with Manchester coding to retain the average power output [11].Furthermore, guidelines to avoid harmful flickering of the light sources forthe human eye are followed by determining a minimum communication speedof 140 baud [7].

In the following sections, a mathematical model is presented to describevisible light communication in the lighting control system. All the steps torecover a transmitted bit sequence based on the sensed signal at a photosen-sor are explained. These derivations will be used in Chapter 4 to evaluatethe performance of the VLC link, to formulate a method of extracting opticalchannel gains from a received message, and to estimate daylight contribu-tions at photosensors.

3.2.1 Modulated signal

The signal received at a photosensor may be described in terms of the under-lying contributions. In an environment with N luminaires capable of VLC,the sensed signal at receiving luminaire m within the scope of one receivedpacket is

ym(t) = dm(t) + vm(t) +N∑n=1

(αm,nβm,n(un + ∆nbn(t))) ∗ hm,n(t), (3.1)

where n = 1, ..., N , m = 1, ..., N , dm(t) is the daylight contribution at desti-nation m over time, and vm(t) is the modeled additive white Gaussian noise(AWGN) contribution with vm(t) ∼ N (0, σ2

m). Furthermore, hm,n(t) is thenormalized impulse response of the channel between source n and destina-tion m, αm,n is the optical channel gain from source n to destination m, andβm,n is the maximum illuminance contribution from source n to destinationm defined by βm,n = Pn

Am, where Pn is the maximum luminous power output

of source n and Am is the sensing surface area of the sensor at destination m.The dimming level un at source n takes values in the range [∆min

2 , 1− ∆min2 ],

and ∆n is the modulation depth used ranging [∆min, 1], with ∆min beingthe minimum modulation depth for reliable communication. The messagecontribution is bn(t), defined by

bn(t) =

2L∑j=1

MjΠ( tT − j + 12) if n is the transmitting luminaire

0 otherwise,

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where L is the message length in bits, Mj is the jth symbol ranging {−12},{

12}

in the Manchester coding of the bit sequence corresponding to the message,T is the symbol period, and Π(t) is the rectangular function

Π(t) =

{1 if |t| ≤ 1

20 if |t| > 1

2 .

3.2.2 Message interpretation

For the following theoretical analysis of how a bit sequence is recovered fromthe modulated signal, the assumption is made that hm,n(t) = δ(t), whereδ(t) is the Dirac delta function. In this case, (3.1) may be rewritten as

ym(t) = dm(t) + vm(t) +N∑n=1

αm,nβm,n(un + ∆nbn(t)). (3.2)

At receiving luminaire m, the signal ym(t) is sampled at a frequency fsand processed using a matched filter which computes for each sample s

ρm[s] =1

2Tfs

2Tfs−1∑k=0

ym

(s+ k

fs

)g

(k

fs

), (3.3)

where g(t) is the template signal defined by

g(t) =

{1 if t ≤ T−1 if t > T,

corresponding to the Manchester coding of a ‘1’ bit. For simplicity, it isassumed that 2Tfs ∈ N here and in the rest of this work.

In the noiseless case, assuming un and dm are constant in the scope of abit period, the average sensed illuminance in (3.2) during a ‘1’ Manchestersymbol from transmitting luminaire p is

y+ = dm + αm,pβm,p∆p(1

2) +

N∑n=1

αm,nβm,nun (3.4)

and the average value during a ‘0’ Manchester symbol is

y− = dm + αm,pβm,p∆p(−1

2) +

N∑n=1

αm,nβm,nun. (3.5)

Using (3.4) and (3.5), in the case of a transmission of a ‘1’ bit at matchingsample s? from transmitting luminaire p, the output of the matched filter is

ρm[s?] =1

2(y+ − y−) + vm =

1

2αm,pβm,p∆p + vm, (3.6)

15

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.120

25

30

35

40

t

ym(t)

(a) Received signal with sensor.

−0.01 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

−1

−0.5

0

0.5

1

t

g(t)

(b) Template signal for matched filter.

0 500 1000 1500 2000 2500 3000−1.5

−1

−0.5

0

0.5

1

1.5

s?

s

ρm[s]

(c) Matched filter output.

1 2 3 4 5 6 7 8 9 10 11−1.5

−1

−0.5

0

0.5

1

1.5

i

µm[i]

(d) Matched filter output sampled down to bit rate.

Figure 3.1: Example of signals in noisy message interpretation for T = 5ms, fs = 32 kHz, and L = 8 bits.

16

where vm represents the reduced noise due to the matched filter: vm ∼N (0, 1

2Tfsσ2m). Equivalently, a ‘0’ bit would result in the value −ρm[s?].

The matched filter output is sampled down to the bit rate 12T to obtain

µm[i] = ρm[2Tfs(i− 1)] for i = 1, ..., L. (3.7)

Fig. 3.1 illustrates how the sequence µm[i] is obtained from ym(t), in-cluding the intermediate steps described above. Note that it is necessary toemploy the correct sampling phase on a received signal. This is called tim-ing recovery. A method such as the Gardner algorithm accomplishes this[33]. The values of µm[i] are compared with a zero-level threshold in theslicer. Thus, all positive values are interpreted as ‘1’ bits and all negativevalues are interpreted as ‘0’ bits. These values will also be used to make anestimation of the optical channel gains αm,n. The knowledge of all opticalchannel gains in the environment will be used to make an estimation of thedaylight contributions dm at the sensors. The details of how this is achievedare discussed in Chapter 4.

3.3 Scheduling

Assuming one luminaire is capable of reliable communication with all otherluminaires in the environment, a time division multiple access (TDMA)schedule may be regulated from this luminaire. Call this luminaire the mas-ter luminaire. The same luminaire may also act as the central controller forthe lighting control system. In TDMA, each luminaire in the environmentis assigned a unique time slot within one control cycle by the master lumi-naire. In its time slot, a luminaire may transmit its VLC message. Thisway collisions in messages are avoided.

It is important to note that the proposed VLC-networked lighting sys-tem is presented in this thesis with a level of generality. Environments withdifferent luminaire topologies may require different scheduling methods how-ever. This is discussed further in Chapter 6. For completeness, one possibleimplementation is provided below.

For luminaires to adhere to their time slot, clock synchronization is re-quired between them. To achieve this, a method such as the Berkeley algo-rithm may be integrated with the existing message structure. The Berkeleyalgorithm synchronizes distributed devices by communicating timestampsto a master device [34]. The master device then computes an average clocktime and communicates back the adjustment each device must make to itsinternal clock. Accordingly, luminaires can be made to include a timestampin the VLC message transmitted every control cycle.

The master luminaire compares the transmitted timestamps with its own,taking into account the known duration of communication, and stores them.After each luminaire has transmitted in a control cycle, the master luminaire

17

0 1 2 ... N N+1 ... 2N−1

N

...

2

1 ...

...

...

...

...

...

...

...

Master luminaire transmits

all control actions

Each luminaire transmits

its own measurementsSense

One control cycle

scheduling slot

luminaire

Figure 3.2: TDMA scheduling for N luminaires in centralized control.

computes the control actions for the next cycle as well as an average clocktime. Each following message from the master luminaire then contains boththe control action to be used by the destination luminaire as well as anadjustment to be applied to its internal clock. Fig. 3.2 illustrates onecontrol cycle of the proposed TDMA scheduling. Here, luminaire 1 is thedesignated master luminaire.

In the case of a centralized control algorithm, the information containedin the VLC message transmitted by each luminaire now includes its iden-tifier m, a timestamp, the most recent estimations of αm,n for all n, themodulation depth used ∆m, and a measurement of the average ym(t) for thecurrent control cycle.

18

Chapter 4

Method

This chapter provides a detailed analysis of the proposed system’s func-tionalities. The control behavior relies on the accuracy of the informationprovided for the constrained optimization problem. Both optical channelgains between all luminaires and photosensors and daylight contributions atall photosensors are used in the formulation of its cost function and con-straints, as is shown below.

Section 4.1 first describes the performance of VLC communication. Next,Section 4.2 details the method used to extract optical channel gains fromreceived VLC signals and the method used to estimate daylight contributionsat sensors. Lastly, Section 4.3 formulates the optimization problem whichmay be solved to obtain optimal control actions across all luminaires in thesystem.

4.1 VLC link performance

The performance of VLC networking may be expressed in the probability ofa packet error. This probability depends on the parameters introduced inChapter 3 such as the modulation depth used and the amount of noise.

In (3.6), noise conditions captured with vm may result in a bit error whena negative value occurs for ρm[s?] while a ‘1’ bit was present, or when apositive value occurs while a ‘0’ bit was present. The bit error ratio (BER)in a message from source p to destination m may be expressed in terms ofthe AWGN power σ2

m by [35]

BERm,p = 1− Φ(12αm,pβm,p∆p√

12Tfs

σ2m

)

= 1− Φ(

√Tfsαm,pβm,p∆p√

2σm), (4.1)

19

where Φ(x) is the cumulative distribution function of the normal Gaussiandistribution.

Assuming constant message lengths L and assuming that no error correct-ing technique is used, the packet error ratio (PER) between source p anddestination m in the VLC setup may be characterized by

PERm,p = 1− (1− BERm,p)L

= 1− Φ(

√Tfsαm,pβm,p∆p√

2σm)L. (4.2)

This expression for the probability of a packet error relates the performanceof communication in the proposed system. The system’s parameters may bechosen in such a way that the PER is minimized. Trade-offs arise here. Forexample, increasing T lowers the communication speed and can improvereliability. However, this will lead to longer control cycle durations andlonger periods of waiting before dimming levels are updated.

4.2 Estimation of control variables

By using VLC, the optical channel gain αm,n may be estimated at desti-nation m based on a message received from n. The maximum illuminancecontribution βm,n is a known constant of the system and the original mod-ulation depth ∆n used is communicated in the message itself. This means(3.6) may be used to recover the optical channel gains αm,n based on thematched filter output ρm[s?] as shown below. Furthermore, if all the opticalchannel gains and all the current dimming levels used are known, an estima-tion may be made of the contribution of daylight dm at every sensor, givenits total measured illuminance. If both these estimations are made, an opti-mization problem may be formulated to obtain a control action um, as willbe shown in Section 4.3. Fig. 4.1 shows a flowchart combining the messageinterpretation methods from Section 3.2.2 with the methods described here.

4.2.1 Optical channel gain extraction

Consider a VLC message sent from source p. The message contents include∆p used at the source. Any luminairem in range may make an interpretationof the transmission. The system’s βm,p is considered known. Assuming thatun for all luminaires n and dm are constant in the scope of this message andthat no bit errors occur in detection, an estimation αm,p can be made fromthe matched filter output at matching sample s? in (3.6) as

αm,p =2ρm[s?]

βm,p∆p. (4.3)

20

Luminaire sidePhotosensor

Matched filter

Slicer

Gain extraction

InterpreterDaylight

estimation

Artificial light & Daylight

Central

controller side

Figure 4.1: Flowchart of control variable estimations.

The error in the optical channel gain extraction with (4.3) is

αm,p − αm,p =2vm

βm,p∆p∼ N (0,

2

Tfsβ2m,p∆

2p

σ2m). (4.4)

In the above method, the optical channel gain extraction occurs based ona single bit. One extraction may also be performed over the scope of theentire packet to reduce the error. In this case the estimation is made by

α′m,p =2

βm,p∆pL

L∑i=1

bit[i]µm[i], (4.5)

where

bit[i] =

{1 if bit i is ‘1’−1 otherwise.

The error in the optical channel gain extraction with (4.5) is

α′m,p − αm,p ∼ N (0,2

TLfsβ2m,p∆

2p

σ2m). (4.6)

Expressed as an expected absolute proportional error, the error correspond-

21

ing to (4.6) is

E

[∣∣∣1− α′m,pαm,p

∣∣∣] = E

[∣∣∣αm,p − α′m,pαm,p

∣∣∣]=

1

αm,pE[|αm,p − α′m,p|

]=

1

αm,p

√2

π

√2

TLfsβ2m,p∆

2p

σ2m

=2

αm,pβm,p∆p√πTLfs

σm. (4.7)

This expression shows the performance of optical channel gain extractionsin terms of the system parameters. Similar design trade-offs are seen hereas in the VLC link performance of Section 4.1.

4.2.2 Daylight estimation

In the case of a centralized control algorithm, the central controller obtainsestimates of the optical channel gains α′m,n for all destinations m and allsources n after each luminaire has transmitted its VLC message. The mes-sages also contain ym, the sample average of ym(t) obtained in a time slotin the beginning of the control cycle, where no VLC messages were trans-mitted. Also, all dimming levels un used in the current control cycle arestored at the central controller. Lastly, the system’s maximum illuminancecontributions βm,n are again considered known.

At a luminaire m, the average daylight contribution dm during the mea-surement of ym of duration 2TL is given by

dm = ym − vm −N∑n=1

αm,nβm,nun, (4.8)

where

ym =1

2TLfs

2TLfs−1∑k=0

ym

(k

fs

)is the sample average of ym(t) at sampling frequency fs and vm representsthe remaining noise contribution: vm ∼ N (0, 1

2TLfsσ2m).

An estimation of the daylight contribution dm can be made by

dm = ym −N∑n=1

α′m,nβm,nun. (4.9)

22

The error in daylight estimation in (4.9) is given by

dm − dm = vm +N∑n=1

(α′m,n − αm,n)βm,nun

∼ N (0,1

2TLfsσ2m +

N∑n=1

(2un∆n

)2 1

2TLfsσ2m)

∼ N (0,1

2TLfsσ2m +

1

2TLfs

N∑n=1

(2un∆n

)2σ2m)

∼ N (0,1

2TLfs(1 +

N∑n=1

4u2n

∆2n

)σ2m). (4.10)

The expected absolute error in daylight estimation corresponding to 4.10 is

E[|dm − dm|

]=

√2√π

√√√√ 1

2TLfs(1 +

N∑n=1

4u2n

∆2n

)σ2m

=

√√√√ 1

πTLfs(1 +

N∑n=1

4u2n

∆2n

)σm. (4.11)

These estimated daylight contributions dm are computed by the centralcontroller for all luminaires m. With these, and the optical channel gains,all the information has been provided to compute optimal dimming levelsto be used in the next control cycle.

4.3 Control algorithm

Consider A the N × N matrix containing the products of the extractedoptical channel gains α′m,n and maximum luminous received power βm,nfrom source luminaire n to receiving luminaire m, as

A =

α′1,1β1,1 . . . α′1,Nβ1,N

.... . .

...α′N,1βN,1 . . . α′N,NβN,N

.Also let u be the vector containing all luminaires’ dimming levels um, d bethe vector containing all estimated daylight contributions dm, and l be thevector containing the reference illuminance at each sensor lm, as

u =

u1...uN

, d =

d1...

dN

, l =

l1...lN

.23

Lastly denote 0 and 1 the N × 1 vectors

0 =

0...0

, 1 =

1...1

.Note once more that although the photosensors are mounted at the ceil-

ing due to practicalities, the constant level of illuminance is desired at theworkplane below it. Two different levels of average workplane illuminationare needed: 300 lux for unoccupied zones and 500 lux for occupied zones [4].To account for this, the system is calibrated once after installation with tem-porary workplane sensors while there is no contribution of daylight in theenvironment. During this calibration, two configurations of dimming levelsare manually chosen which match these levels of workplane illumination,u300 and u500, and these are stored. A mapping of workplane illuminanceis approximated with these stored dimming levels. To handle environmentchanges affecting the average workplane illumination, the reference illumi-nance l is updated each control cycle by computing for each element

lm =

{ ∑Nn=1 α

′m,nβm,nu

300n if the zone under m is unoccupied∑N

n=1 α′m,nβm,nu

500n if the zone under m is occupied.

(4.12)

The desired control behavior results from the least possible error withrespect to the reference illuminance. By minimizing the Euclidean normof the error ||Au + d − l|| for some optimal u? this is achieved and thepower consumption is minimized as well. The solution to the minimizationof ||Au + d− l|| is equivalent to the solution of the minimization of

||Au + d− l||2 = (Au + d− l)T (Au + d− l).

Solving for this minimization alone does not guarantee dimming levels unwithin the permitted range [∆min

2 , 1 − ∆min2 ]. Also, the system must guar-

antee the minimum illumination is met, i.e. overillumination errors mayoccur while underillumination errors may not. The optimization problemmay then be expressed as:

u? = arg minu

(Au + d− l)T (Au + d− l)

subject to

{Au + d > l∆min

2 1 ≤ u ≤ (1− ∆min2 )1.

(4.13)

The formulation in (4.13) may be rewritten in standard form [36]. Theoptimization problem then becomes

u? = arg minu

(Au + d− l)T (Au + d− l)

subject to

−Au− d + l ≤ 0∆min

2 1− u ≤ 0

u− (1− ∆min2 )1 ≤ 0.

(4.14)

24

This is a convex quadratic minimization problem with linear inequality con-straints. A derivation for the objective function’s convexity is provided inAppendix A. It is attractive to use a method of quadratic programming tosolve the optimization problem, such as the interior point or barrier method[36]. This solution for u gives the control action at every control cycle.

25

Chapter 5

Results

This chapter features evaluations of the proposed system. Both simulationsand an experimental testbed are employed. Section 5.1 details several resultsconcerning the VLC aspect of the system and discusses them. This includesevaluations on packet errors, optical channel gain extractions, and daylightestimation. Section 5.2 explores the control behavior of the proposed sys-tem and compares its performance to that of a non-VLC state of the artapproach. The effect of environment changes are shown to compromise thecontrol behavior of the state of the art systems, while the proposed systemis robust against them.

5.1 Performance of VLC

The communication link performance detailed in Section 4.1 is evaluated aswell as the estimation methods for optical channel gain and daylight con-tribution detailed in Section 4.2. Simulations are employed to evaluate themathematical models established to describe the system. An experimen-tal testbed is also used to evaluate the proposed VLC networking underreal-world conditions. In both cases, the same system parameters are used:

• The bit rate is set at 1000 bits/second, thus T = 500 µs.

• The sampling rate of the sensors is fs = 32 kHz.

• The message length is L = 512 bits.

These parameters are suitable to most indoor environments. The relativelylow-rate communication still allows for regular updates of dimming levels.For instance, with N = 8 luminaires communicating back and forth withthe central controller, control cycle durations can be kept under 10 seconds.

Further denote

Channel SNRm,p =(1

2αm,pβm,p∆p)2

σ2m

(5.1)

27

as the ratio of signal power to noise power at the input of the matched filterat destination m when source p is transmitting.

5.1.1 Simulation

In Chapter 4, the mathematical expressions (4.2) and (4.7) were presentedwhich described trade-offs between system parameters and the quality ofVLC communication. These expressions to aid in the design for specificenvironment topologies are evaluated here.

In simulation, (3.2) is used to model a received VLC signal. One sourceand one destination are considered under different channel noise conditions.Messages consist of random bits following a uniform distribution. Becausethe performance in terms of system parameters are of interest, the timingrecovery of the detected signal is assumed to be ideal.

Packet error ratio

Ten distinct Channel SNRs are chosen and 10 000 packets are simulatedfor each one. Message interpretation occurs as described in Section 3.2.2.A packet is considered erroneous when at least one of its bit is incorrectlyinterpreted. The PERs found for these messages are compared with thetheoretical description in (4.2). Fig. 5.1 shows the simulated detectionmethod matches well with what was described in theory.

Electrical noise conditions, the modulation depth used, and the distancebetween source and destination contribute to the Channel SNR shown in Fig.5.1. The performance of the system’s VLC link has been characterized withthis result. A further experimental result is required to confirm whether theshown Channel SNRs are below the expected conditions in communicationbetween luminaires without line of sight. To have the impact of packet losseson control be acceptable, consider the PER must be less than 1%.

Optical channel gain extraction

Again, ten distinct Channel SNRs are chosen and 10 000 packets are simu-lated for each one. The method for optical channel gain extraction describedin Section 4.2.1 is applied to each packet. The absolute proportional errors

in these gains |1 − α′m,pαm,p| are calculated and compared with the expected

theoretical value in (4.7). Fig. 5.2 shows that the mean error found perdistinct SNR matches the expected value well.

This result shows that the error in optical channel gain extraction is rela-tively small. Comparing with Fig. 5.1, it is reasonable to say that so long ascommunication is reliable (PER < 1%), the optical channel gain extraction

are accurate as well (|1− α′m,pαm,p| < 1%).

28

0 0.1 0.2 0.3 0.4 0.5 0.6 0.710

−3

10−2

10−1

100

Channel SNR

PER

TheoreticalSimulated

Figure 5.1: Theoretical and simulated PER by SNR for T = 500 µs, fs = 32kHz, and L = 512 bits.

29

0 0.2 0.4 0.6 0.8 10

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Channel SNR

E

[

∣ ∣ ∣1−

α′ m,p

αm,p

∣ ∣ ∣

]

TheoreticalSimulated

Figure 5.2: Theoretically expected and simulated absolute proportional er-ror in gain extraction by SNR for T = 500 µs, fs = 32 kHz, and L = 512bits.

30

0 1 2 3 4 5 6 70

1

2

3

4

5

windows

2

1

4

3

6

5

8

7

x [m]

y[m

]

Figure 5.3: Overview of experimental testbed and room layout with desks,ceiling-mounted LED luminaires, and photosensors indicated.

5.1.2 Experimental

To further evaluate the proposed VLC system, it is implemented on anexperimental testbed to introduce realistic conditions found in practice. Thetestbed consists of N = 8 LED luminaires mounted on the ceiling as a 2-by-4 grid within an in-use office room. The distance between the centersof any two neighboring luminaires on the grid is 2.1 meters. The office isfurnished with desks, monitors, chairs, and cabinets, which result in a varietyof optical channel gains in the environment. The north side of the roomfeatures windows all along its length which can be occluded with blinds. Anoverview is shown in Fig. 5.3.

Co-located sensors at each of the luminaires are read out with a data ac-quisition (DAQ) device which is connected to a central computer. Dimminglevels for each of the luminaires are described from the same computer in asignal with an equal number of samples to what is sensed.

Packet error ratio

The effect of realistic components used in the system is investigated withthe testbed. The effect of other factors on communication performance areminimized. The blinds are closed to remove fluctuating daylight conditions.

31

Also, the timing recovery during detection is made ideal through the centralcomputer by applying a synchronized sampling phase.

One combination of source (luminaire 1) and destination (luminaire 4)is selected and a range of 11 unique SNRs are used. To obtain differentSNRs, the signal strength is varied by changing the modulation depth ∆n

used in transmission. For each distinct modulation depth, 400 packets arecommunicated. The erroneous packets are counted to conclude a PER. Tomeasure the SNR, a period of no communication precedes each message.During this time, noise power PN is measured. Then, during the message,the combined power PS+N is measured. The Channel SNR is then estimated

byPS+N−PN

PN. Per unique modulation depth used, the measured SNRs for

the packets are averaged.Note that the PER result shown in Fig. 5.1 was obtained by simulation

with the VLC received signal model in (3.2), which assumed a Dirac deltafunction for the channel impulse response, or h(t) = δ(t). In practice, thePER is affected by a realistic channel impulse response. Factors such asthe response time of the photosensor shape the signal, causing intersymbolinterference. Such a photosensor may be modeled as a low-pass filter witha decaying exponential as an impulse response [37]. A simulation wherethis modeled component is included is then also considered and comparedwith the result obtained through the testbed. In this case, the channel

impulse response is modeled as h(t) = 1τ e−tτ with τ = 10−4 and (3.1) is used

as a received signal model. Other than this, the setup for this additionalsimulation is identical to the PER by SNR simulation found in Section 5.1.1.

Now consider Fig. 5.4, where the earlier theoretical model, the addi-tional simulation with a low-pass response, and the result obtained with thetestbed are shown. The PER found with the testbed shows a clear loss inperformance. The additional simulated case shows that the cause of this ischaracterized by the properties of realistic components. The shown qualityof communication performance with the testbed indicates desired channelconditions for reliable communication.

It is now possible to evaluate the VLC link between luminaires. Recalla minimum modulation depth ∆min was defined in Section 3.2. The mod-ulation depths used to achieve the shown PERs in the experimental caseare all below the expected operating conditions for the office room. Usingfor example ∆min = 0.1 in the experimental testbed results in reliable com-munication for this set of parameters even though there is no line of sightbetween luminaires and photosensors.

Daylight estimation

In this experiment, the daylight estimation functionality is illustrated usingthe testbed. By doing so, the underlying optical channel gain extractions onwhich the daylight estimation depends are also tested. For this result, all

32

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.610

−3

10−2

10−1

100

Channel SNR

PER

Theoretical, delta responseSimulated, low−pass responseExperimental

Figure 5.4: Comparison of PER by SNR for T = 500 µs, fs = 32 kHz, andL = 512 bits, with different channel responses.

33

0 50 100 1501010

1020

1030

1040

1050

1060

1070

t [s]

ym(t)

[lux]

Sensor inputDaylight estimationDaylight ground truth

Figure 5.5: Experimental daylight estimation at luminaire 7 over 10 schedul-ing cycles.

luminaires in the testbed are made to communicate. TDMA scheduling isapplied so that each luminaire communicates once in a cycle of 12 secondsin the order of their identifiers. Realistic timing recovery is now performedduring detection and the blinds of the room are opened to allow daylightinto the office.

In Fig. 5.5 the daylight estimation behavior is shown. For around the first15 seconds of the experiment, the sensed illuminance at luminaire 7 equalsthe ground truth of daylight. All luminaires in the room are then turned onto a dimming level of 0.5 (half the maximum power output) for ten cyclesof scheduling. For each cycle, after all eight luminaires’ optical channelgains are extracted from the transmitted messages, a daylight estimation isperformed. The occurrence of these messages can be seen in the receivedsignal of each cycle. After the ten cycles, the luminaires are switched offagain. In the figure, the daylight estimations were found to be close to theground truth.

34

5.2 Performance of networked lighting control

The proposed control algorithm is evaluated by comparing its behavior to astate of the art algorithm. The robustness of either system against environ-ment changes is observed.

To investigate the system’s dimming behavior over a sequence of con-trol cycles, the optimization problem detailed in Section 4.3 is solved usingquadratic programming to obtain a control action u based on the avail-able information. Changes are introduced to the environment during theseoperations. The effect of this is investigated for two distinct system setups.

The first setup implements the proposed method of this work. By usingVLC for networking, extractions of optical channel gains α′m,n and estima-

tions of daylight contributions dm occur as described in Section 4.2.The other setup employs a non-VLC method of communication and thus

while sharing information across luminaires cannot provide continuous esti-mation of the optical channel gains. Instead, the stored result of a manualmeasurement of these gains α′′m,n is used. This non-VLC setup is repre-sentative of state of the art intelligent lighting systems using centralizedcontrol [27, 28].

Daylight estimation using these values as in the proposed method maylead to negative values for dm under changes to the environment. Thiscan render (4.14) infeasible when the constraints −Au − d + l ≤ 0 andu− (1− ∆min

2 )1 ≤ 0 become mutually exclusive. Because of this, the insightis added that negative values for daylight contribution estimation at a sensorcould not be a correct representation of daylight behavior. Thus, instead of(4.9), this method uses

dm =

{0 if ym −

∑Nn=1 α

′′m,nβm,nun < 0

ym −∑N

n=1 α′′m,nβm,nun otherwise.

(5.2)

A constant minimum level of illuminance at the workplane level is thedesired control behavior. Therefore the average levels of illuminance wz(t)for workplane zone z resulting from both methods are compared. Furtherdenote pz(t) the daylight contribution of illuminance at a workplane zone z.The evaluations are performed both in simulation and with the experimentaltestbed described in Section 5.1.2.

5.2.1 Simulation

The proposed controller is first compared with the state of the art usingsimulations. This way, it is possible to isolate and observe the effectivenessof the proposed environment change adaptation. Several situations are mod-eled in simulation to highlight different aspects of the underlying behavior.

An indoor open-plan office environment model is considered. It consistof N = 80 ceiling-mounted luminaires in an 8-by-10 grid. The workplane

35

luminaire 58

zone 20

Figure 5.6: Environment model used in simulation.

level below consists of a configuration of desks where each desk defines onezone of interest. One luminaire-zone combination, as indicated in Fig. 5.6,is observed while all luminaires in the environment are controlled.

Values for A and d in this environment are obtained using DIALux [38].In this model, daylight may enter through one side of the room, wherewindows are situated. The daylight conditions used here may be replicatedin DIALux using the settings: mixed sky conditions for March 3rd, 2015,from 8:00 a.m. onward.

Environment changes are introduced by changing desk reflectance in theunderlying DIALux model. The reflectance parameter is a value between0% and 100%. Low values represent a surface with a dark color and highvalues represent one with a light color. Changing it results in different valuesfor A and d. Different environment changes are explored in the simulationsbelow.

For the following simulations, (3.2) is used to model the input of a photo-sensor. Since the effect of environment changes is of interest, the noiselesscase is considered, i.e. vm(t) = 0. All zones are considered occupied, makingthe desired workplane illuminance 500 lux [4].

Both the proposed system and the non-VLC system are considered foreach simulation. The non-VLC system setup uses the same initial valuesfor A for its entire run while the system using VLC obtains a new A everycontrol cycle.

For each simulation, the daylight contributions d58 and p20 are observed,as well as the average ceiling illuminance per control cycle y58, the dimminglevel u58, and the average workplane illuminance per control cycle w20. Theperformance of both systems is judged by how well w20 corresponds to the

36

desired constant level of light.

Local underillumination and overillumination

In the first simulation, three consecutive environment situations are consid-ered. Originally, all desks have a reflectance of 60% as their natural color.After 12 control cycles, only the desk corresponding to zone 20 has its re-flectance changed to 90%. This may represent white-colored paper placed onit. Lastly after 24 control cycles, this same desk has its reflectance changedagain to 30%. This may represent a dark object has been placed on thedesk, such as a bag or a laptop.

Consider Fig. 5.7 for the resulting control behaviors. Note firstly in Fig.5.7a how the environment changes affect ceiling illuminance differently thanworkplane illuminance. Further, the behavior marked in red in Fig. 5.7dshows that using the non-VLC setup which cannot update A during controlleads to undesired behavior. Underillumination occurs after the reflectanceis increased, compromising user satisfaction, and slight overillumination oc-curs after the reflectance is decreased, wasting energy in increased dimminglevels. However, the proposed method implementing VLC can update Aand thus obtains new references l using (4.12). The result is a constantsatisfactory w20 across environment changes for the behavior marked greenin Fig. 5.7d.

Local oscillation

In the second simulation, three consecutive environment situations are againused. Now, all desks are modeled as having a darker natural color by usinga reflectance of 30%. After 12 control cycles, only the desk correspondingto zone 20 has its reflectance changed to 90%, again representing a light-colored object placed on the desk. After 24 control cycles, this same deskhas its reflectance changed again to 60%. This may represent an object ofneutral color is placed on the desk, such as brown cardboard.

The control behavior resulting from these changes is shown in Fig. 5.8.Note that the stored values for A in the non-VLC setup correspond to deskreflectances of 30% in this simulation. The increased reflectances comparedwith this in environment changes that follow result in irregular behaviorfor the non-VLC state of the art system as shown in Fig. 5.8c. The largerdiscrepancy with the ground truth of the optical channel gains causes slowlyconverging oscillations in the output dimming level. This means that afterplacing a light-colored object on a dark desk surface, a user would observea flashing overhead luminaire. Furthermore, the positive reflectance changeresults in constant underillumination as seen in Fig. 5.7d. Contrarily, thesetup implementing VLC can adapt to these changes, resulting in desiredworkplane illumination.

37

Global oscillation

So far, only local changes to one desk have been considered in the abovesimulations. With an occupied office space, such changes may be expectedto occur on all desks. The third simulation applies the same environmentchanges as the previous simulation, only they are applied to all desks. First,all desks have a reflectance of 30%, then all desks have their reflectancechanged to 90%, lastly all desk have their reflectance changed again to 60%.Also, dm and pz are scaled by a factor of 1.5 from the previous two simula-tions, as an example of stronger daylight conditions.

Consider Fig. 5.9 for the behavior resulting from this last simulation.Because more than a single zone is altered in the environment change, theoscillating behavior in the state of the art approach is now much stronger.Note how for the red behavior in Fig. 5.9b, the dimming level oscillations inFig. 5.9c do not correspond. This is because now that environment changeshave occurred at all zones, the neighboring luminaires are oscillating aswell. The sum of all oscillating artificial light results in the non-decreasingfluctuations seen in Fig. 5.9b and 5.9d.

The proposed implementation using VLC shown in green in Fig. 5.9d isstill able to capture the larger environment changes and adapts to them. Thedesired control behavior is exhibited despite large changes all throughout theenvironment.

5.2.2 Experimental

In order expose the proposed control algorithm to real-world conditions, itis evaluated in practice. Physical environment changes are now applied.Both the proposed system and the non-VLC state of the art system areimplemented on the experimental testbed of N = 8 luminaires described inSection 5.1.2. Again, all zones are considered occupied.

In order to compare the two behaviors, similar external light contribu-tions must be present for both measurements. Because of this, daylightadaptation cannot be taken into account here. The blinds in the testbedare therefore closed. In this real-world application, the sensed signals aresubject to realistic conditions such as noise. This introduces errors in bothcalibrations and estimations of A to which the system must remain robust.

Luminaire 3 is observed as well as the workplane surface below it, denotedas zone 3, using an external light meter. Environment changes consist ofdifferent objects which are common in office environments to be placed onthe desk under the luminaire. Again three environment situations are de-fined. During the first, plain brown cardboard is placed on the desk. After3 control cycles, it is overlaid with white paper of similar size. Lastly, after6 control cycles, the black fabric of a common overcoat is placed over thepaper.

38

The resulting control behaviors are shown in Fig. 5.10. Again, the be-havior shown in red corresponds to the non-VLC state of the art setup andthe behavior shown in green corresponds to the proposed VLC setup.

In the control cycle following either environment change, the difference insensed ceiling illuminance is evident from Fig. 5.10a. The proposed VLCsetup is shown to be able to adapt to these changes in practice, providing thedesired constant workplane illuminance shown in Fig. 5.10c. As can be seenin the same plot, using the setup without ongoing estimations of the opticalchannel gains leads to irregular workplane illuminance. Clear underillumi-nation presents after the first environment change and clear overilluminationpresents after the second environment change. The experimental result re-sembles the simulated behavior shown in Fig. 5.7 and shows that unlike thestate of the art, the proposed VLC system adapts to environment changes.

39

0 5 10 15 20 25 30 350

2

4

6

8

10

control cycle

d58,p20

[lux]

Daylight at ceilingDaylight at workplane

(a) Daylight contribution, both at the ceiling and at the workplane.

0 5 10 15 20 25 30 350

20

40

60

80

control cycle

y58

[lux]

Precalibrated AEstimations of A with VLC

(b) Total illuminance sensed with the ceiling-mounted photosensor.

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

control cycle

u58


(c) Dimming level used by the luminaire.

0 5 10 15 20 25 30 350

100

200

300

400

500

600

control cycle

w20

[lux]


(d) Total illuminance at the workplane.

Figure 5.7: Simulated control behavior through localized changes in desk re-flectance (60%, 90%, then 30%) under changing daylight conditions, showingunderillumination and slight overillumination are prevented in the proposedVLC method.

40

0 5 10 15 20 25 30 350

2

4

6

8

10

control cycle

d58,p20

[lux]



0 5 10 15 20 25 30 350

20

40

60

80

control cycle

y58

[lux]



0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

control cycle

u58



0 5 10 15 20 25 30 350

100

200

300

400

500

600

control cycle

w20

[lux]



Figure 5.8: Simulated control behavior through localized changes in desk re-flectance (30%, 90%, then 60%) under changing daylight conditions, showingoscillation is prevented in the proposed VLC method.

41

0 5 10 15 20 25 30 350

2

4

6

8

10

12

14

control cycle

d58,p20

[lux]



0 5 10 15 20 25 30 350

20

40

60

80

control cycle

y58

[lux]



0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

control cycle

u58



0 5 10 15 20 25 30 350

100

200

300

400

500

600

control cycle

w20

[lux]



Figure 5.9: Simulated control behavior through changes in all desks’ re-flectances (30%, 90%, then 60%) under changing daylight conditions, show-ing oscillation is prevented in the proposed VLC method.

42

0 1 2 3 4 5 6 7 8 90

10

20

30

40

50

60

70

control cycle

y3[lux]


(a) Total illuminance sensed with the ceiling-mounted photosensor.

0 1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

control cycle

u3[lu

x]


(b) Dimming level used by the luminaire.

0 1 2 3 4 5 6 7 8 90

100

200

300

400

500

600

control cycle

w3[lu

x]


(c) Total illuminance at the workplane.

Figure 5.10: Control behavior in experimental testbed through changes inobjects placed on desk (plain cardboard, white paper, then black fabric),showing both underillumination and overillumination are prevented in theproposed VLC method.

43

Chapter 6

Conclusions and future work

6.1 Conclusions

This thesis presented a novel intelligent lighting system. It makes use of acentralized control algorithm by networking between luminaires with visiblelight communication. By doing so, no dedicated hardware for communica-tion is required. Moreover, the use of VLC has been shown to allow forthe optical channel gains between luminaires and sensors to be extractedsuccessfully from received signals. This allows the proposed lighting con-trol system to adapt to changes in the environment, something state of theart lighting control systems using radio communication have not taken intoaccount.

The VLC communication method was evaluated in theory, in simulation,and with an experimental testbed, to reveal the limitations of its perfor-mance in terms of channel noise conditions. In an experimental testbedused, the normal operating conditions were found to be nowhere near theselimitations.

Environment changes were represented by changing surface reflectancevalues in simulation. In an experimental testbed, different objects commonin an office environment were placed on the workplane level, producing anequivalent effect to the changes applied in simulation. These realistic envi-ronment changes were shown to compromise the desired lighting behaviorin non-VLC lighting control systems, while the proposed system was able tocorrect for them.

The proposed system was presented with a level of generality. The sys-tem’s performance was described in terms of its parameters. Different lu-minaire topologies, environment sizes, and application fields will lead todifferent trade-offs between system parameter choices, like the communica-tion speed, the number of luminaires, or the desired duration of a controlcycle.

45

6.2 Future work

The implementation of VLC in this thesis is a sufficient one for its purpose.The performance may however be further improved. Amplitude modulatedcommunication with Manchester coding in TDMA scheduling has been con-sidered here for simplicity. Filtering the sensed signal at the symbol rate forthe purposes of bit detection may increase the SNR further. Furthermore,error correcting codes may yet be applied just as more advanced modulationschemes may be considered, so long as a notion of received signal strength(received modulation depth, here) remains in order to perform optical gainextractions. Also, the system may benefit from photosensors whose responseis more suited to high communication speeds. With an impulse responsecloser to a Direc delta function the effect seen in Fig. 5.4 is minimized.

In spacious environments with large numbers of luminaires, the proposedapproach may be met with additional challenges. The requirement thatone master luminaire is capable of communication with all other luminairesmay become infeasible. The implementation of message forwarding betweenluminaires may overcome this issue. However, another issue that ariseswith such large networks is the extended duration of communication itself,which determines the minimum time before a control cycle update may beachieved. Further research is thus required into possibilities of distributedcontrol networks or a seamless integration of several smaller centralized net-works within the same environment.

Note once more that it is impractical to mount photosensors on the work-plane level due to the ease with which they might become obstructed. Theproposed system translates desired workplane illumination to illuminationsensed at the ceiling with (4.12). This translation is an approximation andstill relies on a calibration step to obtain u300 and u500. With environmentchanges significant enough to constitute for example a renovation of the of-fice, this step will have to be repeated. While this is not an unreasonabletask, intelligent lighting system may still benefit from a method of more ac-curately mapping ceiling-mounted photosensors to workplane illumination.

The presented control algorithm updates all optical channel gains basedon the most recently extracted value. While this allows for immediate cor-rections for environment changes, user comfort may be increased by usinga method of filtering to smooth out changes in provided illumination. Also,only desired levels of illuminance have been taken into account as a factor ofuser satisfaction in this thesis. Future work may incorporate quality metricssuch as color temperature as well. Lastly, concerning user comfort, whilethe proposed intelligent lighting system provides the required amount of il-lumination in the absence of daylight, overillumination caused by daylightis not taken into account. Future lighting systems may be integrated withan intelligent system of blinds to protect against glare.

46

6.2.1 Internet of Things application

The proposed intelligent lighting system provides opportunities for network-ing beyond inter-luminaire communication for the purposes of lighting con-trol. In the context of the Internet of Things (IoT), external devices in theenvironment may also be enabled for VLC networking. One example of sucha device which may even be integrated with the proposed intelligent lightingsystem is a seat occupancy sensor which determines the presence of a userbased on a pressure sensor. Other sensors which provide helpful informationfor building management or even actuators such as a controllable system ofblinds may benefit from connectivity through VLC. An aspect of this exten-sion to an intelligent lighting sytem with IoT connectivity has been exploredover the course of this thesis’ work and is presented here.

Consider an indoor office space where ceiling-mounted luminaires act as apoint of global access with an IP address. Through VLC, data from externaldevices is collected at the luminaires and there made available to the outsideworld through other networking means such as power over ethernet [39, 40].In this manner, networking traffic is offloaded to the wireless optical channel.The applications suggested here have low demands on communication rateand are thus suited to VLC.

Challenges with such an interconnected intelligent environment includethe scheduling of its diverse devices, achieving synchronization betweenthem, and in the case of external devices which may be moved by users,the tracking of them. One approach could employ the luminaires for thepolling of the external devices. For an external device to be polled by one ofthe luminaires in order to receive its data, it must be known which luminaireis closest to it.

The proximity of an external device may be derived from the optical chan-nel gains extracted from messages it transmits. Let α′m,n for all luminairedestinations m be the optical channel gains extracted from a message orig-inating at source external device n. If no message was received between nand m, α′m,n = 0. These values are collected and processed by the lumi-naires through their point of global access. For the transmitting device n,the luminaire q?(n) of greatest estimated proximity is

q?(n) = arg maxm

α′m,n. (6.1)

This method of determining external device proximity is evaluated withthe experimental testbed described in Chapter 5. A transmitting VLC de-vice is placed at a workplane-appropriate height of 80 cm above the ground.The device is moved along the length of the office room under a row ofluminaires while oriented towards the ceiling. At thirteen evenly-spaced lo-cations between luminaire 2 and luminaire 8, the device transmits 512 bits ata communication speed of 1000 bits/s. The locations of these transmissionare shown in Fig. 6.1.

47

0 1 2 3 4 5 6 70

1

2

3

4

5

2

1

4

3

6

5

8

7

x [m]

y[m

]

Figure 6.1: Experimental testbed layout with locations of external devicetransmissions indicated.

0 1 2 3 4 5 6 7

2

4

6

8

luminaire 2 location




distance [m]

q?

Figure 6.2: Estimated luminaires of greatest proximity based on externaldevice position.

48

The luminaires each perform an optical gain extraction on every message.Using (6.1), the resulting proximities shown in Fig. 6.2 were found. Thesedetermined proximities match the closest photosensor at any given location.

Proximity determination as presented here is but one aspect of the VLC-interconnected environment. Further research is required to investigate thebenefits of IoT applications in indoor environments and their realizationwith VLC communication.

49

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Appendix A

Convex objective functionderivation

In order to apply quadratic programming methods, the objective functionof the optimization problem must be a convex function. A function f(x) isconvex for all x if the second derivative ∇2f(x) � 0 for all x [36]. Here,the relation � indicates the generalized inequality for positive semidefinitematrices. A symmetric matrix M is positive semidefinite when vTMv ≥ 0for any column vector v [41]. In the proposed optimization problem, theobjective function f : RN → R is

f(u) = (Au + d− l)T (Au + d− l).

The first derivative is shown by

∇f(u) =∂

∂u(Au + d− l)T (Au + d− l)

= (Au + d− l)T∂

∂u(Au + d− l) + (Au + d− l)T

∂

∂u(Au + d− l)

= 2AT (Au + d− l).

The second derivative is then shown by

∇2f(u) =∂

∂u2AT (Au + d− l)

= 2ATA.

Therefore, the object function (Au + d− l)T (Au + d− l) is convex if

2ATA � 0.

The matrix 2ATA is symmetric since 2ATA = (2ATA)T . Furthermore, forany N × 1 vector v it holds that

vT 2ATAv = 2(Av)T (Av)

= 2(Av) · (Av) ≥ 0.

55

Therefore, the proposed objective function is convex for all inputs to thecontrol algorithm.

56

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