A Map of the Research About Lighting Systems in the 1995–2024 Time Frame
Abstract
1. Introduction
- It provides a map of the state of the art of research in the domain of LSs, within a 1995–2024 time frame. The reported bibliometric research is somewhat urgent because, as will be proven subsequently, the extant literature about LSs is becoming huge; nevertheless, as far as we know, our research is the first study in such a category. In recent years, a recurrent refrain has been that the Internet of Things (IoT) and Artificial Intelligence (AI) are the technologies to rely on, so that traditional LSs can make a qualitative leap by becoming smart and at the same time sustainable. This study highlights that the state of the art of LSs confirms this widespread thinking, but at the same time it tells us that the relevance of AI is still marginal. Such a conclusion confirms previous reports from both academia and industry.
- It meets the rigor requirement for this type of literature review. This objective was pursued by adopting a well-known protocol, whose implementation is reported in full detail. Thus, the investigation is transparent and replicable.
- It delves into the impact that the use/non-use of a thesaurus of authors’ keywords had on the number and composition of the thematic clusters returned by VOSviewer and, ultimately, on how this choice affected the correctness of the interpretation of the clusters. Published articles adopting keyword co-occurrence analysis can be split in two groups: the smaller group mentions the usage of a thesaurus of authors’ keywords [9,11,17,18,21,22,23], while the larger group does not. References [7,12,15,16,20,24,25,26,27,28,29]. Among the seven works belonging to the first group, references [11,17,23] just mentioned that a thesaurus was used, reference [22] focused on the relevance of adopting a thesaurus for data clean-up, finally, references [9,18,21] detailed the steps needed to build a thesaurus of authors’ keywords that complies the VOSviewer’s format. Ref. [18] listed, in addition, the 34 terms which comprised the thesaurus.
- The choice of the number of clusters of authors’ keywords was obtained by balancing the numerical value of four independent methods belonging to the family of so-called “clustering validity approaches” [30,31,32,33,34] with what the experts (the authors on this occasion) judged to be a meaningful partition of the input dataset. Such an approach was recommended in [35,36] for overcoming the issue pointed out in ref. [37]. In the latter work, the authors observed the absence of a reason behind their choice, common to most published articles talking about bibliometric analyses, of the value of the minimum number of occurrences for an authors’ keyword to be included in the BA.
- Regarding the latter two points, it follows that the present study also gives two recommendations from a methodological perspective. First, it points out that keyword co-occurrence analyses must be driven by a robust thesaurus of keywords. Second, the VOSviewer parameters must be optimized.
2. Background
2.1. VOSviewer Terminology [38]
2.2. On the Choice of the Number of Clusters
- Elbow method [41]This method first measures the Within-Cluster Sum of Squares (WCSS) for a varying cluster number, then it selects the cluster for which the change in WCSS starts to decrease. The mathematical definition of WCSS may be found in [33]. In simple words, we can say that WCSS measures how well the data points are clustered around their respective centroids. With respect to leveraging the Elbow method, it is necessary to note that it provides a starting point, but since, in some cases, the Elbow point may not be distinctly visible, a subjective interpretation is then required. In addition, it is useful to remember that such a method is specifically tailored for centroid-based clustering algorithms like KMeans, so it does not necessarily work well for other methods [36].To reinforce the clustering decision, the Davies–Bouldin index, the Silhouette score, and the Calinski–Harabasz index [33] are frequently adopted, either individually or together.
- Davies–Bouldin Index [42]The Davies–Bouldin (DB) index is defined as the ratio between the intra-cluster distance and the inter-cluster distance. The former distance is calculated as the mean distance between each element in a cluster to the centroid of that cluster. (It offers insights into how closely grouped the elements within a single cluster are); while the latter distance measures how far apart each cluster’s centroid is from the other clusters. (The larger this distance, the more separated the clusters).A lower score of DB index signifies a better cluster formation, with zero being the absolute ideal score. It has been remarked [43] that relying solely on the DB index for cluster analysis would be imprudent, because such an index is only effective with clusters of convex shape. The DB index should be used in conjunction with other metrics for a more holistic evaluation.
- Silhouette score [44]The Silhouette score (Sscore) of an input dataset measures how dense and well separated the clusters are. The mathematical definition of this metric may be found in [35]. For the purposes of the present study, it is sufficient to recall the following: Sscore is defined as [(b − a)/max(a,b)], where “a” and “b” denote, respectively, the mean intra-cluster distance and the mean nearest-cluster distance for each sample in the input dataset. To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of. Sscore takes values in the range [−1, 1]. A higher score indicates better clustering. Values near 0 indicate overlapping clusters, while negative values generally indicate that a sample has been assigned to the wrong cluster.
- Calinski Harabasz Index [45]The Calinski–Harabasz (CH) index is calculated as the ratio of the sum of inter-cluster dispersion and the sum of intra-cluster dispersion for all clusters (where the dispersion is the sum of squared Euclidean distances). In simple words, the CH index measures how dense and well separated the clusters are. The mathematical definition of this metric may be found in [35]. A higher CH score means better clustering.
3. Research Method
- Definition of the research objectives.The present work is the outcome of a cooperation between the Department of Industrial & Information Engineering & Economics of University of L’Aquila (Italy) and an Italian SME (B2B S.r.l). Recently, B2B S.r.l stated a desire to obtain an unbiased map about the state of the art of LSs. Their interest originated within national IT projects that B2B S.r.l. is involved in. The aim of the research is represented by the two Research Questions (RQs) this study aimed to answer:RQ1. What are the major topics explored by scholars in connection with LSs in the 1995–2024 time frame?RQ2. How do they group together?
- Literature search and data collection.In ref. [4], it was clarified that it is not necessarily convenient to use more than one scientific database in bibliometric research. A more serious issue comes from the inevitable duplication of publications, which makes the findings of the analysis debatable. In the case of Scopus and Web of Science, for example, the percentage of duplicates tends to be large, because most of journals are scanned in both databases. A previous research work by Singh et al. [46] showed that about 99% of the journals indexed in Web of Science are also indexed in Scopus. Moreover, the integration of items belonging to files obtained by querying distinct databases is time consuming, since they provide article information in a different format. In the present study, we queried Scopus rather than Web of Science due to its significantly greater coverage of published literature in the field of LSs, as will be proven subsequently.In ref. [47], it was remarked that a search string can be either generic or specific. We opted for the first option, since generic search strings maximize the “recall” value (i.e., the fraction of the documents that are relevant to the query that are successfully retrieved). The search string was the following: (“Lighting system” OR “Light control”). These terms are directly related to the study RQs. Previous research has stressed that to extract the literature relevant to the review aim, the terms in the search string are crucial [4]. The above search string was restricted by adding filters for publication type (articles, conference papers, review, and book chapters) and language. These filters represent what in ref. [4] the authors called the inclusion/exclusion criteria to be applied in bibliometric research. The final search string, plus the filters as written by Scopus, were as follows:TITLE-ABS-KEY ((“Lighting system” OR “Light control”)) ANDPUBYEAR > 1994 AND PUBYEAR < 2024 AND (LIMIT-TO (DOCTYPE, “ar”) ORLIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) ORLIMIT-TO (DOCTYPE, “ch”)) AND (LIMIT-TO (LANGUAGE, “English”))Scopus returned 12,148 documents, which were saved to the following file:Scopus12148.csv.
- Data screening and PreprocessingWhen multiple databases are queried, cleaning the output dataset is mandatory to ensure accuracy. Basically, it is necessary to remove duplicates and correct authors’ names. This effort was not required in our case, since we only queried Scopus. About the preprocessing, it first included the semi-automatic construction of a thesaurus of authors’ keywords, then the investigation of the best value. Section 4 discusses both these arguments in connection with the clustering effectiveness. In practical terms, this review answers a further relevant research question:RQ3. What is the impact of the thesaurus of authors’ keywords and the value on the clustering effectiveness?
- Selection of Bibliometric techniquesThe Scopus engine computes statistics for the “publication” and “citation” performance of authors, journals, institutions, and countries. These bibliometric indicators were developed by running a Performance analysis on the dataset output of the search [4]. To match the aim of the present review and, hence, to be able to answer the RQs, the Science Mapping analysis procedure was selected [4,5]. Specifically, keyword analysis was chosen, since this bibliometric method allows mapping relevant publications on a given topic ([3,25]) (in our case the LSs domain), because the authors’ keywords represented the major themes of the underlying publication (before delving into the investigation of its content through a systematic literature review). Moreover, such an analysis allows tracking the evolution of the reference research field over time, and hence recognizing emerging trends.
- Data analysis, Visualization, and ReportingData analysis and visualization were carried out leveraging VOSviewer. Preliminary, the authors entered the Scopus12148.csv file (returned by Scopus) into VOSviewer. The following options were selected in sequence: (a) create a map based on bibliographic data; (b) read data from the bibliographic database file. As the counting method, full counting was selected. The interpretation of the results is the subject of Section 4, where the three RQs are also answered.
4. Results
4.1. Data Collection
4.2. About Data Preprocessing
4.2.1. The Thesaurus
4.2.2. Computation of the Number of Clusters
4.2.3. Analysis of the Network of Keyword Co-Occurrences Without a Thesaurus
Synonyms of … | Keyword | Synonyms of … | Keyword |
---|---|---|---|
adaptive traffic light control | Traffic light control | congestion | Traffic control |
intelligent traffic light | intelligent traffic | ||
intelligent traffic light control | pedestrian detection | ||
intelligent traffic system | road traffic congestion | ||
smart traffic light | simulation of urban mobility (SUMO) | ||
smart traffic lights | smart traffic | ||
traffic light | SUMO | ||
traffic light control | traffic congestion | ||
traffic light control (tlc) | traffic control | ||
traffic light control system | traffic control systems | ||
traffic light control systems | traffic flow | ||
traffic lights | traffic management | ||
traffic lights control | traffic monitoring | ||
traffic signal control | traffic network | ||
traffic optimization | |||
traffic simulation | |||
urban traffic | |||
urban traffic control |
4.2.4. Analysis of the Network of the Keyword Co-Occurrences in Presence of the Thesaurus
- Node analysis
- Link analysis
- Temporal evolution of the research topics
4.2.5. Comparison of the Two Paths
5. Limitations
6. Conclusions
- (Answer to RQ1) The trade-off between the numerical value of four independent methods belonging to the family of clustering validity approaches and the authors’ evaluation of a meaningful partition of the input dataset resulted in three independent clusters, collecting 25 authors’ keywords. The most relevant topics in Cluster 1 were “led lighting system” and “energy management”; while the most relevant topics in Cluster 2 were “IoT”, “smart building”, “street light control system”, “WSN”, “Arduino”, and “MCU”. Finally, the most relevant topics in Cluster 3 were “smart city, “computer vision”, “image processing”, “ML”, “DL”, and “RL”.
- (Answer to RQ3) It was proven that, in absence of a thesaurus of authors’ keywords, the interpretation of the thematic clusters returned by VOSviewer became difficult due to the simultaneous presence of synonyms, out of scope keywords, and too-generic keywords. The only way to prevent this situation consisted in making use of the thesaurus. Regarding the choice of the value, this work implemented recommendations from previous research in the field of clustering validity approaches.
- Regarding the construction of the thesaurus, it is worth noting that this is an iterative process. The factors to be taken into account in the workflow are the following: It is fundamental to keep the value low, in order to include a large number of documents. In the present review, the authors started from 12,148 documents retrieved from Scopus. Here, the issue that must be addressed is making the realization of a thesaurus manageable, since the task is not fully automatic. In the present study, was set to 5. By setting = 3, the number of keywords to be considered in the construction of the thesaurus rose from 1096 to 4693, which made the manual work much more tedious and error-prone.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ANN(s) | Artificial Neural Network(s) |
CNN | Convolution NN |
CV | Computer Vision |
DNN | Deep Neural Network |
DL | Deep Learning |
DRL | Deep Reinforcement Learning |
FL | Federated Learning |
IoT | Internet of Things |
LDR | Light Dependent Resistor |
LED | Light Emitting Diode |
LS(s) | Lighting System(s) |
MCU | Microcontroller Unit |
ML | Machine Learning |
MQTT | Message Queuing Telemetry Transport |
NN(s) | Neural Network(s) |
PIR | Passive Infrared |
PSO | Particle swarm optimization |
RF | Random Forest |
RL | Reinforcement Learning |
RNN(s) | Recurrent Neural Network(s) |
SME | Small and Medium-sized Enterprise |
SML | Supervised ML |
WSN | Wireless Sensor Network |
Appendix A. The Thesaurus
label | replace by |
2D code | |
2D code | |
2D materials | |
3D display | |
3D printing | |
3D reconstruction | |
6lowpan | |
absorption | |
accident prevention | |
active clamp circuit | |
adaptive | |
adaptive control | |
adaptive driving beam | |
adaptive front-lighting system | |
adaptive lighting | |
adaptive lighting system | |
adaptive optics | |
adaptive systems | |
adaptive traffic light control | |
adaptive traffic signal control | |
additive manufacturing | |
afs | |
agriculture | |
ahp | |
ai | |
aircraft | |
airfield lighting | |
airports | |
algae | |
algorithm | |
algorithms | |
ambient intelligence | |
android | |
android application | android application |
ansys | |
anthocyanin | |
antioxidant | |
antioxidant capacity | |
application | |
applications | |
approach lighting system | |
arabidopsis | |
arabidopsis thaliana | |
architectural lighting | |
architecture | |
arduino | arduino |
arduino microcontroller | arduino |
arduino nano | arduino |
arduino uno | arduino |
arm | |
artificial intelligence | artificial intelligence |
artificial light | artificial lighting |
artificial light at night | artificial lighting |
artificial lighting | artificial lighting |
artificial lighting system | |
artificial neural network | ANN |
artificial neural networks | ANN |
artificial neural networks (anns) | ANN |
augmented reality | |
automatic control | |
automation | |
automotive | |
automotive lighting | |
automotive lighting system | |
autonomous distributed control | |
autonomous vehicles | |
availability | |
azobenzene | |
ballast | |
battery | |
beam shaping | |
behavior | |
behaviour | |
bidirectional converters | |
big data | |
bi-level optimization | |
biodynamic lighting | |
biomass | |
blue light | |
bluetooth | bluetooth |
boost converter | |
bright light | |
brightness | |
broiler | |
buck converter | |
buck-boost converter | |
building | |
building automation | Smart building |
building energy | building energy management |
building energy consumption | building energy management |
building energy efficiency | building energy management |
building energy management | building energy management |
building energy management system | building energy management |
building energy saving | building energy management |
building envelope | |
building information modeling | |
building performance simulation | |
building simulation | |
buildings | |
calibration | |
camera | |
can bus | |
carbon emission reduction | |
carbon emissions | |
carbon footprint | |
carotenoids | |
case studies | |
case study | |
cc2530 | |
cct | |
cellular automata | |
cfd | |
cfl | |
charge controller | |
children | |
chlorophyll | |
chlorophyll fluorescence | |
chromaticity coordinates | |
circadian | |
circadian clock | |
circadian light | |
circadian lighting | |
circadian rhythm | |
circadian rhythms | |
circadian stimulus | |
classification | |
climate change | |
cloud | cloud computing |
cloud computing | cloud computing |
clustering | |
cmos | |
co2 emission | |
co2 emissions | |
color | |
color control | lighting control system |
color mixing | lighting control system |
color rendering | lighting control system |
color rendering index | lighting control system |
color temperature | lighting control system |
colorimetry | lighting control system |
colour | |
colour temperature | lighting control system |
comfort | User comfort |
commercial buildings | |
communication | |
compact fluorescent lamp | |
compact fluorescent lamp (cfl) | |
component | |
computational fluid dynamics | |
computational fluid dynamics (cfd) | |
computer simulation | |
computer vision | computer vision |
conflict resolution | |
congestion | |
connected lighting | |
connected lighting systems | |
conservation | |
construction | |
consumption | |
context-aware | |
control | |
control algorithm | |
control circuit | |
control network | |
control strategies | |
control strategy | |
control system | |
control systems | |
controlled environment | |
controlled environment agriculture | |
controller | |
controlling | |
controls | |
convolutional neural network | CNN |
convolutional neural networks | CNN |
cooling | |
cop1 | |
correlated color temperature | lighting control system |
correlated color temperature (cct) | lighting control system |
correlated colour temperature | lighting control system |
cost | |
cost analysis | |
cost benefit analysis | |
COVID-19 | |
cri | |
cryptochrome | |
cultivation | |
cultural heritage | |
cyanobacteria | |
cyber-physical systems | |
daily light integral | daylight |
dali | |
data fusion | |
data-driven | |
daylight | daylight |
daylight factor | Daylight |
daylight harvesting | Daylight |
daylight simulation | Daylight |
daylighting | Daylight |
dc lighting | |
dc/dc converters | |
dc-dc converter | |
dc-dc converters | |
dc-dc power conversion | |
dc-dc power converters | |
decision making | |
deep learning | DL |
deep neural networks | DL |
deep q-learning | deep q-learning |
deep q-network | deep q-learning |
deep reinforcement learning | DRL |
deep reinforcement learning (drl) | DRL |
defect detection | |
degradation | |
delirium | |
demand response | |
demand side management | |
dementia | |
design | |
development | |
dialux | |
dialux evo | |
dialux evo software | |
diffraction | |
diffuser | |
diffusers | |
digital control | |
digital twin | Digital twin |
dimmer | |
dimming | dimming control |
dimming control | dimming control |
dimming level | dimming control |
direct current | |
discomfort glare | |
discontinuous conduction mode (dcm) | |
displays | |
distributed control | |
distributed generation | |
distributed systems | |
dna | |
domotics | |
driver | |
driving simulator | |
dynamic control | |
dynamic light | lighting control system |
dynamic light control | lighting control system |
dynamic lighting | lighting control system |
dynamic traffic light control | |
economic analysis | |
economic evaluation | |
edge computing | edge computing |
edge detection | |
eeg | |
efficacy | |
efficiency | |
efficiency evaluation | |
efficient lighting | energy management |
electric lighting | |
electrical energy | |
electrical lighting | |
electricity | |
electricity consumption | energy management |
electrochromism | |
electroluminescence | |
electrolytic capacitor | |
electromagnetic interference | |
electromagnetic interference (emi) | |
electronic ballast | |
electronic ballasts | |
embedded system | embedded system |
embedded systems | embedded system |
emergency lighting | emergency lighting |
emergency vehicle | |
emergency vehicles | |
emission | |
emission reduction | |
emissions | |
energy | |
energy audit | |
energy certification | |
energy conservation | |
energy consumption | |
energy conversion | |
energy demand | |
energy efficiency | |
energy efficiency in buildings | |
energy efficient | |
energy efficient lighting | energy management |
energy harvesting | energy management |
energy management | energy management |
energy management system | energy management |
energy modeling | |
energy optimization | energy management |
energy performance | energy management |
energy policy | |
energy retrofit | |
energy saving | energy management |
energy savings | energy management |
energy simulation | |
energy storage | |
energy use | energy management |
energy use efficiency | energy management |
energy-efficiency | |
energyplus | |
energy-saving | energy management |
environment | |
environmental monitoring | environmental sustainability |
environmental protection | environmental sustainability |
environmental sustainability | environmental sustainability |
esp8266 | |
ethernet | |
evaluation | |
experimental measurements | |
experimental study | |
face recognition | face recognition |
fatigue | |
fault detection | |
fault tolerance | |
feedback | |
feedback control | |
fiber optic | |
fiber optic lighting | |
fiber optics | |
field study | |
flavonoids | |
flicker | |
flowering | |
fluorescence | fluorescent lamp |
fluorescent | fluorescent lamp |
fluorescent lamp | fluorescent lamp |
fluorescent lamps | fluorescent lamp |
flyback | |
flyback converter | |
fog computing | fog computing |
formal methods | |
formal verification | |
fpga | FPGA |
freeform optics | |
fresnel lens | |
fuzzy control | fuzzy control |
fuzzy controller | fuzzy control |
fuzzy logic | fuzzy control |
fuzzy logic controller | fuzzy control |
gallium nitride | |
game theory | |
gas exchange | |
gene expression | |
genetic algorithm | genetic algorithm |
genetic algorithms | genetic algorithm |
genetic code expansion | genetic algorithm |
germination | |
gis | |
glare | |
glass | |
gold nanoparticles | |
gprs | |
gps | |
graphene | |
graphical user interface | |
green building | green building |
green buildings | green building |
green energy | |
green light | Daylight |
green lighting | Daylight |
green technology | |
green wave | |
greenhouse | green building |
greenhouse gases | |
greenhouses | green building |
growth | |
growth chamber | |
gsm | |
gsm module | |
harmonic | |
harmonics | |
haul road | |
headlamp | |
headlamps | |
health | |
heat sink | |
heat transfer | |
heating | |
hid | |
hid lamps | |
high power led | LED |
high pressure sodium | high pressure sodium lamp |
high pressure sodium lamp | high pressure sodium lamp |
high-pressure sodium lamp | high pressure sodium lamp |
highway | |
historical building | |
historical buildings | |
holography | |
home | |
home automation | home automation |
home energy management system | Smart building |
horticulture | |
hospitals | |
hps | |
hps lamp | |
human centric lighting | human centric lighting |
human computer interaction | |
human detection | motion detection |
human-centric lighting | human centric lighting |
hvac | |
hy5 | |
hybrid lighting | hybrid lighting system |
hybrid lighting system | hybrid lighting system |
hybrid system | |
hydroponics | |
hyperspectral imaging | |
ieee 802.15.4 | |
illuminance | illumination control |
illuminance distribution | illumination control |
illuminance level | illumination control |
illuminance sensor | illumination sensor |
illuminance uniformity | illumination control |
illumination | illumination control |
illumination control | illumination control |
illumination design | illumination control |
illumination sensing | illumination sensor |
illumination system | illumination control |
image acquisition | image processing |
image analysis | image processing |
image processing | image processing |
image segmentation | image processing |
incandescent | |
india | |
indoor air quality | |
indoor environment | |
indoor environmental quality | |
indoor farming | |
indoor lighting | indoor lighting system |
indoor lighting system | indoor lighting system |
indoor localization | |
industrial lighting | industrial lighting |
industry | |
infrared | |
infrared sensor | infrared sensor |
infrared sensors | infrared sensor |
integrative lighting | |
intelligent | |
intelligent building | |
intelligent buildings | |
intelligent control | |
intelligent light | Smart lighting control system |
intelligent lighting | Smart lighting control system |
intelligent lighting control | Smart lighting control system |
intelligent lighting system | Smart lighting control system |
intelligent lighting systems | Smart lighting control system |
intelligent street lighting | Smart lighting control system |
intelligent system | |
intelligent systems | |
intelligent traffic | |
intelligent traffic control | |
intelligent traffic light | |
intelligent traffic light control | |
intelligent traffic system | |
intelligent transport system | |
intelligent transport systems | |
intelligent transportation | |
intelligent transportation system | |
intelligent transportation system (its) | |
intelligent transportation systems | |
intensity | |
interaction design | |
interactive lighting | |
interface | |
interior lighting | Indoor lighting system |
interior lighting system | Indoor lighting system |
internet of things | IoT |
internet of things (iot) | IoT |
internet of vehicles | |
internet-of-things | IoT |
intersection | |
intersections | |
inverter | |
iot | IoT |
ir sensor | PIR sensor |
irradiance | |
its | |
junction temperature | |
knx | |
label | |
labview | |
lamp | |
lamps | |
laser | |
laser diode | |
lca | |
lcd | |
ldr | LDR sensor |
ldr sensor | LDR sensor |
led | LED lighting system |
led (light emitting diode) | LED lighting system |
led array | LED lighting system |
led dimming | LED lighting system |
led driver | LED lighting system |
led drivers | LED lighting system |
led illumination | LED lighting system |
led lamp | LED lighting system |
led lamps | LED lighting system |
led light | LED lighting system |
led light source | LED lighting system |
led lighting | LED lighting system |
led lighting control | LED lighting system |
led lighting system | LED lighting system |
led lighting systems | LED lighting system |
led lights | LED lighting system |
led luminaire | LED lighting system |
led luminaires | LED lighting system |
led matrix | LED lighting system |
led street lighting | LED lighting system |
led system | LED lighting system |
led systems | LED lighting system |
led technology | LED lighting system |
leds | LED lighting system |
leni | |
lenses | |
lettuce | |
library | |
life | |
life cycle assessment | |
life cycle cost | |
life cycle cost analysis | |
life-cycle assessment | |
lifetime | |
li-fi | |
light | |
light control | lighting control system |
light control film | |
light control system | lighting control system |
light controller | |
light dependent resistor | LDR sensor |
light dependent resistor (ldr) | LDR sensor |
light dimming | Dimming control |
light distribution | |
light emitting diode | LED lighting system |
light emitting diode (led) | LED lighting system |
light emitting diodes | LED lighting system |
light emitting diodes (leds) | LED lighting system |
light environment | |
light guide | |
light intensity | |
light pipe | |
light pollution | |
light quality | |
light regulation | lighting control system |
light scattering | |
light sensor | light sensor |
light sensors | light sensor |
light shelf | |
light signaling | |
light source | |
light sources | |
light spectrum | |
light therapy | |
light-control | |
light-emitting diode | LED lighting system |
light-emitting diode (led) | LED lighting system |
light-emitting diodes | LED lighting system |
light-emitting diodes (led) | LED lighting system |
light-emitting diodes (leds) | LED lighting system |
lighting | |
lighting comfort | User comfort |
lighting conditions | lighting control system |
lighting control | lighting control system |
lighting control system | lighting control system |
lighting control systems | lighting control system |
lighting controls | lighting control system |
lighting design | |
lighting efficiency | energy management |
lighting energy | energy management |
lighting quality | lighting control system |
lighting retrofit | |
lighting simulation | |
lighting simulations | |
lighting system | lighting control system |
lighting system design | |
lighting systems | lighting control system |
lighting technology | |
lights | |
linear programming | |
liquid crystal | |
liquid crystal display | |
liquid crystals | |
localization | |
long lifetime | |
lonworks | |
lora | LoRa |
lorawan | |
low power consumption | energy management |
lumen maintenance | |
luminaire | |
luminaires | |
luminance | |
luminescence | |
luminous efficacy | User comfort |
luminous environment | |
luminous flux | |
luminous intensity | |
lux | |
m2m | |
machine learning | ML |
machine vision | Computer vision |
maintenance | maintenance |
management | |
management system | |
markov decision process | |
matlab | |
maximum power point tracking | |
mcu | MCU |
measurement | |
melanopsin | |
melatonin | |
mesopic vision | |
metamaterials | |
metasurface | |
metasurfaces | |
microalgae | |
microcontroller | MCU |
microcontrollers | MCU |
microgrid | |
micropropagation | |
mobile application | mobile application |
mobility | |
model | |
model checking | |
model predictive control | |
modeling | |
modelling | |
monitoring | |
monitoring system | |
motion detection | motion detection |
motion sensor | |
mppt | MPPT |
mqtt | MQTT |
multi agent system | multi agent system |
multi-agent | multi agent system |
multi-agent reinforcement learning | multi agent system |
multi-agent system | multi agent system |
multi-agent systems | multi agent system |
multi-objective optimization | |
multiple intersections | |
museum | |
museum lighting | museum lighting system |
myopia | |
nanocrystals | |
nanomaterials | |
nanoparticles | |
nanophotonics | |
natural convection | |
natural light | Daylight |
natural lighting | Daylight |
natural ventilation | |
nb-iot | NB-IoT |
negotiation | |
network | |
neural network | NN |
neural networks | NN |
nitric oxide | |
nodemcu | |
node-red | |
nonimaging optics | |
non-imaging optics | |
nonlinear optics | |
non-visual effects of light | |
numerical simulation | |
object detection | computer vision |
occupancy | occupant behavior |
occupancy and daylight adaptation | occupant behavior |
occupancy detection | motion detection |
occupancy sensing | occupant behavior |
occupancy sensors | occupant behavior |
occupant behavior | occupant behavior |
occupant behaviour | occupant behavior |
off-grid | |
office | |
office building | |
office buildings | |
office environment | |
office lighting | office lighting |
offices | |
oled | |
oleds | |
open source | |
opencv | |
optical | |
optical communication | |
optical communications | |
optical design | |
optical fiber | |
optical fibers | |
optical properties | |
optical system | |
optics | |
optimal control | |
optimisation | |
optimization | |
optochemical biology | |
optoelectronics | |
optogenetics | |
organic light emitting diodes | |
outdoor lighting | outdoor lighting system |
packaging | |
par | |
particle swarm optimization | PSO |
pattern recognition | |
payback period | |
pedestrian | |
pedestrian crossing | |
pedestrian detection | motion detection |
pedestrian safety | |
perception | |
performance | |
performance evaluation | |
pervasive computing | ubiquitous computing |
petri net | |
petri nets | |
phenolic compounds | |
phosphor | |
phosphors | |
photobiology | |
photobioreactor | |
photocatalysis | |
photochemistry | |
photodynamic therapy | |
photoluminescence | |
photometry | |
photomorphogenesis | |
photonic crystal | |
photonics | |
photoperiod | |
photopharmacology | |
photoreceptor | |
photoreceptors | |
photosensors | |
photosynthesis | |
photosynthetic photon flux density | |
photosynthetically active radiation (par) | |
phototaxis | |
photovoltaic | |
photovoltaic (pv) | |
photovoltaic cell | |
photovoltaic cells | |
photovoltaic system | |
photovoltaic systems | |
photovoltaics | |
phytochrome | |
pic microcontroller | PIC MCU |
pir | PIR sensor |
pir sensor | PIR sensor |
plant factory | |
plant growth | |
plant lighting | plant lighting |
plants | |
plasmonics | |
plc | |
poe | |
polarization | |
polarized invisible code | |
polarized light control | |
polymer-dispersed liquid crystal | |
position estimation | |
poultry | |
power | |
power consumption | |
power electronics | |
power factor | |
power factor correction | |
power factor correction (pfc) | |
power led | |
power leds | |
power line communication | |
power management | energy management |
power quality | |
power saving | energy management |
ppfd | |
precision agriculture | |
prediction | |
predictive control | |
predictive maintenance | predictive maintenance |
principal component analysis | |
privacy | |
production | |
productivity | |
public lighting | public lighting system |
public lighting system | public lighting system |
pulse width modulation | |
pulse width modulation (pwm) | |
pv | |
pv panel | |
pv system | |
pwm | |
pwm dimming | |
python | python |
q-learning | q-learning |
quality | |
quality control | |
quantum dots | |
radar | |
radiance | |
radiated emission | |
radiometry | |
rare earths | |
raspberry pi | raspberry pi |
ray tracing | |
reactive oxygen species | |
reactive power | |
real-time | |
real-time control | |
real-time monitoring | |
real-time systems | |
red light | |
reflectance | |
reflection | |
reflective | |
reflector | |
refractive index | |
regression analysis | regression analysis |
regulation | |
reinforcement learning | RL |
reinforcement learning (rl) | RL |
relays | |
reliability | |
remote control | remote monitoring |
remote monitoring | remote monitoring |
remote sensing | |
renewable energies | |
renewable energy | |
renewable energy sources | |
renovation | |
requirements engineering | |
retina | |
retrofit | |
retrofitting | |
return on investment | |
rfid | |
rgb | |
risk | |
road lighting | Public lighting system |
road safety | |
road traffic congestion | |
road tunnel | |
roadway lighting | Public lighting system |
robustness | |
rural electrification | |
safety | |
saving energy | energy management |
scada | |
scattering | |
scheduling | |
school buildings | public building |
secondary metabolites | |
secondary optics | |
security | security |
segmentation | |
self-assembly | |
self-powered | |
sensitivity analysis | |
sensor | |
sensor fusion | |
sensor network | |
sensor networks | |
sensors | |
servo motor | |
shading | |
signal control | |
signal transduction | |
signalized intersection | |
simulation | |
simulation of urban mobility (sumo) | |
simulink | |
single chip microcomputer | |
single intersection | |
single stage | |
skyglow | |
sleep | |
sleep quality | |
smart | |
smart building | smart building |
smart buildings | smart building |
smart cities | smart city |
smart city | smart city |
smart control | |
smart environments | |
smart grid | smart grid |
smart grids | smart grid |
smart home | smart building |
smart homes | smart building |
smart light | |
smart lighting | smart lighting control system |
smart lighting control | smart lighting control system |
smart lighting system | smart lighting control system |
smart lighting systems | smart lighting control system |
smart lights | smart lighting control system |
smart meters | |
smart street light | smart street lighting system |
smart street lighting | smart street lighting system |
smart street lighting system | smart street lighting system |
smart street lights | smart street lighting system |
smart streetlight | smart street lighting system |
smart systems | |
smart traffic | |
smart traffic light | |
smart traffic lights | |
smart transportation | |
smart window | |
smart windows | |
smartphone | |
soft-switching | |
software | |
software goniophotometer | |
solar | |
solar cell | |
solar cells | |
solar collector | |
solar concentrator | |
solar energy | Daylight |
solar lighting | Daylight |
solar lighting system | Daylight |
solar panel | |
solar photovoltaic | |
solar power | |
solar radiation | Daylight |
solar street lighting | smart street lighting system |
solid state lighting | |
solid-state lighting | |
spatial light modulator | |
spectral power distribution | |
spectrum | |
stability | |
stadium | |
standards | |
statistical analysis | |
stm32 | STM32 |
stray light | |
stray light analysis | |
stray light control | lighting control system |
street lamp | |
street light | street lighting control system |
street light control system | street lighting control system |
street lighting | street lighting control system |
street lighting control | street lighting control system |
street lighting system | street lighting control system |
street lighting systems | street lighting control system |
street lights | street lighting control system |
streetlight | street lighting control system |
streetlights | street lighting control system |
stress | |
structured light | |
style | |
sumo | |
sunlight | daylight |
supercapacitor | |
supplemental lighting | |
surface plasmon resonance | |
surgical lighting | |
survey | |
sustainability | sustainability |
sustainable building | smart building |
sustainable development | sustainability |
sustainable energy | energy management |
sustainable lighting | energy management |
switched-mode power supply | |
synthetic biology | |
system | |
system design | |
system reliability | |
technology | |
telescope | |
temperature | |
temperature sensor | |
temporal light modulation | |
thd | |
thermal analysis | |
thermal comfort | |
thermal conductivity | |
thermal design | |
thermal management | |
thermal resistance | |
thin films | |
thingspeak | |
tomato | |
total harmonic distortion | |
total harmonic distortion (thd) | |
total internal reflection | |
tracking | |
traffic | |
traffic congestion | |
traffic control | |
traffic control systems | |
traffic density | |
traffic flow | |
traffic light | |
traffic light control | |
traffic light control (tlc) | |
traffic light control system | |
traffic light control systems | |
traffic light controller | |
traffic light system | |
traffic lights | |
traffic lights control | |
traffic management | |
traffic monitoring | |
traffic network | |
traffic optimization | |
traffic safety | |
traffic signal | |
traffic signal control | |
traffic simulation | |
transcription | |
transfer learning | |
transition metal dichalcogenides | |
transmittance | |
transportation | |
triboelectric nanogenerator | |
tunable | |
tunnel lighting | tunnel lighting system |
tunnel lighting system | tunnel lighting system |
tunnel management | |
uav | |
ubiquitous computing | ubiquitous computing |
ultrasonic sensor | |
ultraviolet | |
ultraviolet radiation | |
unidirectional observation | |
unified glare rating | |
uniform illumination | User comfort |
uniformity | |
urban intersections | |
urban lighting | Public lighting system |
urban mobility | |
urban traffic | |
urban traffic control | |
urbanization | |
user comfort | User comfort |
user experience | |
user interaction | |
user satisfaction | User comfort |
uv-b | |
validation | |
vanet | |
vehicle density | |
vehicle detection | |
vehicle safety | |
vehicles | |
vehicular communications | |
vehicular networks | |
ventilation | |
vertical farming | |
vertical illuminance | |
vhdl | |
virtual prototyping | |
virtual reality | |
visibility | |
visible | |
visible light | |
visible light communication | |
visible light communication (vlc) | |
visible light communications | |
vision | |
visual comfort | User comfort |
visual fatigue | |
visual inspection | |
visual perception | |
visual performance | User comfort |
visualization | |
vlc | |
voltage regulation | |
waiting time | |
waste | |
wavefront shaping | |
wavelength | |
welfare | |
well-being | |
white led | |
white leds | |
white light | |
wi-fi | |
wind energy | |
window signage | |
windows | |
wireless | |
wireless communication | wireless communication |
wireless network | Wsn |
wireless networks | Wsn |
wireless sensor | Wsn |
wireless sensor and actuator network | Wsn |
wireless sensor network | Wsn |
wireless sensor networks | Wsn |
wireless sensors | wsn |
wsn | wsn |
xbee | |
yield | |
yolo | |
zigbee | zigbee |
zigbee network | zigbee |
zigbee technology | zigbee |
References
- de Kort, Y.; Michalos, A.C. Light and Quality of Life. In Encyclopedia of Quality of Life and Well-Being Research; Springer: Berlin/Heidelberg, Germany, 2014; pp. 3615–3620. [Google Scholar] [CrossRef]
- Paolone, P.; Paesani, R.; Pilotti, F.; Camplone, J.; Piazza, A.; Di Felice, P. Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm. Future Internet 2025, 17, 90. [Google Scholar] [CrossRef]
- Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res. 2021, 133, 285–296. [Google Scholar] [CrossRef]
- Öztürk, O.; Kocaman, R.; Kanbach, D.K. How to design bibliometric research: An overview and a framework proposal. Rev. Manag. Sci. 2024, 18, 3333–3361. [Google Scholar] [CrossRef]
- Passas, I. Bibliometric Analysis: The Main Steps. Encyclopedia 2024, 4, 1014–1025. [Google Scholar] [CrossRef]
- Khan, G.F.; Wood, J. Information technology management domain: Emerging themes and keyword analysis. Scientometrics 2015, 105, 959–972. [Google Scholar] [CrossRef]
- Pesta, B.; Fuerst, J.; Kirkegaard, E.O.W. Bibliometric Keyword Analysis across Seventeen Years (2000–2016) of Intelligence Articles. J. Intell. 2018, 6, 46. [Google Scholar] [CrossRef] [PubMed]
- Narong, D.K.; Hallinger, P. A Keyword Co-Occurrence Analysis of Research on Service Learning: Conceptual Foci and Emerging Research Trends. Educ. Sci. 2023, 13, 339. [Google Scholar] [CrossRef]
- Lim, W.M.; Kumar, S.; Donthu, N. How to combine and clean bibliometric data and use bibliometric tools synergistically: Guidelines using metaverse research. J. Bus. Res. 2024, 182, 114760. [Google Scholar] [CrossRef]
- Van Eck, N.; Waltman, L. Manual for VOSviewer Version 1.6.20; Universiteit Leiden and CWTS: Leiden, The Netherlands, 2023; Available online: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.20.pdf (accessed on 20 February 2025).
- Ghani, N.A.; Teo, P.-C.; Ho, T.C.F.; Choo, L.S.; Kelana, B.W.Y.; Adam, S.; Ramliy, M.K. Bibliometric Analysis of Global Research Trends on Higher Education Internationalization Using Scopus Database: Towards Sustainability of Higher Education Institutions. Sustainability 2022, 14, 8810. [Google Scholar] [CrossRef]
- Allam, Z.; Sharifi, A. Research Structure and Trends of Smart Urban Mobility. Smart Cities 2022, 5, 539–561. [Google Scholar] [CrossRef]
- Ali, I.; Balta; Papadopoulos, M.T. Social media platforms and social enterprise: Bibliometric analysis and systematic review. Int. J. Inf. Manag. 2023, 69, 102510. [Google Scholar] [CrossRef]
- Bukar, U.A.; Sayeed, M.S.; Razak, S.F.A.; Yogarayan, S.; Amodu, O.A.; Mahmood, R.A.R. A method for analyzing text using VOSviewer. MethodsX 2023, 11, 102339. [Google Scholar] [CrossRef]
- Kirby, A. Exploratory Bibliometrics: Using VOSviewer as a Preliminary Research Tool. Publications 2023, 11, 10. [Google Scholar] [CrossRef]
- Tripathy, P.; Jena, P.K.; Mishra, B.R. Systematic literature review and bibliometric analysis of energy efficiency. Renew. Sustain. Energy Rev. 2024, 200, 114583. [Google Scholar] [CrossRef]
- Anggraini, W.; Ranggaini, D.; Ariyani, A.P.; Sulistyowati, I. World Trends in Dental Ergonomics Research: A Bibliometric Analysis. Int. J. Environ. Res. Public Health 2024, 21, 493. [Google Scholar] [CrossRef]
- Viola, A.; Hauge, J.B.; Bugár, G.; Uckelmann, D.; Romagnoli, G. Moving from the Internet of Things to the Industrial Metaverse: A Systematic Literature Review. In Proceedings of the 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), Funchal, Portugal, 24–28 June 2024; pp. 1–7. [Google Scholar] [CrossRef]
- Bukar, U.A.; Sayeed, M.S.; Amodu, O.A.; Razak, S.F.A.; Yogarayan, S.; Othman, M. Leveraging VOSviewer approach for mapping, visualisation, and interpretation of crisis data for disaster management and decision-making. Int. J. Inf. Manag. Data Insights 2025, 5, 100314. [Google Scholar] [CrossRef]
- Krabokoukis, T. Technology Tools in Hospitality: Mapping the Landscape Through Bibliometric Analysis and Presentation of a New Software Solution. Digital 2023, 3, 81–96. [Google Scholar] [CrossRef]
- Shen, X.; Wang, L. Topic evolution and emerging topic analysis based on open-source software. J. Data Inf. Sci. 2020, 5, 126–136. [Google Scholar] [CrossRef]
- Brzozowska-Rup, K.; Nowakowska, M. Bibliometric Studies on Renewable Energy—Poland Compared to Other EU Countries. Energies 2022, 15, 4577. [Google Scholar] [CrossRef]
- Destriana, N.; Zulfikar, R.; Mulyasari, W.; Ismawati, I. Financial Sustainability for a Sustainable Future: A Bibliometric Analysis. Int. J. Sustain. Dev. Plan. 2024, 19, 4653–4662. [Google Scholar] [CrossRef]
- Hisyam; Lin, S.W. Bibliometric analysis of social enterprise literature: Revisit to regroup. J. Innov. Knowl. 2023, 8, 100411. [Google Scholar] [CrossRef]
- Lim, W.M.; Rasul, T.; Kumar, S.; Ala, M. Past, present, and future of customer engagement. J. Bus. Res. 2022, 140, 439–458. [Google Scholar] [CrossRef]
- Kumar, S.; Sahoo, S.; Lim, W.M.; Dana, L. Religion as a social shaping force in entrepreneurship and business: Insights from a technology-empowered systematic literature review. Technol. Forecast. Soc. Change 2022, 175, 121393. [Google Scholar] [CrossRef]
- Alaeddini, M.; Hajizadeh, M.; Reaidy, P. A Bibliometric Analysis of Research on the Convergence of Artificial Intelligence and Blockchain in Smart Cities. Smart Cities 2023, 6, 764–795. [Google Scholar] [CrossRef]
- Lampropoulos, G.; Garzón, J.; Misra, S.; Siakas, K. The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art. Sensors 2024, 24, 1091. [Google Scholar] [CrossRef] [PubMed]
- Taquia-Faustino, A.; Alvitez-Temoche, D.; Mauricio, F.; Medina, J.; Espinoza-Carhuancho, F.; Mayta-Tovalino, F. Trends, Collaborative Networks, and Impact of Infrared Thermography and Thermal Therapies in Dentistry: A Bibliometric Study. J. Contemp. Dent. Pract. 2024, 25, 803–808. [Google Scholar]
- Unlu, R.; Xanthopoulos, P. Estimating the number of clusters in a dataset via consensus clustering. Expert Syst. Appl. 2019, 125, 33–39. [Google Scholar] [CrossRef]
- Jose-Garcia, A.; Gómez-Flores, W. A survey of cluster validity indices for automatic data clustering using differential evolution. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’21), Lille, France, 10–14 July 2021. [Google Scholar] [CrossRef]
- Yuan, M.; Zobel, J.; Lin, P. Measurement of clustering effectiveness for document collections. Inf. Retr. J. 2022, 25, 239–268. [Google Scholar] [CrossRef]
- Rachwal, A.; Poplawska, E.; Gorgol, I.; Cieplak, T.; Pliszczuk, D.; Skowron, L.; Rymarczyk, T. Determining the Quality of a Dataset in Clustering Terms. Appl. Sci. 2023, 13, 2942. [Google Scholar] [CrossRef]
- Ioannou, I.; Christophorou, C.; Nagaradjane, P.; Vassiliou, V. Performance Evaluation of Machine Learning Cluster Metrics for Mobile Network Augmentation. In Proceedings of the 2024 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India, 21–23 March 2024; pp. 1–7. [Google Scholar] [CrossRef]
- Gagolewski, M.; Bartoszuk, M.; Cena, A. Are cluster validity measures (in) valid? Inf. Sci. 2021, 581, 620–636. [Google Scholar] [CrossRef]
- Schubert, E. Stop using the elbow criterion for k-means and how to choose the number of clusters instead. ACM SIGKDD Explor. Newsl. 2023, 25, 36–42. [Google Scholar] [CrossRef]
- Triantafyllopoulos, L.; Paxinou, E.; Feretzakis, G.; Kalles, D.; Verykios, V.S. Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis. Future Internet 2024, 16, 221. [Google Scholar] [CrossRef]
- Waltman, L.; vanEck, N.J.; Noyons, E.C.M. A unified approach to mapping and clustering of bibliometric networks. J. Inf. 2010, 4, 629–635. [Google Scholar] [CrossRef]
- Panday, A.; Ray, T.; Jalandharachari, A.S.; Gopinath, G. Insights into blended learning research: A thorough bibliometric study. Discov. Educ. 2025, 4, 50. [Google Scholar] [CrossRef]
- Baffa, M.A.; Mustaffa, Z.; Ahmad, N.R.; El-Atroush, M.E.; Aliyu Yaro, N.S.; Seghier, M.E.A.B. Performance of geothermal energy piles in buildings: A bibliometric analysis and systematic review. Energy Build. 2025, 331, 115357. [Google Scholar] [CrossRef]
- Thorndike, R.L. Who belongs in the family? Psychometrika 1953, 18, 267–276. [Google Scholar] [CrossRef]
- Davies, D.L.; Bouldin, D.W. A Cluster Separation Measure. IEEE Trans. Pattern Anal. Mach. Intell. 1979, PAMI-1, 224–227. [Google Scholar] [CrossRef]
- Favati, P.; Menchi, O. An internal validity index for arbitrarily shaped clusters. Expert Syst. Appl. 2024, 235, 121124. [Google Scholar] [CrossRef]
- Rousseeuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 1987, 20, 53–65. [Google Scholar] [CrossRef]
- Calinski, T.; Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. 1974, 3, 1–27. [Google Scholar] [CrossRef]
- Singh, V.K.; Singh, P.; Karmakar, M.; Leta, J.; Mayr, P. The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics 2021, 126, 5113–5142. [Google Scholar] [CrossRef]
- Tran, H.K.V.; Börstler, J.; Ali, N.B.; Unterkalmsteiner, M. How good are my search strings? Reflections on using an existing review as a quasi-gold standard. e-Inform. Softw. Eng. J. 2022, 16, 220103. [Google Scholar] [CrossRef]
- Schneider, J.W.; Borlund, P. Introduction to bibliometrics for construction and maintenance of thesauri: Methodical considerations. J. Doc. 2004, 60, 524–549. [Google Scholar] [CrossRef]
- Montecchi, M.; Plangger, K.; West, D.C. Supply chain transparency: A bibliometric review and research agenda. Int. J. Prod. Econ. 2021, 238, 108152. [Google Scholar] [CrossRef]
- Lim, W.M.; Kumar, S. Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Glob. Bus. Organ. Excell. 2024, 43, 17–26. [Google Scholar] [CrossRef]
- Putrada, A.G.; Abdurohman, M.; Perdana, D.; Nuha, H.H. Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey. IEEE Access 2022, 10, 45137–45178. [Google Scholar] [CrossRef]
- Goyal, S.B.; Bedi, P.; Rajawat, A.S.; Shaw, R.N.; Ghosh, A. Smart Luminaires for Commercial Building by Application of Daylight Harvesting Systems. In Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems; Bianchini, M., Piuri, V., Das, S., Shaw, R.N., Eds.; Springer: Singapore, 2022; Volume 218. [Google Scholar] [CrossRef]
- Mohagheghi, A.; Moallem, M. An Energy-Efficient PAR-Based Horticultural Lighting System for Greenhouse Cultivation of Lettuce. IEEE Access 2023, 11, 8834–8844. [Google Scholar] [CrossRef]
- Gupta, A.; Gulati, T.; Bindal, A.K. WSN based IoT applications: A Review. In Proceedings of the 2022 10th International Conference on Emerging Trends in Engineering and Technology–Signal and Information Processing (ICETET-SIP-22), Nagpur, India, 29–30 April 2022; pp. 1–6. [Google Scholar] [CrossRef]
- Wu, T.-F.; Chang, C.-H.; Chen, Y.-H. A fuzzy-logic-controlled single-stage converter for PV-powered lighting system applications. IEEE Trans. Ind. Electron. 2000, 47, 287–296. [Google Scholar] [CrossRef]
- Mohandas, P.; Dhanaraj, J.S.A.; Gao, X.-Z. Artificial Neural Network based Smart and Energy Efficient Street Lighting System: A Case Study for Residential area in Hosur. Sustain. Cities Soc. 2019, 48, 101499. [Google Scholar] [CrossRef]
Year | Documents | Year | Documents |
---|---|---|---|
2024 | 923 | 2009 | 301 |
2023 | 895 | 2008 | 263 |
2022 | 873 | 2007 | 245 |
2021 | 761 | 2006 | 221 |
2020 | 784 | 2005 | 172 |
2019 | 823 | 2004 | 151 |
2018 | 778 | 2003 | 116 |
2017 | 712 | 2002 | 96 |
2016 | 594 | 2001 | 89 |
2015 | 548 | 2000 | 91 |
2014 | 536 | 1999 | 85 |
2013 | 501 | 1998 | 84 |
2012 | 430 | 1997 | 74 |
2011 | 414 | 1996 | 93 |
2010 | 339 | 1995 | 82 |
Document Type | Documents | Document Type | Documents |
---|---|---|---|
Conference Paper | 5699 (46.9%) | Book Chapter | 286 (2.4%) |
Article | 5786 (47.6%) | Review | 377 (3.1%) |
Keywords | NoS | Keywords | NoS |
---|---|---|---|
android application | 1 | LoRa | 1 |
ANN | 3 | maintenance | 1 |
arduino | 4 | MCU | 3 |
artificial intelligence | 1 | ML | 1 |
artificial lighting | 3 | mobile application | 1 |
bluetooth | 1 | motion detection | 4 |
building energy management | 6 | MPPT | 1 |
cloud computing | 2 | MQTT | 1 |
CNN | 2 | multi agent system | 5 |
computer vision | 3 | museum lighting system | 1 |
daylight | 15 | NB-IoT | 1 |
deep q-learning | 2 | NN | 2 |
Digital twin | 1 | occupant behavior | 6 |
dimming control | 4 | office lighting | 1 |
DL | 2 | outdoor lighting system | 1 |
DRL | 2 | PIC MCU | 1 |
edge computing | 1 | PIR sensor | 3 |
embedded system | 2 | plant lighting | 1 |
emergency lighting | 1 | predictive maintenance | 1 |
energy management | 21 | PSO | 1 |
environmental sustainability | 3 | public building | 1 |
face recognition | 1 | public lighting system | 5 |
fluorescent lamp | 4 | python | 1 |
fog computing | 1 | q-learning | 1 |
FPGA | 1 | raspberry pi | 1 |
fuzzy control | 4 | regression analysis | 1 |
genetic algorithm | 3 | remote monitoring | 2 |
green building | 4 | RL | 2 |
high pressure sodium lamp | 3 | security | 1 |
home automation | 1 | Smart building | 7 |
human centric lighting | 2 | smart city | 2 |
hybrid lighting system | 2 | smart grid | 2 |
illumination control | 8 | smart lighting control system | 11 |
illumination sensor | 2 | smart street lighting system | 6 |
image processing | 4 | STM32 | 1 |
indoor lighting system | 4 | street lighting control system | 9 |
industrial lighting | 1 | sustainability | 2 |
infrared sensor | 2 | tunnel lighting system | 2 |
IoT | 4 | ubiquitous computing | 2 |
LDR sensor | 4 | user comfort | 8 |
LED | 1 | wireless communication | 1 |
LED lighting system | 33 | WSN | 8 |
light sensor | 2 | zigbee | 3 |
lighting control system | 25 |
Out-of-scope keywords | adaptive optics | cultural heritage |
agriculture | traffic control | |
aircraft | traffic light control | |
airports | traffic congestion | |
algae | traffic light | |
antioxidant | traffic light control | |
COVID-19 | intelligent transportation | |
Too-generic keywords | 2D code | design |
2D materials | driver | |
3D display | photosynthesis | |
3D printing | visible light communication | |
3D reconstruction biomass | power quality | |
data-driven | light |
NoC | NoK | DB Score | Sscore | CH Score | |
---|---|---|---|---|---|
5 | 11 | 87 | 2.428 | 0.034 | 25.090 |
10 | 9 | 73 | 2.299 | 0.075 | 26.140 |
15 | 7 | 63 | 1.774 | 0.138 | 27.104 |
20 | 7 | 56 | 1.188 | 0.184 | 29.540 |
25 | 6 | 50 | 1.184 | 0.170 | 22.042 |
50 | 5 | 33 | 0.949 | 0.216 | 22.951 |
60 | 4 | 29 | 0.695 | 0.305 | 23.381 |
65 | 4 | 27 | 0.761 | 0.279 | 20.508 |
70 | 3 | 25 | 0.818 | 0.401 | 26.500 |
75 | 3 | 22 | 0.896 | 0.340 | 16.611 |
100 | 3 | 15 | 1.338 | 0.089 | 5.621 |
Cluster | NK | Top Five Keywords | Occ |
---|---|---|---|
1 | 160 | traffic light control | 183 |
optimization | 91 | ||
smart city | 86 | ||
reinforcement learning | 81 | ||
machine learning | 79 | ||
2 | 121 | led lighting | 156 |
street lighting | 139 | ||
solar energy | 91 | ||
led driver | 60 | ||
power quality | 59 | ||
3 | 112 | light control | 165 |
solid-state lighting | 32 | ||
optogenetics | 30 | ||
energy efficient | 26 | ||
outdoor lighting | 26 | ||
4 | lighting system | 238 | |
energy saving | 208 | ||
lighting control | 135 | ||
simulation | 98 | ||
energy consumption | 90 | ||
5 | leds | 73 | |
light-emitting diodes | 55 | ||
photosynthesis | 55 | ||
light-emitting diode | 54 | ||
light quality | 46 | ||
6 | internet of things | 174 | |
smart lighting | 156 | ||
iot | 145 | ||
microcontroller | 79 | ||
sensors | 72 | ||
7 | lighting | 381 | |
light | 78 | ||
melatonin | 32 | ||
environment | 31 | ||
circadian rhythm | 27 | ||
8 | led | 434 | |
energy management | 49 | ||
design | 39 | ||
tunnel lighting | 35 | ||
luminance | 33 | ||
9 | energy efficiency | 389 | |
sustainability | 74 | ||
visual comfort | 56 | ||
artificial lighting | 43 | ||
energy audit | 42 | ||
10 | light emitting diode | 60 | |
visible light communication | 57 | ||
intelligent lighting | 54 | ||
safety | 32 | ||
daylight harvesting | 25 | ||
11 | illumination | 93 | |
energy | 57 | ||
efficiency | 47 | ||
building automation | 43 | ||
performance evaluation | 22 | ||
12 | sensor | 46 | |
control system | 40 | ||
pwm | 35 | ||
neural network | 31 | ||
wireless communication | 29 | ||
13 | illuminance | 88 | |
daylight | 76 | ||
light pollution | 60 | ||
dimming | 45 | ||
intelligent lighting system | 33 | ||
14 | lighting systems | 148 | |
zigbee | 78 | ||
intelligent control | 35 | ||
artificial neural networks | 18 | ||
calibration | 18 | ||
15 | renewable energy | 62 | |
public lighting | 49 | ||
light emitting diodes | 43 | ||
photovoltaic | 37 | ||
smart grid | 33 | ||
16 | oled | 15 | |
light sources | 14 | ||
cct | 7 | ||
rare earths | 7 | ||
lifetime | 5 |
Keywords | Occurrences | Links |
---|---|---|
LED | 434 | 401 |
energy efficiency | 389 | 312 |
lighting | 381 | 361 |
lighting system | 238 | 231 |
energy saving | 208 | 231 |
traffic light control | 183 | 118 |
internet of things | 174 | 207 |
light control | 165 | 133 |
led lighting | 156 | 170 |
smart lighting | 156 | 173 |
lighting systems | 148 | 150 |
Iot | 145 | 186 |
street lighting | 139 | 168 |
lighting control | 135 | 164 |
simulation | 98 | 122 |
illumination | 93 | 122 |
optimization | 91 | 129 |
solar energy | 91 | 124 |
energy consumption | 90 | 136 |
energy savings | 88 | 125 |
illuminance | 88 | 122 |
smart city | 86 | 118 |
daylighting | 85 | 107 |
reinforcement learning | 81 | 76 |
machine learning | 79 | 104 |
microcontroller | 79 | 109 |
light | 78 | 94 |
zigbee | 78 | 103 |
image processing | 77 | 88 |
daylight | 76 | 104 |
Keywords | Occ | Keywords | Occ |
---|---|---|---|
traffic light control | 183 | signalized intersection | 9 |
optimization | 91 | smart transportation | 9 |
smart city | 86 | traffic signal | 9 |
reinforcement learning | 81 | waiting time | 9 |
machine learning | 79 | deep q-network | 8 |
image processing | 77 | defect detection | 8 |
fuzzy logic | 66 | intersections | 8 |
deep learning | 64 | light sensors | 8 |
wireless sensor network | 57 | smart light | 8 |
artificial intelligence | 47 | smart traffic lights | 8 |
deep reinforcement learning | 47 | traffic network | 8 |
traffic control | 46 | cellular automata | 7 |
intelligent transportation system | 42 | convolutional neural networks | 7 |
machine vision | 42 | deep neural networks | 7 |
traffic light | 42 | deep reinforcement learning (drl) | 7 |
computer vision | 41 | fog computing | 7 |
traffic congestion | 37 | fuzzy controller | 7 |
traffic lights control | 36 | green wave | 7 |
traffic signal control | 32 | image acquisition | 7 |
fuzzy control | 31 | intelligent traffic control | 7 |
genetic algorithm | 29 | intelligent traffic light | 7 |
sumo | 28 | intelligent transport system | 7 |
plc | 27 | intelligent transportation | 7 |
traffic management | 26 | python | 7 |
intelligent transportation systems | 25 | requirements engineering | 7 |
traffic lights | 25 | smart streetlight | 7 |
adaptive control | 21 | smart traffic light | 7 |
artificial neural network | 21 | traffic light control systems | 7 |
fpga | 21 | transfer learning | 7 |
object detection | 21 | vhdl | 7 |
vanet | 21 | visual inspection | 7 |
digital twin | 20 | conflict resolution | 6 |
neural networks | 20 | data fusion | 6 |
q-learning | 20 | deep q-learning | 6 |
traffic light control system | 20 | markov decision process | 6 |
wsn | 20 | multiple intersections | 6 |
adaptive traffic light control | 19 | pedestrian detection | 6 |
traffic flow | 19 | predictive maintenance | 6 |
congestion | 17 | real-time | 6 |
fuzzy logic controller | 17 | real-time control | 6 |
intersection | 17 | reflection | 6 |
optimal control | 17 | scada | 6 |
rfid | 17 | signal control | 6 |
component | 16 | single intersection | 6 |
its | 16 | smart traffic | 6 |
multi-agent system | 16 | traffic control systems | 6 |
particle swarm optimization | 16 | traffic monitoring | 6 |
traffic | 16 | urban traffic | 6 |
ambient intelligence | 15 | urban traffic control | 6 |
embedded system | 15 | urbanization | 6 |
intelligent transport systems | 14 | vehicles | 6 |
intelligent transportation system | 14 | adaptive systems | 5 |
multi-agent | 14 | adaptive traffic signal control | 5 |
traffic simulation | 14 | algorithms | 5 |
edge computing | 13 | arduino microcontroller | 5 |
traffic light controller | 13 | bi-level optimization | 5 |
transportation | 13 | building energy consumption | 5 |
big data | 12 | context-aware | 5 |
camera | 12 | distributed systems | 5 |
multi-objective optimization | 12 | dynamic control | 5 |
adaptive | 11 | emergency vehicle | 5 |
cloud computing | 11 | emergency vehicles | 5 |
embedded systems | 11 | image segmentation | 5 |
intelligent systems | 11 | intelligent traffic system | 5 |
intelligent traffic | 11 | internet of vehicles | 5 |
motion detection | 11 | model | 5 |
multi-agent reinforcement learning | 11 | multi agent system | 5 |
traffic optimization | 11 | negotiation | 5 |
vehicle detection | 11 | privacy | 5 |
yolo | 11 | real-time systems | 5 |
genetic algorithms | 10 | reinforcement learning (rl) | 5 |
intelligent traffic light control | 10 | road traffic congestion | 5 |
multi-agent systems | 10 | simulation of urban mobility | 5 |
opencv | 10 | smart street lights | 5 |
autonomous vehicles | 9 | style | 5 |
clustering | 9 | traffic light control (tlc) | 5 |
edge detection | 9 | urban intersections | 5 |
petri nets | 9 | urban mobility | 5 |
quality control | 9 | vehicle density | 5 |
scheduling | 9 | vehicular networks | 5 |
Synonym Keywords | Cluster |
---|---|
lighting system | 4 |
lighting systems | 14 |
led | 8 |
leds | 5 |
led lighting | 2 |
light-emitting diode | 5 |
light-emitting diodes | 5 |
light emitting diode | 10 |
light emitting diodes | 15 |
Keywords | Cluster | Links | TLS | Occ |
---|---|---|---|---|
led lighting system | 1 | 22 | 593 | 1282 |
lighting control system | 1 | 24 | 556 | 862 |
energy management | 1 | 24 | 481 | 522 |
daylight | 1 | 20 | 292 | 372 |
illumination control | 1 | 19 | 187 | 222 |
user comfort | 1 | 16 | 135 | 130 |
public lighting system | 1 | 15 | 99 | 113 |
sustainability | 1 | 13 | 52 | 88 |
artificial lighting | 1 | 12 | 58 | 72 |
dimming control | 1 | 13 | 74 | 71 |
iot | 2 | 24 | 364 | 382 |
smart lighting control system | 2 | 22 | 292 | 310 |
street lighting control system | 2 | 22 | 189 | 236 |
smart building | 2 | 20 | 165 | 166 |
wsn | 2 | 16 | 153 | 146 |
fuzzy control | 2 | 19 | 96 | 120 |
arduino | 2 | 14 | 95 | 95 |
mcu | 2 | 17 | 79 | 95 |
zigbee | 2 | 16 | 108 | 89 |
smart city | 3 | 18 | 146 | 148 |
computer vision | 3 | 13 | 59 | 101 |
image processing | 3 | 15 | 56 | 95 |
rl | 3 | 9 | 30 | 86 |
ml | 3 | 14 | 54 | 80 |
dl | 3 | 15 | 53 | 71 |
Keywords | Occ | Keywords | Occ |
---|---|---|---|
led lighting system | 1282 | public lighting system | 113 |
lighting control system | 862 | computer vision | 101 |
energy management | 522 | arduino | 95 |
iot | 382 | mcu | 95 |
daylight | 372 | image processing | 95 |
smart lighting control system | 310 | zigbee | 89 |
street lighting control system | 236 | sustainability | 88 |
illumination control | 222 | rl | 86 |
smart building | 166 | ml | 80 |
smart city | 148 | artificial lighting | 72 |
wsn | 146 | dimming control | 71 |
user comfort | 130 | dl | 71 |
fuzzy control | 120 |
Keywords | Links | Keyword | Links |
---|---|---|---|
lighting control system | 24 (100%) | user comfort | 16 (66.7%) |
energy management | 24 (100%) | zigbee | 16 (66.7%) |
IoT | 24 (100%) | public lighting system | 15 (62.5%) |
led lighting system | 22 (91.7%) | image processing | 15 (62.5%) |
smart lighting control system | 22 (91.7%) | DL | 15 (62.5%) |
street lighting control system | 22 (91.7%) | Arduino | 14 (58.3%) |
daylight | 20 (83.3%) | ML | 14 (58.3%) |
smart building | 20 (83.3%) | computer vision | 13 (54.2%) |
illumination control | 19 (79.2%) | sustainability | 13 (54.2%) |
fuzzy control | 19 (79.2%) | dimming control | 13 (54.2%) |
smart city | 18 (75.0%) | artificial lighting | 12 (50.0%) |
MCU | 17 (70.8%) | RL | 9 (37.5%) |
Keyword | AVG | Keyword | AVG |
---|---|---|---|
image processing | 2015.5158 | street lighting control system | 2017.9873 |
wsn | 2015.9795 | public lighting system | 2018.4159 |
lighting control system | 2016.2193 | artificial lighting | 2018.5139 |
dimming control | 2016.2394 | smart lighting control system | 2018.5613 |
illumination control | 2016.3649 | smart building | 2018.6265 |
daylight | 2016.5108 | smart city | 2020.0405 |
computer vision | 2016.7030 | sustainability | 2020.1477 |
user comfort | 2016.9538 | iot | 2020.5288 |
zigbee | 2016.9551 | arduino | 2020.7263 |
led lighting system | 2016.9633 | rl | 2021.1047 |
energy management | 2017.1494 | ml | 2021.3750 |
mcu | 2017.4421 | dl | 2022.0704 |
fuzzy control | 2017.6500 |
Keyword | Link Strength | Keyword | Link Strength |
---|---|---|---|
LED Lighting system | 129 | Zigbee | 10 |
Energy management | 99 | MCU | 9 |
daylight | 45 | Sustainability | 8 |
illumination control | 44 | arduino | 8 |
smart building | 33 | computer vision | 8 |
user comfort | 29 | dimming control | 8 |
smart lighting control system | 26 | artificial lighting | 7 |
IoT | 25 | RL | 3 |
WSN | 24 | smart city | 3 |
fuzzy control | 11 | ML | 2 |
image processing | 11 | street lighting control system | 2 |
public lighting system | 10 | DL | 2 |
Without Thesaurus | Links | Occ | With Thesaurus | Links | Occ |
---|---|---|---|---|---|
led | 401 | 434 | led lighting system | 70 | 1282 |
energy efficiency | 312 | 389 | lighting control system | 69 | 862 |
lighting | 361 | 381 | energy management | 58 | 522 |
lighting system | 231 | 238 | iot | 63 | 382 |
energy saving | 231 | 208 | daylight | 42 | 372 |
traffic light control | 118 | 183 | smart lighting control system | 57 | 310 |
internet of things | 207 | 174 | street lighting control system | 45 | 236 |
light control | 133 | 165 | illumination control | 34 | 222 |
led lighting | 170 | 156 | smart building | 44 | 166 |
smart lighting | 173 | 156 | smart city | 42 | 148 |
lighting systems | 150 | 148 | wsn | 34 | 146 |
iot | 186 | 145 | user comfort | 33 | 130 |
street lighting | 168 | 139 | fuzzy control | 35 | 120 |
lighting control | 164 | 135 | public lighting system | 25 | 113 |
simulation | 122 | 98 | computer vision | 24 | 101 |
illumination | 122 | 93 | image processing | 23 | 95 |
optimization | 129 | 91 | arduino | 27 | 95 |
solar energy | 124 | 91 | mcu | 36 | 95 |
energy consumption | 136 | 90 | zigbee | 35 | 89 |
energy savings | 125 | 88 | sustainability | 16 | 88 |
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Paolone, G.; Piazza, A.; Pilotti, F.; Paesani, R.; Camplone, J.; Di Felice, P. A Map of the Research About Lighting Systems in the 1995–2024 Time Frame. Computers 2025, 14, 313. https://doi.org/10.3390/computers14080313
Paolone G, Piazza A, Pilotti F, Paesani R, Camplone J, Di Felice P. A Map of the Research About Lighting Systems in the 1995–2024 Time Frame. Computers. 2025; 14(8):313. https://doi.org/10.3390/computers14080313
Chicago/Turabian StylePaolone, Gaetanino, Andrea Piazza, Francesco Pilotti, Romolo Paesani, Jacopo Camplone, and Paolino Di Felice. 2025. "A Map of the Research About Lighting Systems in the 1995–2024 Time Frame" Computers 14, no. 8: 313. https://doi.org/10.3390/computers14080313
APA StylePaolone, G., Piazza, A., Pilotti, F., Paesani, R., Camplone, J., & Di Felice, P. (2025). A Map of the Research About Lighting Systems in the 1995–2024 Time Frame. Computers, 14(8), 313. https://doi.org/10.3390/computers14080313