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Cost Analysis of Smart Lighting
Solutions for Smart Cities
Giuseppe Cacciatore†, Claudio Fiandrino‡, Dzmitry Kliazovich, Fabrizio Granelli†, Pascal Bouvry?
†Dipartimento di Ingegneria e Scienza dell’Informazione, University of Trento, Italy
‡Imdea Networks Institute, Leganés, Spain
ExaMotive, Luxembourg
?Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg
E-mails: †giuseppe.cacciatore@studenti.unitn.it, fabrizio.granelli@unitn.it, ‡claudio.fiandrino@imdea.org,
kliazovich@ieee.org, ?pascal.bouvry@uni.lu
Abstract—Street lighting is an essential community service, but
current implementations are not energy efficient and and require
municipalities to spend up to 40% of their allocated budget.
In this paper, we propose heuristics and devise a comparison
methodology for new smart lighting solutions in next generation
smart cities. The proposed smart lighting techniques make use
of Internet of Things (IoT) augmented lampposts, which save
energy by turning off or dimming the light in the absence of
citizens nearby. Assessing costs and benefits in adopting the
new smart lighting solutions is a pillar step for municipalities
to foster real implementation. For evaluation purposes, we have
developed a custom simulator which allows the deployment of
lampposts in realistic urban environments. The citizens travel
on foot along the streets and trigger activation of the lampposts
according to the proposed heuristics. For the city of Luxembourg,
the results highlight that replacing all existing lamps with LEDs
and dimming light intensity in the absence of users in the vicinity
of the lampposts is convenient and provides an economical return
already after the first year of deployment.
I. INTRODUCTION
World population living in cities has experienced an unprece-
dented growth over the past century. While only 10% of the
population lived in cities during 1900, nowadays this percentage
corresponds to 50% and is projected to increase in upcoming
years [
1
]. Sustainable development plays therefore a crucial role
in city development. While nearly 2% of the world’s surface
is occupied by urban environments, cities contribute to 80%
of global gas emission, 75% of global energy consumption [
2
]
and 60% of residential water use [1].
With the aim of improving citizens’ quality of life, sig-
nificant research efforts are undergoing to provision citizens
innovative and sustainable solutions for public services such
as healthcare and wellbeing, safety and smart transportation
among the others [
3
]. To achieve these objectives, smart cities
rely on Information and Communication Technology (ICT)
solutions [
4
], [
3
]. The application of the Internet of Things
(IoT) paradigm to urban scenarios is of special interest to
support the smart city vision [
3
], [
5
]. IoT is indeed envisioned
as the candidate building block to develop sustainable ICT
platforms. In IoT, everyday life objects are “smart”, i.e., they
are uniquely identifiable and are equipped with computing,
storage and sensing capabilities and can communicate one with
each other and with the users to enable pervasive and ubiquitous
Dr. Claudio Fiandrino developed this work as a Ph.D. student at the
University of Luxembourg.
computing [
6
], [
7
]. Proper exploitation of the variety and the
potentially enormous volume of the data generated by these
devices will foster the development of innovative applications
in a broad range of domains. Including citizens in the loop
with crowdsensing approaches augments capabilities of existing
infrastructures without additional costs and is proved to be a
win-win strategy for smart city applications [
8
], [
9
], [
10
], [
11
].
Public street lighting
1
is a traditional service provided by
lampposts distributed on streets and roads. Equipping lampposts
with sensors and communication technologies, thus making
them IoT-based, enables a number of new services. Lampposts
can be employed to monitor traffic, noise and air pollution,
increase coverage of cellular and WiFi networks and enable
Visible Light Communications (VLC) [
12
], [
13
]. Unfortunately,
the cost of deploying new IoT-based lighting infrastructure is
high, while the benefits are often unclear and need to be
quantified. For this reason, in this paper we provide an analysis
on costs and benefits in deploying an IoT-based infrastructure
for the sole service of public lighting. We infer that assessing
the effectiveness IoT-augmented lampposts for their primary
service is essential to motivate municipalities in investing into
the development of new infrastructure.
In this paper, we develop three new heuristics for smart
lighting that are that are designed to reflect technology used in
lamps. For example, lamps that are based on Light-Emitting
Diode lamps (LEDs) can light up/dim the light intensity,
while High Pressure Sodium (HPS) cannot. As a result, LEDs
combined with sensing of presence can be used to dim the light
intensity when people are passing in the lampposts’ vicinity. On
the other hand, HPS-based lampposts can only turn on or off
the lamp. This makes each smart lighting solution to bring in
different level of energy saving. For performance evaluation, we
exploit a custom-built simulator, where lampposts are deployed
in realistic urban environments. Users walking on streets trigger
activation of the lampposts according to different smart lighting
solutions. The results show that in a pedestrian area with nearly
500 lampposts, replacing all existing lamps with LEDs becomes
beneficial already after the first year of deployment. Adopting
HPS lamps and turning them on in the presence of users and
off otherwise reduces annual operational expenditures by nearly
1
In this paper we use the terms street lighting, public lighting and smart
lighting interchangeably.
Table I
COMPARISON OF LAMP FEATURES AND TECHNOLOGIES
TYP E OF LAM PS NOMINAL WATTAGE (W) LAMP EFFIC ACY (L M/W) ENERGY CONSUMPTION (KWH/1000 H) AVE RAGE L IF E (H)
HPS-97241 150.0 110.0 172.7 24 000
HPS-93010296 250.0 129.0 283.4 24 000
MH-NaSc 100.0 90.0 165.0 10 000
LED-GRN60 46.8 131.0 51.8 100 000
LED-GRN100 73.3 138.0 82.7 100 000
60% with respect to current implementations.
The rest of the paper is organized as follows. Section II
presents background on smart lighting, including comparison
of the available technologies and current trends. Section III
proposes the three new heuristics. Section IV details the
comparison methodology used in Section V for performance
evaluation. Section VI concludes the work outlining future
research directions.
II. BACKGROU ND O N SMA RT LIGHTING
The Europe 2020 Strategy defines three targets for cli-
mate change and energy: (i)
20%
reduction of greenhouse
gas emission, (ii)
20%
increase in energy production from
renewable sources, and (iii) at least
20%
increase of the energy
efficiency [
14
]. Street lighting attributes nearly
19%
of the
worldwide use of electrical energy and entails 6% of global
emissions of greenhouse gases. A decrease of
40%
of energy
spent for lighting purposes is equivalent to eliminating a half
of the emissions from the production of electricity and heat
generation in the US [
15
]. In this context, public street lightning,
which is an essential community service, plays an important
role, as it impacts for around
40%
on the cities’ energy
budget. Consequently, in preparation of the EU commitments,
optimizing the lighting service is a primary objective for the
municipalities [16].
The street lighting solutions currently implemented in cities
are not energy efficient. Typically, every lamp operates at
full intensity
12
hours a day on average:
8
hours during
summer and
14
hours during winter period [
16
]. As a result,
the costs the municipalities sustain are high [
15
]. A number
of different types of lamps are applicable for public street
lighting, including High Pressure Sodium (HPS), Metal-halide
(MH) lamps, Compact Fluorescent lamps (CFL) and Light-
emitting diode (LED). The list does not comprise all possible
technologies. For example, the use of mercury-vapor lamps
for lighting purposes was banned in the EU in 2015 [
17
].
HPS is the most common technology currently implemented
in EU streets [
18
]. Nevertheless, in terms of average lifetime,
maintenance, electrical performances and energy savings, LED
technology appears to be the most convenient solution [19].
Table I compares different types of lamps. LEDs have
an average lifetime
4
times longer than HPS lamps and
10
times longer if compared to MH lamps. Installing LEDs is
effective to reduce hardware, installation and maintenance costs.
Low wattage provides significant energy savings and allows
increasing the lamp efficiency [
20
], [
21
]. LED lamps can dim
the light intensity by more than
50%
modifying therefore
the output level of light according to the circumstances. For
example, when traffic is low or in rarely visited areas of the
city, like the parks at night. The city of Brittany in France, dims
street lights by
60%
between
11
PM and
5
AM to decrease
waste energy [16].
III. SMA RT LIGHTING SOLUTIONS
This section presents three new heuristics proposed for smart
lighting and explains the employed underlying technologies and
control mechanisms in detail. In the context of energy-aware
lighting, a number of control mechanisms was proposed [
22
].
The most important strategy is occupancy, which makes lamps
to switch on/off or to dim the light intensity according to
the presence of users or vehicles. In this paper, we adopt the
occupancy control strategy suitable for pedestrian zones in
smart cities.
The control of street lighting can be performed in distributed
or centralized manner. With the latter method, a coordinator
unit is responsible to control a cluster of lampposts on the
basis of their feedback on users presence [
23
]. With the former
method, each lamppost operates independently. Distributed
control systems require significant changes in the existing
infrastructure, while centralized solutions can be deployed with
minor intervention. However, as the employed control policy
is occupancy-based, distributed systems have the potential to
achieve higher energy savings because they react to the change
of user presence faster. In this work, we adopt a completely
distributed system.
Table II briefly summarizes properties of the three different
smart lighting solutions proposed in comparison with current
adopted approach. For each method, its efficiency is denoted
as low (LO), medium (M E) and high (HI).
Current Implementation (CU R):
The most widely imple-
mented methodology for street lighting makes lampposts to
operate at full light intensity for a predefined period of time.
Typically lampposts operate continuously for an average of 10
or 12 hours a day [
16
]. This solution does not account at all
for the presence of users passing nearby the lampposts, and as
a result, it is expected to be the lowest in terms of efficiency
(see Table II).
Unlike CUR the heuristics we propose take into account the
presence of users nearby the lampposts to save energy, which
is achieved by installing a presence sensor like the SE-10 PIR
motion sensor [
24
]. With presence sensors, every lamppost
is able to recognize the presence of citizens within a certain
radius R, as illustrated in Fig. 1.
Table II
SMART LIGHTING SOLUTIONS
METHOD ACRO NYM DESCRIPTION EFFI CAC Y
Current CU R Lampposts remain continuously active emitting maximum light intensity. LO
Delay-based DE L
Lampposts are switched on when users pass nearby. If nobody is present within the coverage
radius R, the lampposts remain active for time window Wand then are switched off. HI
Encounter-based ENC
Lampposts are switched upon the first encounter with at least one user and remain active the
whole night. ME
Dimming DI M
Lampposts operate at 60% light intensity in absence of users within the coverage radius
R
.
Lampposts light up/dim the light intensity in proportion to the number of users passing nearby. HI
R
Figure 1. Coverage radius R
Delay-based (DEL):
The lampposts remain active and operate
at full intensity as long as the motion sensor detects presence
of users. The lamp is turned off if no users are sensed in the
vicinity and during a time window
W
nobody passes nearby.
Then, whenever a pedestrian approaches the lamppost closer
than the distance
R
, it triggers re-activation of the lamp. This
methodology can employ both LEDs and HPSs lamps. For
HPSs lamps, that are currently the most widely adopted in our
cities [
25
], it might take around 15 minutes to the lamp to
reach the maximum brightness after turning on, because the
sodium inside the bulb needs to be fully heated [
26
]. As a
result, DE L should be a preferred method for LED lamps.
Encounter-based (ENC):
This method is a modification of
DEL. The lampposts turn on after the first user passes nearby
and remain active until the end of the predefined activity period
in the morning. Although being simple, the method improves
CUR. Moreover, as lamps do not need to be switched on and
off frequently, HPS technology and not LEDs can be employed
lowering the capital expenditures significantly. For this reason,
Table II rates ENC to be medium efficient.
Dimming (DI M):
The last proposed method dims the light of
lampposts in proportion to the number of users in the vicinity.
Similarly to the solution adopted in Brittany, i.e., the minimum
light intensity level is
60
% if no users are within the coverage
radius
R
. Lampposts then light up or dim the intensity level
in proportion to the number of users passing in the vicinity.
In more details, if the number of users within
R
is increasing,
the light intensity increases up to
100
% or remains at that
level. On the contrary, if the number of users within
R
reduces,
so does the light intensity until it reaches the minimum level.
Having a minimum level of light intensity fixed at 60% ensures
sufficient luminosity to detect obstacles, animals passing by
Figure 2. Position of lampposts in Luxembourg city center
while providing at the same time considerable energy savings
in under utilized scenarios. The HPS lamps do not support
dimming [
19
] and only LEDs can be employed to perform
dimming properly. The use of LEDs is gradually gaining
popularity due to its photo metric characteristics, such as low
weighted energy consumption (kW/1000hrs), high luminous
efficacy (lm / W), high mechanical strength, long lifespan and
reduction of light pollution [
18
], [
19
]. This solution is expected
to be highly efficient (see Table II).
IV. COMPARISON METHODOLOGY
This section presents the methodology adopted to evaluate
and compare the performance of the proposed smart lighting
solutions. For the purpose, we have built a custom discrete-
event simulator, which follows the design criteria illustrated
in [
27
], [
28
] supports realistic urban environments for the
deployment of lampposts on the streets and pedestrian mobility.
The center of Luxembourg city was selected for simulations.
It covers an area of
1.11
km
2
and is the home of many
national and international institutional buildings. To obtain
information about the streets of the city, the simulator exploits
a crowdsourced application which provides free access to
street-level maps
2
. The information is given in the form of
coordinates
C
that contain
<
latitude, longitude, altitude
>
. The
set of
537
lampposts has been deployed according to their
physical location in the streets and squares (see Fig. 2).
The users move along the streets of the city, with their
original locations randomly assigned from the set of coordinates
C
. The number of users is set to
5 000
, which corresponds to
nearly one twentieth of the population of Luxembourg (
110 499
inhabitants as of the end of 2015). Each user walks for a period
2DigiPoint: http://www.zonums.com/gmaps/digipoint.php
PM AM
9-10 10-11 11-12 12-1 1-2 2-3 3-4 4-5 5-6 6-7
0
0.1
0.2
0.3
Time Interval (hour)
PDF
Figure 3. PDF of user mobility during the evaluation period
10 30 50
0
200
400
600
800
1 000
Radius (m)
Energy consumption (kW/day)
CUR EN C DEL DIM
Figure 4. Comparison of smart lighting solutions
of time uniformly distributed between
[10,20]
minutes with
an average speed uniformly distributed between
[1,1.5]
m/s.
The users begin walking according to a specific arrival pattern.
During the evaluation period, set between 9 PM and 7 AM,
each user has a probability to start traveling that is defined by
the probability density function (PDF) defined in Fig. 3. To
illustrate with an example, during 9 PM and 10 PM nearly one
third of the total number of users starts walking and at 7 AM
all 5 000 users end traveling.
V. PERFORMANCE EVALUATIO N
For performance evaluation, the experiments are carried on
varying the coverage radius
R
and the time window
W
. In
more details,
R
assumes values equal to
{10,30,50}
m, while
W
assumes values equal to
{2,5,10,20}
minutes only for the
DIM method.
Fig. 4 compares the smart lighting solutions proposed
in terms of energy consumed per 10 hours activity, which
corresponds to a day. As expected, the current implemented
methodology (CU R) is the least efficient if compared to the
proposed heuristics. The ENC method improves CU R by nearly
7
%. As the lampposts turn on only upon detection of users
nearby, some of the lampposts remains initially idle while in
CUR all the lampposts are active starting from 9 PM. The
most energy efficient techniques are DEL and DIM. For the
DIM method, Fig. 4 shows the results for a time window
W= 5
minutes. Both methodologies are most effective for
short values of
R
because the probability of having users
nearby the lamppost is lower. However, different values of
R
impact differently on the performance of DE L and DI M. For
the former method, the energy consumption augments of nearly
2 5 10 20
0
10
20
30
40
Time window W(Minutes)
Energy Consumption (KWh)
R= 10 R= 30 R= 50
Figure 5. Impact of Won performance of DI M for different values of R
38
% while for the latter the increase is only
17
%. Two are
the main reasons: i) the LEDs used in DI M are more efficient
than HPS lamps used for DEL, ii) the DI M checks presence
of nearby users every more often than DEL.
Having determined the energy costs per kWh in different
countries according to [
29
], [
30
], [
31
], Table III compares the
daily cost to operate for a
10
hours long period the proposed
smart lighting solutions. The values of energy consumption of
each method are determined as the average over
100
simulation
runs of the energy consumption of all the
537
lampposts.
Table III compares capital and operational expenditures (CA PE X
and OP EX respectively). The OPEX costs are determined for
537
lampposts for a time period of
1
year without considering
salary costs of workman for installation and maintenance.
Consequently, the analysis focus completely on energy costs
that directly originate by operating the platform. CAPE X costs
are determined considering the additional sensor components
necessary to make operational the methods. In CU R and in
ENC methods, every lamppost is equipped with an HPS-97241
lamp [
21
]. In order to implement ENC, it is essential to add
a micro-controller (model PIC12F635 [
32
]) and a presence
sensor (model SE-10 [
33
]) per lamppost. For DE L and DI M
in addition to the previous components, the lamp is not an
HPS, but a LED lamp (model GRN100 [
20
]) described in
Table I. Interestingly, with a focus on Luxembourg, Table IV
shows that DI M would be beneficial in providing an economical
return already in his first year of implementation. The EN C
method does not bring considerable advantages over CUR .
However, it is worth mentioning that the simple operation
of not turning on all the lampposts simultaneously saves
operational expenditures for
6.7
%. Implementing ENC is nearly
8×
cheaper than implementing DEL and DIM, but the latter
methods significantly lower the yearly OP EX costs.
Fig. 5 analyzes the impact of the time window
W
used to
check the presence of users nearby on the energy performance
the DI M solution provides. The analysis is carried on with
different values of the coverage radius
R
. As expected, the
energy consumption increases with the increase of
W
and
R
. Interestingly, the contribution given by
R
in the increase
of energy consumption is higher for high values of time
window
W
. For
W= 2
and
W= 20
, the increase of energy
consumption from values of
R= 10
to
R= 50
is respectively
10% and 24%.
Table III
COST COMPARISON OF SMART LIGHTING SOLUTIONS FOR DIFFERENT COUNTRIES
COU NTRY ENERGY CO ST ( C / KWH) M ET HOD ( C )
CUR EN C DEL DIM
(927.4kWh per day) (865.1kWh per day) (384.0kWh per day) (298.5kWh per day)
Luxembourg 0.18 166.9 155.7 69.1 53.7
Italy 0.24 222.6 207.6 92.1 71.6
Germany 0.29 268.9 250.9 111.3 86.6
France 0.17 157.6 147.1 65.3 50.7
China 0.07 64.9 60.5 26.9 20.9
USA 0.10 92.7 86.5 38.4 29.8
0 0.5 1 1.522.533.544.5 5 5.5 6 6.577.588.5 9 9.510
0
50
100
150
200
250
300
Hours of Activity
Active Lampposts
R= 10 R= 30 R= 50
(a) DE L method
0 0.5 1 1.522.533.544.5 5 5.5 6 6.577.588.5 9 9.510
0
50
100
150
200
250
300
Hours of Activity
Active Lampposts
R= 10 R= 30 R= 50
(b) EN C method
Figure 6. Distribution of lamppost activity
Table IV
COS T ANA LYSIS I N LUXEMBOURG
MET HOD CO ST EXPENDITURE ( C)
CAP EX OPE X TOTAL
CUR −60 930 60 930
ENC 4 779 56 835 61614
DEL 36 999 25 227 62226
DIM 36 999 19 611 56610
Having compared the performance of the proposed smart
lighting solutions in terms of energy consumption, we now
investigate the distribution of hours of activity for the DE L
and EN C. The results are displayed with the granularity of
30
minutes. Unlike the other smart lighting solutions, these
methodologies turn off the lampposts if nobody is passing
nearby. The DEL method, being more energy friendly than ENC,
reduces the hours of activity of the lampposts in proportion
to the coverage radius
R
. Fig. 6(a) clearly highlights that the
distribution of the number active lampposts follows a normal
distribution whose center changes for different values of
R
.
The higher the values
R
assumes, the higher is the average
number of hours the lampposts remains active. The lower the
values
R
assumes, the more energy efficient the DEL policy
becomes. The number of lampposts that remain switched off
is higher and on average, the lampposts are active for shorter
time periods. Fig. 6(b) shows the distribution of the hours of
activity for the EN C method. As the lampposts remain active
until the end of the period once turned on, the distribution
is significantly different than the one obtained for the DEL
method and the impact of
R
is almost negligible. Having fixed
R= 10
, Fig. 7 compares with a heatmap the hours of activity
of the lampposts for both DEL and EN C methods. The results
are obtained with Google Heatmap tool3.
VI. CONCLUSION
Smart lighting solutions can significantly decrease energy
costs of street lightning municipalities. In this paper, we propose
three new heuristics for smart lighting based on the peculiar
characteristics of the employed technology. LEDs allows to dim
the light intensity while HPS lamps are turned off if nobody is
passing nearby the lampposts. We developed a custom simulator
to evaluate the proposed smart lighting solutions in a real
city environment.
4
The results highlight that LED technology
combined with dimming of light intensity provides higher
energy savings than other evaluated solutions. In Luxembourg
city center, replacing all existing lamps with LEDs is beneficial
financially already after the first year of deployment, while
in other countries like China and USA economic returns will
come after the second year of installation.
For future work, we plan to investigate more advanced
solutions, such as how to coordinate light dimming of lampposts
of each street according to the prediction of user mobility
patters. ACK NOW LE DG ME NT
The authors would like to acknowledge the funding from
National Research Fund, Luxembourg in the framework of
ECO-CLOUD and iShOP projects.
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