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IEEE TRANSACTIONS ON SMART GRID 1
Smart Personal Sensor Network Control for Energy
Saving in DC Grid Powered LED Lighting System
Yen Kheng Tan, Member, IEEE, Truc Phuong Huynh, and Zizhen Wang
Abstract—Emerging smart grid technologies aim to renovate
traditional power grid by integrating intelligent devices and their
communications for monitoring and automation of the power grid
to enable efficient demand-side energy management. In this paper,
energy management in smart dc building grid powered dc elec-
trical appliances like lighting is investigated, in particular energy
savings from proposed personal lighting management strategy.
Unlike conventional fluorescent lamps powered mainly by ac grid,
LED luminaires are dc in nature, thus results in significant power
conversion losses, if operate on traditional ac powered system, are
analyzed with proposed dc distribution building grid for LED
lighting. This paper continues to explore the use of smart wireless
sensors for personal control of the dc grid powered networked
LED lighting. Experimental results show that the proposed smart
LED lighting system with an energy saving mechanism incor-
porated is able to achieve similar lighting performance as the
conventional lighting condition, while at the same time, able to
attain about 44% energy saving as compared to the original ac
fluorescent system. For a low voltage dc grid being implemented,
the maximum power loss and voltage drop of the developed dc
distribution building grid are 2.25% and 3% respectively.
Index Terms—LED lighting, smart building grid, smart personal
control, wireless sensor network.
I. INTRODUCTION
RECENTLY, emerging smart grid technologies are aiming
to renovate the traditional power grid by integrating
the advances in information and communication technologies
(ICTs). Intelligent devices and their communications provide
opportunities for monitoring and automation of the power grid
to enable efficient demand-side energy management. Within the
concept of demand-side energy management, energy manage-
ment in smart dc building grid powered dc electrical appliances
like lighting is recently attracting increasing interest from the
research community [1]–[3]. Integrated lighting controls for
demand-side energy management in building can significantly
improve its overall performance [4]–[6], increase energy effi-
ciency, and enhance occupant comfort [7] and satisfaction with
the built environment [8].
While previous research has shown that simple lighting
controls using occupancy sensors are effective at reducing the
amount of electrical energy used for lighting in commercial
Manuscript received January 16, 2012; revised March 29, 2012 and June 13,
2012; accepted September 09, 2012. Paper no. TSG-00027-2012.
The authors are with the Energy Research Institute, Nanyang Technological
University (NTU), Singapore 639798.
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSG.2012.2219887
buildings [9]–[11], advanced lighting control strategies have
the potential to achieve even greater energy savings and offer
many advantages over simple controls. But more advanced
control strategies, such as daylighting or load shedding, which
require a more systems-oriented approach, were less successful.
Some of these difficulties are a result of the horizontal structure
of the mass lighting controls market. While there are notable
exceptions, the market is comprised largely of manufacturers
of components (ballasts, switches, and controls devices) rather
than systems. Lighting controls components often do not work
well together when specified as systems, especially in dim-
ming applications where wiring is more elaborate. Lighting
control equipment for implementing more complex strategies
such as daylighting has proven difficult to commission in the
field, leading to poor operation and user complaints [12]. To
overcome these barriers, the approach is to slowly transit from
a primarily analog control world to a digital one with ICT.
Some recent studies reveal that there are some wired control-
lable lighting systems that measure the artificial and daylight
illumination by the use of sensors in controller area network
[13] or a set of data logger devices [14] to control the light in-
tensity and hence its energy consumption. However, such wired
network or devices require bundles of cable for data communi-
cation and transmission. It is a great challenge for the installa-
tion and maintenance of such systems for entire buildings, es-
pecially true for retrofitted buildings. To overcome this instal-
lation problem, the wireless sensor network (WSN) technology,
which has become more and more popular in the demand-side
energy management in smart grids applied in buildings [15], is
used. WSN is much easier and flexible to install and implement
than other wired network. With the combination of advanced
WSN-based ICT and dc grid powered LED lighting system,
the package of advantageous features generated from this com-
bined technology should lead to greater energy savings at the
demand-side of the green smart building grid.
In this paper, a smart personal sensor network, distributed
into the workspace of the building area, is connected to the dc-
grid powered LED lighting system to: 1) collect ambient
intelligence for indoor environment quality monitoring, 2)
conduct real-time control and adjustment of human-lighting
interaction, and 3) optimize/minimize the energy usage of the
building lights. Personal control is incorporated to bridge the
gap between the building design that attempts to satisfy the
majority and people who have very different needs based on a
range of factors like lighting intensity, thermal comfort, etc. The
objective of this personalized dynamic design of networked
lighting is to reduce as much unwanted wastage of energy as
possible and improve the comfort of workers in office buildings
1949-3053/$31.00 © 2012 IEEE

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2 IEEE TRANSACTIONS ON SMART GRID
[16]. Another aspect of this research work, other than the
human-lighting interaction, is to focus on lighting-building grid
interaction whereby the challenges faced by the demand-side
smart dc building grid under dynamic personalized control are
explored.
The rest of the paper is structured as follows: Section II
presents the overview of dc grid powered LED lighting system,
Section III describes the proposed concept of personal sensor
network control of LED lighting system, Section IV illustrates
the implemented test-bed and its experimental results are
illustrated in Section V, lastly a conclusion in Section VI.
II. OVERVIEW OF DC GRID POWERED LED LIGHTING SYSTEM
In any building environment, there exists plenty of opportu-
nity to attain energy saving as lighting accounts for the second
largest primary energy consumption in buildings annually [17].
With recent advances in the development and mass produc-
tion of light-emitting diodes (LEDs), the possibility of replacing
conventional light sources for in-built environment with higher-
power LED luminaires has been realized [18].
LED luminaires offer significant advantages of higher
efficiency, longer lifetime and lower maintenance over con-
ventional lighting sources like incandescent, fluorescent and
halogen lamps to enable more energy saving in buildings [18].
However, LED lighting is an inherently dc electrical load
whereas the power supply grid installed in buildings is in ac
form. A research work presented by Chen et al. in [19]
proposed a driving technique that operates electronic ballasts of
fluorescent lamps to drive LEDs without requiring the replacing
of any electronic ballast circuitry or modifying the existing
infrastructure of a lighting network. Even so this is a viable
solution, an extra power converter is needed to convert the ac
power from conventional ac grid to constant direct current (dc)
for powering the LED luminaire.
To overcome this drawback of having to use the ac-dc power
converter to condition the electrical power for the LEDs and
incur several power conversion losses, a low voltage level dc
grid, which is relatively much safer than conventional ac grid,
has already been proposed by Koh et al. [20] and Kurohane et
al. [21]. According to the findings reported by Cheng et al. [22]
and Zhou et al. [23], a dc grid is more energy-efficient in pro-
viding electrical power to the LED lighting system. The block
diagram of a typical lighting system commonly used in com-
mercial or residential buildings using gas discharge/fluorescent
lighting (FL) system as compared to an energy-efficient dc grid
for powering LED lighting system are illustrated in the left and
right sides of Fig. 1 respectively.
Within any building of today, the 110/230 , 60/50 Hz ac
power grid is the only power supply available to power both ac
and dc electrical loads. In this case, as can be seen in the left side
of Fig. 1, the fluorescent lamps are powered by the ac grid via
their onboard electronic ballasts that consist of two power con-
ditioning stages (see Fig. 1); 1) an ac-dc power factor correction
circuit and 2) a dc-ac high frequency inverter with an output res-
onant tank circuit. Unlike these conventional fluorescent lamps
powered mainly by the ac grid, LED luminaires and many other
electronic loads within in-built environment are dc in nature,
thus resulting in significant power conversion losses if the dc
Fig. 1. Overview of conventional ac grid powered fluorescent system and dc
grid powered LED system.
Fig. 2. Layout of networked LED lighting system.
loads are to operate on traditional ac powered system. As such,
referring to the right side of Fig. 1, an ac-dc power converter is
employed to convert the existing 230 power source coming
from the public utilities into a much lower voltage and safer dc
power source to power these dc LED luminaires directly. If a dc
renewable source, i.e., solar and/or fuel cell are available, direct
dc output from the dc renewable source can be supplied to the
LED lights and dc loads while at the same time hybrid the dc
grid with the building ac grid.
III. PERSONAL SENSOR NETWORK CONTROL OF LED
LIGHTING SYSTEM
The dc grid powered LED lighting system is constructed into
a 3-D virtual office workspace of 70 as shown in Fig. 2 and it
is simulated with various design parameters such as lu-
minaires layout, reflectance of ceiling, walls and floor and other
key measurable parameters, i.e., light intensity, daylight, occu-
pancy, etc., for the floor area. The simulation results of the lu-
minaires and their lighting isolines are depicted in Fig. 3.
Referring to the lighting simulation illustrated in Fig. 2, the
height of the designed working plane is set as 0.8 meters from
floor level and altogether 9 sets of 24 54 W
LED luminaires and 5 sets of 24 19 W LED down lights are
installed on the ceiling of the target office. The layout of
networked LED lights designed in Fig. 2 is then simulated to

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TAN et al.: SMART PERSONAL SENSOR NETWORK CONTROL FOR ENERGY SAVING 3
Fig. 3. Luminaires isolines simulation.
generate the isolines of the room lighting condition described in
Fig. 3. This is the typical step taken by a lighting designer in its
initial design for the ambient lighting of an office area. As can
be seen in Fig. 3, at the center of the room where the highest
illuminance exists, it is read that the lumen value, measurable
on the office desk 1 meter from the ground, is around 550 lux.
However, as the simulated lux points move towards the edge of
the room, it is observed in Fig. 3 that the illuminance starts to
drop and near the office walls is the lowest with only 220–330
lux. According to the Illuminating Engineering Society of
North America report [24], the standard illumination for an
office space is to be between 300 lux to 500 lux. For the layout
of networked LED lighting placed in Fig. 2, it is able to gen-
erate lighting isolines (see Fig. 3) for the workplace areas in the
room with more than the standard illuminances of 300 lux.
A. Personal Wireless Sensors for DC Grid Powered Networked
LED Lighting
For the office space depicted in Fig. 2, the isolines analysis
of the room brightness illustrated in Fig. 3 shows that the of-
fice staffs are having either sufficient lighting of around 300 lux
or brighter than required lighting of up to 500 lux. This phe-
nomenon poses an opportunity to recuperate some wasted en-
ergy while bringing down the lighting intensity of the room to
suit the personal preferences of individual office user. On top of
that, with this personalized feature, the office usage behaviour of
each user, i.e., sitting at his/her desk, walking around common
spaces, etc., is able to further cut down the energy consumption
of lighting system and its dc grid. This is where inexpensive
and mini personal wireless sensors are required, instead of those
fixed conventional bulky sensors mounted on the ceiling. The
personal wireless sensors are distributed throughout the office
space in a network form to acquire ambient information as in-
telligence for use in control of the dc-grid powered LED lighting
system.
Fig. 4. Positioning of wireless sensor nodes distributed in the workplace.
In the proposed smart WSN-based LED lighting system, the
context ambient information from the user’s environment is
obtained and managed intelligently through sensor network to
provide an adequate interaction between the users and their
surrounding environment. Take for example; light dependent
resistor (LDR) sensors measure illuminance, whereas pyro-
electric infrared-red (PIR) sensors detect the movements of
inhabitants. Comparing with the commercially available com-
puter-based lighting control systems, which are mostly open
loop types and the sensor data is not fully exploited [25] [26],
the proposed system collects these output context information
from the distributed sensor network for use as feedback infor-
mation to control the LED lighting. In order to make sure each
user of the lighting area has its own lighting preference, such
context information must be processed by self-adaptable and
dynamic mechanisms to satisfy independently of each partic-
ular situation. In addition, the control requirement becomes
more complex when the natural day light levels is neither
changeable nor controllable; the combined illumination of the
sunlight with the LEDs is measured by the lighting sensor to
ensure constant preset light intensity at sensor’s ambient
environment. Therefore in this paper, the closed loop approach
is used to control the light intensity of the LED lighting and
then adjust the ambient light at user’s location as illustrated in
Fig. 4.
The wireless sensor network (WSN) based LED lighting
system consists of 9 distributed wireless sensors, also known
as end device (ED) nodes, wirelessly connected to an access
point (AP) node, a personal computer (PC) and a digital ad-
dressable lighting interface (DALI) controller to control 14 sets
LED lighting arrays as shown in Fig. 1. The AP of the WSN
communicates with the DALI controller of the LED lighting
via a standard RS232 serial communication protocol of the PC.
With the proposed system, the ambient intelligence collected
by the WSN is used to influence and control the way the LED
lighting system operates in order to conserve LED lights and
their luminance, hence energy, while maintaining the indoor
lighting condition to be within the standard lighting between
300–500 lux.
Based on the lighting lumen expectations and needs of dif-
ferent users, diverse references have been set in the controller
for each user. After the sensors detect the illuminance of the
ambience as illustrated in Fig. 5, the sensed data is transmitted
back to the base station in a wireless manner. The base station
communicates with the central controller to calculate and adjust
the brightness of the LED lighting for several times, the illumi-
nance on the desks is then able to reach the reference lighting

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4 IEEE TRANSACTIONS ON SMART GRID
Fig. 5. Closed loop LED lighting control scheme.
Fig. 6. Deployment of smart personal sensor network control for energy saving
in dc grid powered LED lighting system.
set points defined by the each individual user. By having the
self-autonomous wireless dimming feature, the unused light,
hence its energy can be conserved.
B. Smart Control Algorithm for Energy Saving in LED
Lighting System
Based on the personal sensor network based LED lighting
system platform, a smart control algorithm is proposed to
achieve energy saving in the LED lighting system. An overview
of the wireless LED lighting control scheme being proposed is
illustrated in Fig. 6.
The onboard microcontroller of a wireless sensor node is re-
sponsible for processing the output signal of the LDR sensor
that measures the ambient light intensity contributed from both
daylight and LED lighting. The equivalent voltage of the sensed
lumen value is converted into a digital number per-
formed by an onboard 10-bit analog-to-digital converter. This
digital output is calculated by the closed loop control approach
with the set point set by users as illustrated in Fig. 5. Re-
ferring to Fig. 5, this net difference, denotes as error signal, is
amplified and controlled by the proportional-integral (PI) con-
troller. The output control signal, , is sent to the driver circuit
of the LED luminaires to adjust the light intensity and regulate
the difference, , to close to zero value. With this closed loop
control integrated into the LED lighting system, if the ambient
illumination changes abruptly, the system can react according
to the continuous awareness of the light intensity received from
the sensor at any time. This digital output is calculated by the
closed loop control approach with the set point preset by users.
From a macro point of view, for the lighting control problem
formulation, each sensor node, , where has a
utility function, , representing the close relationship
amongst the duration to be controlled, the ambient illumination
and the corresponding light output from the LED lighting. With
this setting, the function, , is defined as follows:
(1)
A particular setting, , control the specific light intensity at
the location of the sensor node while the timer, , indi- cates
when the PIR sensor control mode, , is performed to- gether
with the utility function, . The LED lighting system will be
turned on/off or dimmed automatically during office hours
(from 8 A.M. to 6:30 P.M.). After office hours (6:30 P.M. to 8
A.M.), as usual the lighting system is automatically turned off
by LDR sensor-based control system; however, the lighting
system at the locations where the staffs are still working needs
to remain in its on-state based on PIR sensors expressed as
(2)
Once the PIR sensors detect the movement of any human
, the wireless sensor network feeds the sensed occu-
pancy signals back to the access point (AP) node in a wireless
manner. The PC will identify the respective LED lights which
are still in need by the individual who is still at work and send
out the control signals, , to that individual LED lighting. The
illuminance level, , is again controllable according to the
user’s preference. The other areas of the workplace which are
not in use after office hours will have their respective
LED lighting turn-off to achieve energy conservation
in the building.
Beside the task of minimizing the energy usage, the smart
LED lighting system also offers satisfaction for individual
users’ lighting preference. In the building offices, the common
arrangement for working places is to share the space where the
illumination on each of the working space is mutually affected
by others. And different users have their own preferred illumi-
nation level. Even though the standard illumination for office
is between 300–500 lux [24], some office users still prefer the
lighting conditions to be at lower level in between 300 lux to
400 lux. In this case, with dimmable function, the smart LED
lighting system allows the users to select their preferred
illumination by setting the individual sensor node located at
each office desk. The system automatically recognizes the
address of each sensor node together with LED lighting, and
then controls that particular LED lighting to match with the
individual user’s reference. This approach optimizes the light
intensity to the satisfaction of every user.
IV. IMPLEMENTATION TEST-BED
The proposed personal sensor network control dc grid pow-
ered LED lighting system is depicted in Fig. 6. As can be seen
in Fig. 6, there is a dc power mains panel that houses 2-unit of
power factor correction (PFC) converters, each with power
rating of 750 W, running at greater than 95% power factor to
output a 24 supply along the dc grid to the connected LED

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TAN et al.: SMART PERSONAL SENSOR NETWORK CONTROL FOR ENERGY SAVING 5
lighting system. The digital communication between the ad-
dressable LED lighting system and the personal sensor network
distributed into the office space is a hybrid interface of stan-
dardized wired communication, DALI, and wireless communi-
cators. Through this hybrid communication means, the ambient
information is gathered by the sensors in a wireless manner and
then transmitted to the main PC, as illustrated in Fig. 6, to en-
able individual addressable ballast to switch on-off or dim the
respective LED luminiaire.
The personal sensor nodes platform employed in this
research composes of an ultra-low-power MSP430F2274 mi-
crocontroller and a CC2500 2.4 GHz wireless radio transceiver
arranged in a star topology form within the specified 70 office
space. Taking advantage of the small constrained size of the
office space, which is well within the capability of wireless
sensors without compromising on signal communication loss,
a single-hop star topology WSN with all the wireless sensor
nodes within direct communication range to the gateway, is
implemented. The implementation cost of the proposed system,
excluding the expensive LED luminaries, is quite comparable
to the conventional fluorescent lighting system using similar
wirings and protection subsystems. The extra cost for having
more sensor nodes is also trivial as compared to the overall
system cost, probably 1%.
The address information of each LED lighting and the sensor
nodes are encoded to ensure that each sensor node controls its
respective LED lighting. Taking into account the LED lighting,
the preference value of each user for each sensor is changed
flexibly versus the specific location of that sensor node. The
LED lighting system is controlled by the proposed smart wire-
less sensor network as illustrated by the following steps:
• Decision Making Algorithm: To determine the time to
change the mode: LDR sensor and PIR sensor.
• Detection Phase: To detect and calculate the lumen value.
• Receive the output signal from sensor node, classify this
signal and then send it to DALI controller to control the
LED lighting.
The access point (AP) of the distributed personal sensor net-
work, like the base station of a star wireless sensor network, is
tasked to calculate and determine the time to control the sensing
frequency of sensor node as well as to operate the mode of PIR
sensors whenever possible. For example, LDR sensors control
the LED lighting system during office hours from 8:30 A.M. to
5:45 P.M.; after office hours the mode which human movement is
detected by PIR sensors is activated. An overview of the lighting
control schedule set in accordance to occupants’ activities in a
full day is shown in Fig. 7. After the time is specified, a message
from AP is sent to end devices (EDs) in the personal sensor net-
work control system to select the operation modes. Based on this
information, an internal timer in each ED is activated to count
the operation time to change such modes properly.
To adequate control the dc grid powered LED lighting system
to react according to the lighting need of individual office user,
a closed loop PI control scheme has been employed to real-time
adjust the brightness of the LED luminiaires. In the proposed
control loop, the respective sensor of the wireless node, acti-
vated based on the designed decision making algorithm, senses
the value of either lighting condition or occupancy for feedback
Fig. 7. A full day lighting control schedule set in accordance to occupants’
activities.
periodically in every two seconds to the PI controller. By com-
paring with the preset user-defined reference value, the desired
control signal, in terms of a specified dimming percentage or
on/off level, is outputted from the AP node via the DALI net-
work to control the LED luminiaire. Note that the address of
each LED light is encoded into its assigned controlling sensor
node. At times, a predetermined group of LED lights is con-
trolled by one sensor node. By controlling the intensity of LED
lighting to reach the satisfactory level and in combination with
the use of the day lighting, it is seen that the energy is used ef-
ficiently with the best effort for energy saving.
V. EXPERIMENTAL RESULTS
The smart personal sensor network controlled dc grid pow-
ered LED lighting system has been implemented and the exper-
iment results are discussed here. Several experiments were con-
ducted on the developed smart LED lighting system powered by
dc grid to understand its transient and steady state performance
under various office users’ needs and working behaviour.
A. Luminance Analysis of LED Lighting System With Closed
Loop Personal Control
For comparison purpose, the original office lighting fitted
with T8 fluorescent lamps powered from ac grid supply was
retained while the dc grid powered LED lighting was installed
into the same ceiling to carry out fair experiments. The mea-
surements of the indoor illuminance of the office space were
conducted using a portable lux meter at different time intervals
of the day. Several sets of experimental results were recorded
namely: the condition of LED lighting with wireless sensor
control was measured first, followed by the condition of LED
lighting without dimming or control. The average illuminance
graph of the office space with traditional T8 fluorescent and
LED lighting is shown in Fig. 8.
Referring to Fig. 8, it is observed that the average bright-
ness of the office space, throughout the working day, illuminated
with personal sensor network controlled LED lighting remains
around 400 lux. The closed loop control system with the lighting
intensity feedback has exhibited its performance to personalize

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6 IEEE TRANSACTIONS ON SMART GRID
Fig. 8. Comparison of Lux measurement with: (a) AC Luminaires (AC FULL),
(b) LVDC LED luminaires (DC FULL), and (c) LVDC LED luminaires with
Smart Wireless Sensors (DC Sensor).
the lighting to the users’ needs and reduce excessive usage of
electrical energy. Fig. 8 showed that in mid afternoon, i.e., 11
A.M.–12 P.M., when indoor ambience becomes brighter due to
natural daylight, LED lighting with wireless sensor compen-
sation control algorithm was still able to keep the office desks
within stable ambient lumen level through dimming so that en-
ergy from dc grid is conserved automatically. The illuminance
graph in Fig. 8 shows that the retrofitted LED lighting system
has 150–200 lux of illuminance on average more than the fluo-
rescent lighting system, so there is plenty room for adjustment
using the LED lighting system to control and keep the office am-
bience on a desirable lighting level. LED luminaire employed
can be dimmed when its input current decreases, while its input
voltage essentially constant.
To evaluate the performance of the designed closed loop PI
controller and track the lux set-point defined for the brightness
of each desktop in the office, several experiments were con-
ducted on the proposed smart LED lighting system under dif-
ferent lighting conditions. The light intensity is controlled by a
step change scenario from 530 lux to 390 lux respectively. Fig.
9 shows the lux value of light intensity response which fol-
lows the reference set-point firmly with negligible small steady
state error. The lux value of the light intensity is measured by a
handheld light meter (Tenma 72-6693). Notice that, the time of
controlling is about 10 minutes to reach the steady state in these
experiments with data acquisition of 2 seconds in this system.
A gradual change in the light intensity is preferred by the users
instead of a fast and abrupt lighting response.
Referring to Fig. 9, at steady state condition, the maximum
percentage error between the measured light intensity and the
reference lux value is 1.53%. This minimal percentage error
comes from various sources: sensors, location of installed sen-
sors and ambient light intensity. Another observation seen from
Fig. 9 is that there exist some minor fluctuations in the LED
lighting system. This fluctuation is so small that it is almost im-
possible for human eye to sense it [16]. Hence, a stable lighting
supplied by the smart LED lighting system which totally meets
with users’ preference set-points is achieved. On top of that,
with the ability to control and dim the brightness of the room
according to the users, it implies that the redundant and wasteful
lumen generated from the LED lights system can be conserved,
hence saving energy.
Fig. 9. Performance of wireless sensor network-based smart lighting system
with ambient intelligence.
TABLE I
ENERGY USE (kWh) BY DIFFERENT LIGHTING SYSTEMS AT VARIOUS
REFERENCE CONTROL
B. Energy Saving Evaluation of DC Networked Lighting With
Smart Personal Sensor Control
The experiments conducted to evaluate the energy saving
level of the proposed dc networked lighting with smart personal
sensor control are accomplished in three different settings: a)
existing 9 sets of ac lighting with 2 28 W 4 T8 lamps, b) low
voltage dc (LVDC) powered LED luminaires with full bright-
ness, and c) LVDC powered LED luminaires controlled by a set
of smart personal sensors embedded with closed loop control
algorithm in the PC, which will allow continuous monitoring
of ambient lighting level. The energy consumptions of the three
different lighting systems are summarized in Table I.
Referring to Table I, in general, a dc-grid LED lighting
system is more energy-efficient than a fluorescent lighting
system. At similar lux level spread across the office space, the
LED system consumes 13.5% of less energy than its counter-
part lighting system. Another 10% of energy saving is achieved
when the reference lux value of the smart controlled LED
lighting system is set as 500 lux, the standard illumination for
office between 300–500 lux [24]. Finally, referring to Table I,
it is read that the smart wireless personal sensors and dimming
control helps the LED lighting system to save as much as 44%
energy of the original fluorescent system yet offering the same

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TAN et al.: SMART PERSONAL SENSOR NETWORK CONTROL FOR ENERGY SAVING 7
illuminance in the offices. The payback period for the cost of
the system from 44% energy savings versus the capital cost of
putting it in is around 3–5 years, depending on the electricity
tariff rate.
VI. CONCLUSION
In this paper, a low voltage dc lighting system with wireless
sensor network control is developed. It is clearly demonstrated
that LVDC LED luminaires has a far superior lumens per watt
performance compared to traditional ac lighting. With the pro-
posed smart lighting system, the ambient light at the user’s lo-
cation is able to be controlled in real-time to give users the most
amount of indoor environmental quality but in an energy effi-
cient manner. Moreover, wireless lighting sensors help realize
better energy conservation and the human interaction with the
indoor lighting system by dimming LED lighting according to
set points in the controller. The LVDC LED system saves as
much as 44% energy compared to the original T8 fluorescent
system. Finally, the power losses in the dc grid are studied and
the experimental results show that a bigger energy saving po-
tential if the quality of the wires used can be improved.
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Yen Kheng Tan (S’02–GS’06–M’11) received the
B.Eng. degree in electrical and computer engineering
from the National University of Singapore (NUS),
Singapore, in 2003, the M.S. degree in technological
design (mechatronics engineering) degree jointly of-
fered by NUS and the Eindhoven University of Tech-
nology (TU/e), Eindhoven, The Netherlands, in 2006,
and the Ph.D. degree from NUS in 2011.
He is a Research Scientist (Group Leader—Energy
Harvesting & Sustainable Building Technologies) at
the Energy Research Institute, Nanyang Technolog-
ical University (ERI@N), Singapore. He leads a research group comprising of
Ph.D-, Master-, and Bachelor-trained researchers to conduct research, design,
development and testbedding on various academic and industrial projects. Key
projects include: a) self-autonomous energy harvesting/scavenging systems, b)
smart TCP/IP-connected wireless sensor network (WSN) and optimal control
algorithms for LED lighting control, facilities IEQ monitoring, etc.; c) dc re-
newable connected grid and its high and low voltage power conversion inter-
face; and d) wireless power transfer systems in mid and far fields, i.e., high
frequency (MHz) high power (kW).
Dr. Tan is an invited member of InTech Scientific and SpringerOpen Edito-
rial Board and he is the book editor of more than 6 books published separately
in Taylor & Francis, John Wiley, InTech, and Sciyo. He is a committee member
of the IEEE Industry Applications/Power Electronics Joint Chapter and serves
as the Technical Programme Chairperson for the IEEE Power Electronics and
Drive Systems (PEDS, 2011) and International Conference on Sustainable En-
ergy Technologies (ICSET, 2012) conferences.

8. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception ofpagination.
8 IEEE TRANSACTIONS ON SMART GRID
Truc Phuong Huynh was born in Binh Dinh,
Vietnam. She received her B.Sc. degree from the Ho
Chi Minh City University of Natural Sciences,
Vietnam, and her M.Sc. degree from the Nanyang
Technological University (NTU), Singapore.
She has worked with the Energy Research Institute
at NTU for two years, as a Research Associate. Her
current research interests include design, analysis and
programming for embedded systems used for wire-
less sensor networks in green/smart buildings.
Zizhen Wang received the B.Eng. degree in au-
tomation from Xi’an Jiaotong University, China, in
2009, and the M.Sc. degree in computer control and
automation from Nanyang Technological University,
Singapore, in 2010.
From 2011 to 2012, he was a Research Associate
with Energy Research Institute at Nanyang Techno-
logical University, Singapore, where he was involved
with intellectual lighting control, neural network, and
optimization process.