Smart Lighting Using LED Luminaries

Sachin Bhardwaj, Tamr 6z�elebi, Johan Lukkien Department of Mathematics and Computer Science,

Eindhoven University of Technology P.O. Box 513, 5600 MB, Eindhoven, The Netherlands

s.bhardwaj@tue.nl, t.ozcelebi@tue.nl,j.j.lukkien@tue.nl

Abstract- The target of a smart lighting system is to control light sources in an environment (e.g. home, office) adaptively according to user contexts and preferences. Literature work in this area focuses on traditional light sources such as

incandescent and fluorescent lights, whereas this paper takes a step towards adopting LED luminaries. A novel illumination

model for distributed LED luminary control is presented. A prototype system is designed and implemented using several

LED luminaries and light sensors. Experiments carried out on the reading space use case show that the desired illumination can be achieved based on user preferences, irrespective of the existence of external light sources.

Keywords-Adaptive lighting; illumination nwdel; LED luminary; light control; light sensor; smart lighting; user preference.

I. INTRODUCTION

Light provides us with the ability to see. Either in a building or outside, light is a crucial ingredient of any task one may want to carry out. Furthermore, for any given task and place human being feel comfortable with certain levels of light intensity. For example a low light intensity level is preferable for watching a movie at home, while bright light is required for videoconferencing on a PC since a typical webcam is usually not able to capture good quality picture in low light conditions. Due to these reason, there is a growing demand towards lighting systems to meet user requirements for different use cases and in different places. Smart lighting can create various types of atmosphere such as romantic, relaxed, enjoyable, and comfortable feelings. Smart lighting is an important component of smart living. Experts predict that in the near future, smart lighting will replace ordinary lighting and become the industry mainstream.

Until now, smart lighting has been considered using traditional luminaries, including incandescent and fluorescent lights. Traditional lights cannot easily be applied to digital control due to technical deficiency. With incandescent lights, a synchronous control of color and intensity is very difficult, because increasing intensity raises the heat and shifts the color spectrum. On the other hand Light Emitting Diode (LED) luminaries can maintain lumen efficacy and light color in a larger sink current range, enabling digital control. Unlike conventional incandescent lamps which need to convert the electricity into thermal energy first and then to light, LED illumination is achieved when a semiconductor crystal is activated so that it directly produces visible light in a desired wavelength range.

978-1-4244-5328-3/10/$26.00 ©201O IEEE 654

Therefore, there is a rising interest of the research community in use of LEDs in smart lighting systems. LED lamps is started to be used in domestic (home) lighting for energy saving, extra long operating life and environmental safety [1, 2].

In this work, we have taken a step to adopt LED lamp in a smart lighting system for performing multiple tasks in a single room. We propose a smart lighting system, which can provide various light control strategies automatically based on user preferences. A set of LEDs, called an LED luminary, can be controlled on and off, and be dimmed according to user context and preferences. The room is divided into physical sub-spaces for different activities of a user, e.g. watching TV, reading books and video conferencing. We have considered reading space to design a prototype of smart lighting using LED luminaries. Several conditions and user preference types are considered to give desire illumination at reading work place.

The rest of this paper is organized as follows: In Section II, some background information on related work is given. In Section III and IV, the proposed architecture and the proposed illumination model are presented respectively. In Section V, the experimental results are presented. Finally, in Section VI, conclusions are drawn and future research directions are suggested.

II. BACKGROUND

The term "intelligent lighting system" refers a system where multiple lighting fixtures and sensors are connected and they cooperate, forming a network [3]. The main objectives of such a system are to provide energy saving on one hand, and user satisfaction on the other hand, by cooperation of individual nodes. Several researchers are working towards developing an smart lighting system [4, 5]. Thus far, research in this field has been focused mostly on utilizing traditional light sources such as incandescent and fluorescent. Energy savings of up 40% have been achieved by adopting lighting preferences such as daylight harvesting, occupancy sensing, scheduling and load shedding [6].

Nowadays, the adoption of LED lamps in daily lighting instead of incandescent, fluorescent and halogen lamps rise the opportunity of saving even more energy. Recently, a study has been carried out on LED light source as a study lamp with visual perception of user, comparing its performance with Compact Fluorescent Lamp (CFL) [7]. There was no substantial difference found in the visual performance using the two types of lamps. In [8], yet another

comparison has been made between LED lamps and incandescent lamps, and again no statically significant difference was found. The current trends in lighting industry show that LED lamps are going to be used vastly in domestic lighting [1, 2] in the near future. This drastic change is coming to the domestic lighting market for energy saving, extra long operating life and environmentally safe.

III. SYSTEM DESIGN

A physical space in a room is considered in which several sensors and LED luminaries are deployed. The set of LEDs need to be controlled according to the user preferences and the instantaneous measurements of the light intensity. Here the limiting factor is that the lightweight actuators (i.e. luminaries) and light sensors are of very small capacity (i.e. CPU, memory, energy) and they are not able to process data in a complex manner. Therefore, a centralized approach needs to be adopted, where a Knowledge Processor (KP) is given the light intensity measurements as its input and controls light output from the LED luminaries accordingly. The KP also needs to discover when new nodes (LEDs, light sensors) are added to the system and when the existing nodes are removed. The basic components of the smart lighting system including the KP and low capacity nodes are shown in Fig. 1.

KP

Light Output User and Event

Preferences � Illumination --. Generator -

Relationship

Illumination Model

Sensor Data Handler

i Sensor/

Actuator Mapping

t

I Services I

I Actuator L Handler I

._-------------------------- ---------------------------- ----------------------------- -------------------------------, Ugh, S'"':�,

S'""� LED Aom:::: A"aawn I", Sensor I 1 1 Sensor" LuminarYI I .... 1 Luminary"

Figure I. Basic components of smart lighting using LED luminaries

We assume that the user position in a room is known. When a user enters a room and reaches the reading space, luminaries (i.e. LEDs) will be triggered and try to provide the illumination level the user prefers to have while reading. The light sensors report the measured light intensity at their physical locations periodically to the sensor data handler of the KP. The LED actuator reports the status of each LED (on, off and brightness level) to the actuator handler. An actuator also receives commands from the event generator in

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order to provide the required light output. These commands given by the event generator are derived from an illumination model, which is based on user preferences.

The proposed adaptive smart lighting needs interaction of sensors and actuators via the KP as illustrated in the sequence diagram of Fig. 2. We assume that the user preferences are known to the KP, whereas the present status of light sensors and LED actuators need to be monitored continuously. Given specific user preferences and the corresponding illumination values, the KP tries to set the brightness of the LED actuators in an optimal manner. This mapping is derived from an illumination model, which is explained in Section IV. In case an LED is added to or removed from the system, the KP adapts the brightness levels of the new set of LEDs by sending the necessary commands, in order to maintain the user preferences.

I sen�ors I I K,P I I Acu�tors I , , , , status , , : status : ): i< . . . i present light output i present IlIumlnatlo�--------------------------, r---------------� :

status

p chec� for user prefererces

: set of light output : : >I , , , , , new LED added ' k : , , t==:> update , ,

IE : present illumination: �---------------�

status

P check for user prefere�ce

i new set of light output i : � , , : LED removed : � , ,

D update , ,

1< : present illumination: �---------------� , , i � check for user preference , :.-------- ,

i i new set of light output i : : ): " , " ,

Figure 2. Sensor and actuator interaction with KP

The proposed approach is explained in the following steps:

• The sensors will detect present illumination of the physical sub-space in a room and send information to the sensor handler of the KP.

• The actuators in the same sub-space of room will give information to actuator handler at KP about their present light outputs.

• The user preference is considered to set the light output for the desired illumination in sub-space for reading environment. (Assumptions: The position and activity of a user in the room is known to the KP). The KP will set light outputs of individual actuators by means of the illumination model.

• If a new LED is added into the system, the KP will discover the new LED and manage the light outputs of individual LED luminary according to the required illumination in the sub-space.

• If any LED is removed from the system, the KP will adapt the light outputs of the remaining LEDs in order to preserve the illumination in the sub-space.

IV. ILLUMINATION MODEL

The KP functions based on the proposed illumination model, which defines the relationship between the illumination at a given activity space (e.g. reading table, TV chair etc) and the corresponding light output (luminous intensity) from the LEDs. The illumination model takes into account sensor readings, brightness of individual LEDs, user preferences and other external sources of light, e.g. daylight. The LED luminary or luminaries can maintain illumination at an activity space by autonomously adjusting (dimming) their luminous intensity in the 0% to 100% brightness range.

An analysis of the relationship between illumination and luminous intensity has been given in [9]. The basic relationship can be formulated as:

Lxcos(O) E= 2 d

(1)

In order to calculate the illumination (E) at a particular point; the luminous intensity, L (candela) of the light source; the distance (meter) to light source, d; and the angle of light distribution, i.e. (), need to be known. A light sensor gives illumination value in units of lux, i.e. lumens per meter square, which is equivalent to the result of (1 ).

The lumen method calculates the number of luminaries (i.e. LEDs in this case) needed to produce an illumination level in a given activity space as follows:

Lumens Needed Ex d2 NumberojLEDs = = --

Lumens per LED x CU I x CU (2)

where the Coefficient of Utilization (CU) for LED is approximately 0.77, it is higher as compared to 0.7 for fluorescent because LEDs direct their entire light output downwards [10].

A sensor reading is affected mostly by the brightness of those LED luminaries closest to the sensor. Therefore, it helps to have a static mapping of the sensors and LEDs in order to maintain illumination in an activity space. The activity space is divided into grids of equal size. For the sake

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of simplicity, let us for now assume that these grids are squares of I meter square size. Let the room area be divided into MxN square grids, denoted by gm,n where 19n$M and 1'915N. A light sensor is located in each square grid, denoted by sm,n' For the sake of simplicity, let an equal number (K) of LEDs in one luminary be placed in each square grid. Then the mapping of sensors and LED luminaries to a particular square grid are as follows:

where LEDm,n denotes the set of K LEDs (Iedm,n,k) placed in gm,n as an LED luminary. At any time, the average of the readings from the individual light sensors gives the total illumination at the activity space, denoted by E, .

MN L L sm,n

E - m-ln-l ,- M·N

(3)

The illumination caused by only the LED luminaries, E/, is given by

M N I L LEm,n E - m=1n=1

1- M.N (4)

where E�,n denotes the LED illumination on gm,n. Note

that, in addition to E/, external light sources (e.g. daylight), also contribute to the total illumination by an amount of Ee. This relation is given by the following formula:

(5)

The illumination from external factors, i.e. E., cannot be controlled by the proposed system unless it is changed manually and externally. For example, more daylight contribution is expected when the user opens the curtains of the room. Therefore, the proposed system can adjust E, only by controlling E/. For this purpose, the brightness percentage of each LED luminary in gm,n, namely bm,n, is controlled by dimming. Here 0% and 100% correspond to ledm,n,k being turned OFF and being fully ON, respectively,for all k. The corresponding light output of ledm,n,k in units of lumens is denoted by Im,n,k . The total light output from one LED luminary (Lm,n) and all LED luminaries in the activity space (L) can be calculated as shown by (6) and (7).

K L = L I m,n m,n,k

k = 1

MN L= L LLmn m=1n=1 '

(6)

(7)

Let the unit of the increment and decrement value for brightness percentage at one time be b, resulting in a certain lumen value change for each LED luminary (Lm.n.b).

bxL L m,n

m,n,b = 100

(8)

The user preferences for the activity space are defmed as a minimum (Emin) and maximum (Em3X) illumination for m� m� each square grid, Um•n•

U . J �min Emax } m,n . �m,n' m,n (9)

For each square grid, we propose the following algorithm to meet the user requirements.

If sm.n:S E:::,� && bm.n <100 then bm.n = bm.n + b

If sm.n 2: E:::,�x && bm,n >0 then bm,n = bm.n - b

Assuming that Ee ::;; E:::,a:, in the steady state, the above

algorithm will achieve the required illumination on the activity space, i.e. Emin:SSm.n :SEmax. Finally, the set of LED m.n m.n brightness values corresponding to all square grids, B, is given as follows:

(10)

Addition/Removal of LEDs If an LED is added, removed or completed its lifetime in

any set of LEDs, then the KP will detect this easily by means of changing sensor readings. If the new illumination is outside the illumination bounds preferred by the user for the given square grid, the algorithm will automatically adjust the LED outputs to satisfy the user preferences.

We assume that the user preferences for each activity and the user activity (position) in a room known. In real life, user preferences may change over time and according to the task that user wants to do in the activity space. The illumination model adapts the light output on individual square grids according to the user activity and preferences.

V. USE CASE: READING SPACE

In the example use case, the dimension of the activity space (reading space) is ] -by-] meter square, which can be divided into 4 equal square grids represented by gl,b gu, g2,J and g2.2' Such an activity space can be described as a reading table and a chair for the user. Four light sensors (su, su, S2,J, sv) and four sets of LEDs (36 LEDs in each set) are placed in the center of each square grid which as shown in Fig. 3. Then the static mapping of each square grid with sensors and LED luminaries are as follows:

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gl,l � sl,I � ¥edl,l,l,ledl,I,2, ... ,ledl,I,36} gl,2 � sl,2 � ¥edl,2,I,ledl,2,2" .. ,ledl,2,36} g2,1 � s2,1 � ¥ed2,1,bled2,1,2, ... ,led2,1,36} g 2,2 � s 2,2 � ¥ed 2,2,1 ,led 2,2,2 , ... , led 2,2,36 }

....----.. Chair gl.1 L �gl/.2

[!] .... SetofLEDs

,!J ,!J Ught Sensor

g2.1 g2.2 [E [±] ,!J ,!J

Figure 3. Sensor and LEDs placement for reading space.

The illumination model depends on the user preference, which may change over time and according to the task that user wants to do in the activity space. For the reading space scenario, the user may prefer to have equal illumination at all point in the activity space (reading table) or may prefer unequal distribution of illumination among square grids. For example, more illumination may be required in grids that are close to the chair in which the user sits. These two types of choices are illustrated in Fig. 4.

gil gl2 gil gl2 250-300 250-300 250-300 250-300

g21 g22 g21 g22 250-300 250-300 200-250 200-250

Type I Type 2 Figure 4. User preference choices

Type]: The user requires equal illumination on each square grid, i.e. E:::,� =250 lux and E:::,";;' =300 lux for all m

and n, where l:Sm:s2 and 1:Sn:s2.

Type 2: The user requires more illumination near the chair (i.e. Eftin = Er'ln =250 lux and EB3x = Et2ax =300 lux)

and less illumination away from the chair (i.e. Emin = Emin =200 lux and Emax = Emax =250 lux) 2,1 2,2 2,1 2,2 .

VI. EXPERIMENTAL RESULTS

Given the reading space use case, we have tested the proposed approach on a prototype by using Phidgets [11] precision light sensors and LED driver board. In this prototype, the light sensors report data with the maximum frequency of 50Hz to the KP, measuring human perceptible

light level from I lux (moonlight) to 1000 lux (TV studio lighting). The 4 sets of 36 LEDs placed in each square grid of activity space are controlled individually by the LED driver board, which is attached to the KP for receiving inputs of precise light intensity. The LED driver is able to update the LED brightness values every 33 milliseconds (30 updates per second). Each LED can be turned on and off, and can be dimmed from 0% and 100% by means of Pulse Width Modulating (PWM) the current that goes through the LED. The source current can be changed between 0 rnA (0% output: off setting) and 30 rnA (100% output: brightest setting) for each LED as shown in Fig. 5.

100 ,-., :J. 80 � '-' Qj > 60 Q,) � '" '" Q,) 40 = ...

-= .!:.D 20 .. �

0 a 15 30

Cu .... ent (mA)

Figure 5. Brightness level vs current

We have considered the frequency of reporting light sensor data to the KP is 30Hz, which is equal to the updates on LED actuators and the brightness level increment/decrement factor b is 1%, i.e b=±l. The hardware nodes used for the prototype setup are shown in Fig. 6 (a) and (b).

64 LED COIlillec'tors!'

64-PhidgetLED Board

(a)

Photo Sensor�

Power Supply

--3> (6- 12V)

'"''''---'"USB Connector

Figure 6. (a) LED luminary actuator and (b) Light sensor

We have carried out two sets of experiments with the designed prototype, assuming three conditions (C 1, C2 and C3). In CI, only the installed LED light sources are available and no other sources of light are available and the

658

illumination from external sources (Ee) is negligible, i.e in the 0-10 lux range. In C2 and C3, Ee is in the 100 to 150 lux range and in the 250 to 300 lux range, respectively. Under these conditions, we get two sets of results considering Type I and Type 2 user preferences.

A. Type I user preference resutls

The brightness levels of 4 sets of LEDs are adjusted automatically based on illumination model. During CI, C2 and C3, the light outputs of the LED luminary sets are {bl,h b,,2, b2,,, b2,2}={100%, 94%, 95%, 90%}, {59%, 68%, 71%, 53% } and {O%, 18%, 7%, O%}, preserving the desired illumination (E() at 250�300 lux in the activity space consistently as shown in Fig 7 (a) and (b) respectively. In Fig 7(b), the peaks above 300 lux in transitions from CI to C2, and from C2 to C3 are due to instantaneous excess of light from external light sources, and are compensated by dimming the internal LED luminaries appropriately.

100 90 80 70 60 50 40 30 20 10 o

400 350 300 250 200 150 100

50 o

C2 -.-----CI

: .... :

b. C3 ---- ..... I X-axis time(x33ms) Y-axis brightness level(%)

1 1001 2001 3001 4001 5001 6001 7001 8001 9001 10001

(a)

CI C2 C3 --.II L JII

. -------

X-axis=time(33ms) Y -axis=illumination (lux)

1 1001 2001 3001 4001 5001 6001 7001 8001 900110001

(b)

-b11 -b12 -b21 -b22

-,11 -,12 -,21 -,22

Figure 7. Results of Type 1 user prefemece (a) light output (b) Illumination at activity space based

B. Type 2 user preference resutls

Similarly to the discussion above, in this case, the illumination levels of the individual square grids are set according to the user preferences and preserved under changing external light conditions. During Cl, C2 and C3, the algorithm sets the light outputs of the LED sets to {bl,,, b,,2, b2,,, b2,2}={100%, 98%,83%, 71%}, {61%, 70%, 41%, 34%} and {O%, 13%, 0%, O%}, respectively, as shown in Fig. 8(a). The desired illumination values for individual grids

are achieved and maintained as shown in Fig. 8(b). When the illumination due to external light sources is too high, i.e. when Ee > Emax, the algorithm turns the luminary in gm,n m.n OFF, as in C3 for g2I andgn.

100 �========�.---------------------90 �--------��--�

C2�'

--------------80 �========�--��--- -----------70 �--------��--------'r-----------60 �.------��--------�---------50 +----

C�

I�--�---------- fr_--------

40 �----------�--------�-----------30 �--------�'�"�"'�" �" �"'�" �"�" 20 +---------------------�·----------10 t-�X�- ��x�is4�in�le�(,�x3� 3�m� s)��----�========�

Y-axis=brightness level(%) o .f-����.:.:..:::...;..:...�:...:.c..--.-�:-....,..--.,.....�

C3

400 350 300 250 200 150 100

50 0

1 1001 2001 3001 4001 5001 6001 7001 8001 9001 10001 (a)

Cl C2 C3

� J '" tI

X-axis4ime(33ms) Y-axis=illumination (lux)

1 1001 2001 3001 4001 5001 6001 7001 8001 900110001 (b)

-b11 -b12 -b21 -b22

-sl1 -s12 -s21 -s22

Figure 8. Result of Type 2 user preference (a) light output and (b) Illumination at activity space.

The experimental results show that the desired illumination for individual grids can be achieved and maintained, if the external light does not exceed the maximum illumination preferred by the user.

VII. CONCLUSIONS AND FUTURE RESEARCH

This paper proposes a smart lighting system approach for distributed LED luminary control and an illumination model based on user preferences. The illumination model guarantees that the desired illumination levels of user preference are achieved. Given the illumination model, the brightness level of each LED luminary in the system is determined dynamically by a central knowledge processor such that the illumination in the activity space varies in the minimum-maximum illumination range specified by the user. Experiments have been carried out with the reading space use case. The experimental results show that the target illumination level for reading space can be achieved precisely by the proposed smart lighting system.

As future work, the proposed system will be examined also with other home use cases, namely, watching television and videoconferencing, and perceptual quality tests will be made on real test subjects. Note that the light intensity

659

requirements of all these use cases are different. For example, videoconferencing requires bright illumination to get good quality capture, whereas softer illumination is preferable when watching a movie on television. A perceptual user experience model will be devised and the results gained with this model will be compared with the subjective tests.

Our experimental results show that the proposed system is able to satisfy the preferences of a single user very successfully. On the other hand, it is still a challenge to design a smart lighting system that works in harmony in the presence of multiple users with diverse and possibly conflicting preferences for lighting. Obviously, the perceptual user experience model in this case will be more complicated than the single user case, since visual levels of comfort for every human being can be different.

ACKNOWLEDGMENT

This work has been supported by Smart Objects For Intelligent Applications (SOFIA) project funded by the European ARTEMIS programme. We also would like to thank Richard Verhoeven for his help in setting up the experiment environment.

REFERENCES

[ 1 ] M. Richards, D. Carter "Good lighting with less energy: Where next?" Lighting Research and Technology, Vo1.4 1 , No.3, 2009, pp.285-286, doi: 10. 1 1 77/ 14771 53509343493.

[2] T. M. Goodman, "Measurement and specification of lighting: A look at the future," Lighting Research and Technology, Vol.4 1 , No.3, 2009, pp.229-243, doi: 10. 1 177/ 1477153509338881.

[3] M. Miki, T. Hiroyasu, K. Imazato, "Proposal for an intelligent lighting system and verification of control method effectiveness" IEEE Cybernatics and Intelligent System, voU, Dec. 2004, pp.520 -525.

[4] W. Yao-Jung, AM. Agogino, "Wireless networked lighting systems for optimizing energy savings and user satisfaction" IEEE Wireless, Hive Networks Conference, Austin, Texas, USA, August 07-08, 2008, pp.I-7.

[5] M.-S. Pan, L.-W. Yeh, y'-A Chen, Y.-H. Lin; Y.-C. Tseng, "A WSN-based intelligent light control system considering user activities and profiles," IEEE Sensors Journal, Vol. 8, Issue 10, Oct. 2008, pp. 17 10-1721 .

[6] E . Mills, "Global lighting energy savings potential," Light and Engineering, vol. 10, 2009, pp. 5- 10.

[7] S. Varadharajan, K. Srinivasan, S. Srivatsav, A Cherian, S. Police, R. K. Kumar, "Effect of LED-based study-lamp on visual functions" in proceedings of Experiencing Light 2009, Eindhoven , The Netherlands.

[8] S. Newel, B. Albert, "Factors in the perception of brightness for LED and incandescent lamps," Society of Automotive Engineers Transactions, 2005, vol. 1 14, pp. 908-920.

[9] E. M. Guttsait, "Analysis of LED Modules for local illumination," Journal of Communications Technology and Electronics, Vol. 52, No. 12, pp. 1377-1395, 2007.

[ 10] R. Hall, "Overview of LEOs in general illumination applications," CREE LED Light, December 8, 2008.

[II] hUp://www.phidgets.coml.