Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2022.0092316

Corresponding author: E. Mohammadrezaei (e-mail: elliemh@vt.edu).

# Systematic Review of Extended Reality for Smart Built Environments Lighting
Design Simulations

E. MOHAMMADREZAEI1  S. GHASEMI2  P. DONGRE3  D. GRAČANIN4  (Senior Member
IEEE)  and H. ZHANG5  Department of Computer Science, Virginia Tech, 220
Gilbert Street, Room 3213, Blacksburg, VA 24060, USA (e-mail<EMAIL_ADDRESS>Department of Computer Science, Virginia Tech, 220 Gilbert Street, Room 3213,
Blacksburg, VA 24060, USA (e-mail<EMAIL_ADDRESS> Department of Computer
Science, Virginia Tech, 220 Gilbert Street, Room 3213, Blacksburg, VA 24060,
USA (e-mail<EMAIL_ADDRESS> Department of Computer Science, Virginia Tech,
220 Gilbert Street, Room 3207, Blacksburg, VA 24060, USA (e-mail:
<EMAIL_ADDRESS> Department of Mechatronics Engineering, Middle Tennessee
State University, Davis Science Building (DSB), 1301 E Main St, Room 121,
Murfreesboro, TN 37132, USA (e-mail<EMAIL_ADDRESS>
###### Abstract

This systematic literature review paper explores the use of extended reality
(XR) technology for smart built environments and particularly for smart
lighting systems design. Smart lighting is a novel concept that has emerged
over a decade now and is being used and tested in commercial and industrial
built environments. We used PRISMA methodology to review 270 research papers
published from 1968 to 2023. Following a discussion of historical advances and
key modeling techniques, a description of lighting simulation in the context
of extended reality and smart built environment is given, followed by a
discussion of the current trends and challenges.

###### Index Terms:

Extended reality, Human-Centric Lighting, Lighting Simulations

=-15pt

##  I Introduction

The visual aspect of light has been the topic of many studies for more than
500 years. The drawings of Leonardo da Vinci, around 1489, show the connection
between the eye and the brain [1, 2]. In 1722 Antony van Leeuwenhoek
discovered the presence of “rod and cone cells” in the retina [3]. In 1834,
Gottfried Treviranus, a German physician [4], confirmed “the light-sensitive
photoreceptors.” It took 150 years to find the third type of photoreceptor in
the retina that responds to the light even when all input from rods and cones
are blocked [5, 6].

Throughout these years, many researchers and authoritative bodies worked to
define standards and regulations to provide a healthy and comfortable lighting
environment [7, 8, 9]. The foundation of these standards considers the visual
aspect of the light, and most lighting designs evolved around this aspect,
which includes how the eye and brain perceive light.

In recent years interest in a non-visual aspect of light is being increased.
To name a few, considering circadian rhythm in lighting design and using
phototherapy for Seasonal Affective Disorder (SAD) [10] are some of the
examples that have gained attention. Human-centric lighting [11] is a term
used to integrate both visual and non-visual effects of lighting to explore
both the physiological and/or psychological benefits of light. As our
understanding of lighting and its effect on the environment increases, more
sophisticated models and simulations are required to reflect such effects
correctly.

The current advancement in XR has introduced new methods and approaches to
study the effect of lighting that was never accessible to the research
community. Our goal is to gain insight into traditional simulations and recent
approaches that leverage XR to provide realistic scenarios that describe some
of the characteristics of light that previously were difficult to explore. We
also examine studies that explore the psychological aspect of lighting effects
and the capabilities of modern lighting designs.

The remainder of the paper is organized as follows. Section II provides the
background. Section III provides information about our systematic approach and
defines our research questions and approach to collecting related research. In
Section IV we provide a summary of our findings for each defined research
question. Section V provides a summary of our finding and explain the
necessary steps required for future studies in this area. Section VI concludes
the paper.

##  II Background

Extended reality is an umbrella term that encompasses various immersive
technologies, including augmented reality (AR), virtual reality (VR), and
mixed reality (MR). However, academics and professionals have been
inconsistent in their use of these terms. As a result, this inconsistency has
given rise to confusion in understanding and indistinct boundaries [12]. XR
refers to the spectrum of experiences that combine real-world elements with
digital content, creating a continuum that ranges from fully physical reality
to fully virtual environments. XR technologies have been gaining increasing
attention in various fields for creating immersive, interactive and engaging
user experiences. Smart built environments (SBE) on the other hand, refer to
built environments that leverage advanced technologies like Internet of Things
(IoT), Artificial Intelligence (AI), and automation to optimize building
performance and enhance user comfort. In this context, XR has the potential to
significantly contribute to SBE, particularly in the area of lighting design
simulations.

Lighting design plays a crucial role in shaping the built environment and has
a significant impact on energy consumption, user comfort, and well-being. By
using XR technologies for lighting design simulations, architects, engineers,
and designers can test and visualize different lighting design options in a
virtual environment. This can help them make informed design decisions, reduce
design errors, and avoid costly rework. XR simulations can also provide a more
accurate representation of the actual lighting conditions in a built
environment, which is crucial for designing spaces that meet user needs and
preferences.

Despite the potential benefits, research on smart lighting design within XR
lacks cohesion and requires a more in-depth and structured approach.
Nonetheless, there have been instances where XR has been applied to lighting
design simulations with encouraging outcomes. For instance, some studies have
used VR to create immersive simulations that allow designers to manipulate
lighting conditions in a virtual environment. This allows designers to test
different lighting scenarios and quickly evaluate their impact on the space
and its occupants. Similarly, MR has been used to simulate the effect of
different lighting conditions on user experience in different spaces like
offices or hospitals. By using MR, designers can observe the impact of
lighting on the environment, user behavior and movement, and the effect of
lighting on the overall user experience. AR has also been harnessed in some
studies to simulate lighting design and visualize lighting fixtures in real-
time, which can help designers to optimize lighting conditions and identify
design issues before implementation. However, there are some challenges
associated with the use of XR technologies for lighting design simulations in
SBE. These challenges include high costs associated with purchasing and
maintaining XR equipment, privacy and security concerns related to collecting
and using data generated by XR systems, and a lack of standards and guidelines
for XR in SBE. Despite these challenges, the potential benefits of using XR
technologies for lighting design simulations in SBE are significant. They
include faster design iterations, reduced costs, improved user experience, and
energy efficiency. Therefore, a systematic review of the existing literature
on the use of XR for lighting design simulations in SBE is crucial to fully
understand the potential of this technology and its challenges. This review
will serve as a valuable resource for researchers, practitioners, and
policymakers interested in using XR to optimize the performance and efficiency
of the built environment.

##  III Materials and method

A systematic Literature Review (SLR) is a strategy for discovering, analyzing,
and summarizing published primary research publications on a particular topic
of interest. Its goal is to answer specific research questions, identify
knowledge gaps, and suggest future possibilities [13, 14]. We reviewed the
research studies and modern frameworks for SBE design and related XR
technologies. We focused on design of a smart lighting system and investigated
factors in smart lighting design in residential housing context that have a
significant impact on user experience of such spaces. There is a variety of
SLR methodologies and recommendations to accomplish the SLR process, including
PRISMA [15] used in this study. The three-step review phases include planning
the review, conducting the review, and reporting the review.

###  III-A Stage 1: Planning the review:

During the planning phase, a research procedure is conducted to guide the SLR
process and increase the accuracy of the review process. The research
procedure is composed of the research objectives, research questions, details
about the research strategy (i.e., search keywords and resources that will be
searched) and eligibility criteria explaining both the inclusion and exclusion
criteria of the research results. We explain in-depth the contents of this
research procedure.

####  III-A1 Research objectives

The primary research objectives include:

RO1:

_Investigating current trends in smart lighting and state of the art in
lighting simulation tools._

RO2:

_Previewing the current state of application of XR in smart lighting design in
SBE context._

RO3:

_Investigating how modern smart lighting systems can enhance the well-being
and comfort of users._

####  III-A2 Research questions

We aim to answer the following main research questions:

RQ1:

_What is state of the art in lighting simulation tools?_

This research question provides a focused insight into the concept of
intelligent lighting design and modern smart lighting simulation tools in the
context of smart built environment.

RQ2:

_How can XR technology be used for SBE lighting system modeling?_

This research question provides a comprehensive description of the concept of
XR technology which is a novel concept and how it has been developed and
implemented in the SBE context to assist lighting design simulation.

RQ3:

_What are psychological effects of lighting on human behavior and activities?_

This research question sheds the light on the effectiveness of the light on
the quality of life of people and how the different lighting design strategies
can enhance this experience and their well-being and comforts.

To determine the scope of the SLR study, the research questions are developed
using the PICOC methodology. Population, Intervention, Comparison, Outcomes,
and Context are the five criteria in the PICOC model. The first criterion,
population, is related to the investigation’s participants or target system.
Interventions are actions that define the investigation’s aspects and topics
of interest to researchers. The comparison refers to the aspects of study
against which the intervention is evaluated. The investigation’s result is
determined by the outcome. Finally, context is related to the investigation’s
environment.

####  III-A3 Search strategy

Following the identification of research objectives, we use a search technique
to find studies that are related to the study’s objective. Table I shows the
primary and secondary search terms along with the final search phrase.

##### Source selection:

Building a knowledge base from significant resources and databases is the
first stage in developing a research strategy. We incorporated four databases:
ACM Digital Library, IEEE Xplore, ScienceDirect, and VT Library.

##### Search phrase:

The process of selecting our search terms was guided by the need to address
our research questions comprehensively. We aimed to identify keywords that
would best assist us in our inquiry.

To achieve this, we considered a range of terms that were relevant to our
research scope, which encompasses Smart Built Environments, Extended Reality
(XR), and their intersections with lighting design, simulation tools, and
human factors.

The exclusion of certain similar terms was guided by our focus on relevance
and the need to maintain a manageable search scope. We aimed to keep a balance
between specificity and comprehensiveness in constructing the search phrase.

The resulting search phrase, as outlined in Table I, represents our effort to
target a wide range of relevant literature while ensuring the coherence of our
search strategy. The finalized search phrases were created using logical
operators AND and OR. It combines key terms related to lighting, XR
technologies, and human factors to provide a holistic perspective on the
subject.

TABLE I: Search terms.

Primary search terms | Smart built environment, Extended reality
---|---
Smart lighting design, Lighting simulation tools
Secondary search terms | Lighting, Modern lighting, Smart lighting design
lighting simulation, Lighting simulation tools
Lighting evaluation, Psychophysiology, Human behavior
Human activities, Extended reality, Mixed reality
Final search phrase | (Lighting OR modern lighting OR
smart lighting design OR lighting simulation OR Lighting simulation tools
OR lighting evaluation) AND (Extended reality OR mixed reality OR
virtual reality OR augmented reality) AND
(Human Psychophysiology OR human behavior OR human activities)

####  III-A4 Eligibility criteria

Following a review of all relevant studies based on the provided search terms,
a screening method was used to select only those that met the inclusion
requirements. Table II illustrates the process on inclusion/exclusion
criteria.

TABLE II: Eligibility criteria.

Inclusion criteria | Exclusion criteria
---|---
Publication Year: 1994–2023 | Document is not related to the search topic.
Peer reviewed documents | Workshop, panel, tutorial, seminar, interview,
or poster.
Available online | Document is book or magazine.
Document responds to at least one of the RQs. | Marginal publication venue.

###  III-B Stage 2: Conducting the Review

The reviewing process was conducted through three main phases:

####  III-B1 Phase-1

Throughout this stage, we evaluated the papers’ relevance to our study based
on their title, abstract, introduction, and conclusion. Throughout this
process, we sought to be as inclusive as possible to ensure that no research
were overlooked. This phase led to the exclusion of 182 studies.

####  III-B2 Phase-2

We examined the trustworthiness of the selected articles using two credibility
criteria in the second step. Once a study meets one of these two credible
requirements, it is included:

  1. 1.

The research should be published at a high-impact and well-known conference or
journal. As a result, we established criteria to include journal articles with
an impact factor of at least 3. The inclusion of conferences was based on
their repute.

  2. 2.

Studies having more than 10 citations will be maintained and carried on to the
next round. As is well known, the quantity of citations represents the study’s
importance and significant impact. 114 studies were eliminated at the end of
this stage.

####  III-B3 Phase-3

The final phase is a detailed screening of the research that have been
submitted. The entire texts of these papers were examined using the
eligibility criteria, and a total of 270 publications were found to be
relevant to our study topics to some extent and were chosen as primary
studies. Figure 1 is the illustrated result of the complete screening process.

Figure 1: Flowchart demonstrating method of screening phases.

###  III-C Stage 3: Reporting the review:

A data extraction approach was used to relocate the included primary studies.
Each primary study was downloaded from its digital repository and given a
unique file ID in this strategy. To fill in the details of each study, an
excel spreadsheet was prepared. The study’s abstract, country of origin, user
experience problems, set of factors, significance and limitations of the
study, study gap, recommended solution, and future work were all retrieved and
placed in their appropriate columns. As a result, as shown in the next
section, the saved analysis information was used to analyze the data and
respond to the RQs.

##  IV Results

###  IV-A RQ1: What is the state of the art in lighting simulation tools?

The field of smart lighting design is experiencing rapid growth and is
predominantly utilized in disciplines such as architecture and entertainment.
Unlike traditional lighting design, smart lighting design brings new
functionalities to traditionally lit spaces. These include the ability to
influence the atmosphere, improve energy efficiency, highlight areas with
higher foot traffic, and preserve lighting in unpopulated regions [16].

Due to the rigorous requirements for reality representation, lighting
simulation is complex, but it also offers various levels of complexity for
multiple users within the same field. One of its many advantages is that it
can give researchers quicker and more efficient ways to compare more complex
data that would have taken much longer. It also takes the place of rigid scale
models and large, energy-intensive equipment. Designers can compare and change
the effects of artificial and natural lighting on project alternatives. It is
simpler to picture the advantages of well-designed natural and artificial
lighting. Integrating lighting simulation with other building simulations and
the design process is possible.

A critical literature review helps to advance various ideas and map out field
developments. This is significant since lighting simulations are rapidly
replacing traditional verification methodologies. In a poll conducted in 1994,
about 77% of respondents said they employed both computers and actual models
in their professional work. Participants who did not use any program for
daylight prediction fell to 21% by 2004 [17]. As architecture and engineering
students become more accustomed to computer modeling, this number may drop
even more [18]. The use of lighting simulation will also rise due to more
sophisticated ways to prove compliance with newer, complex construction rules
and certifications [19, 20].

This section contains in-depth information and a discussion of the many
simulation technologies that various researchers have used to simulate their
modules and prototypes. Based on this analysis, extensive data that includes
all of the simulation tools used for artificial lighting in buildings and the
many factors that go along with them have been summarized in Tables VI and
VIII. The primary goal of this study is to identify the simulation tool that
is most frequently used to simulate illumination in building envelopes.

Throughout this review, a total of 65 papers that were published between the
years 2001 and 2017 were reviewed. Only 25.71% of conference papers, which
were highly relevant to the construction and energy sectors, as shown in
Figure 1, were considered for this review, accounting for nearly 74.29% of
peer-reviewed articles. In 2013, 15.71% of the highest publishing ratio was
noted. According to the current literature analysis, 14.28% in 2016, 10% in
2012, and just 8.57% in 2017 have been published as of this writing.

####  IV-A1 Historical Developments

Computer calculations are ideal for predicting light behavior. Formulas claim
it would take a long time to solve them manually. As the complexity of the
scene being calculated increases, so does the complexity. The design of
electric lighting installations in sealed rectangular rooms was one of the
earliest repeated applications to use computer calculation—some of the
earliest initiatives in that direction date back to the 1970s [21]. During the
same decade, efforts were undertaken to use streamlined methods to incorporate
calculations for both natural and artificial illumination under set conditions
[22]. The output was mainly numerical.

Computer graphics simultaneously made significant advancements, leading to
notable results in the 1980s. Using computational methods, researchers
enhanced the calculation and representation of light falling on arbitrary
volumes [23]. These estimations were not limited to any particular scenario
because they were based on light travel and material characteristics. Results
could be retrieved as numbers or as photos that closely resemble reality.
Physically-based rendering was established as a distinct computational field
[24]. The fields of lighting research, architectural representation, and
cinematography are some examples of those who would directly profit from these
advancements. Entirely correct computations, however, turned out to be
challenging to specify, computer-intensive, and subject to implicit
constraints [25]. Early lighting modeling algorithms were thus constrained to
simplistic geometries, no consideration of sunshine or energy, and erroneous
output of only a few parameters [26]. Modern models address these
characteristics; however, developing computation-efficient techniques to solve
the “global illumination problem” is still a work in progress [27].

Even while advancements in the two primary areas contribute to the other,
lighting simulation can still be split into these categories. The first
involves creating artistic visuals using photo-realistic rendering.
Physically-based visualization is the second area and the subject of this
review (also known as predictive rendering). It is concerned with accurately
depicting and predicting reality under predetermined circumstances while
adhering to physical rules [28, 29].

####  IV-A2 Modelling

A precise and definite set of instructions is an algorithm. They are used in
simulations to address issues with lighting distribution to combine several
physical formulas [30]. Dutre et al. thoroughly described the foundations and
advancements of lighting simulation methods. They claim that current physics
theories (such as quantum optics) explaining how light moves through all kinds
of media are too intricate for image production and computation. Instead,
physical formulas are known from a simplified geometrical optics model and
energy conservation. This model can produce numerical results for the
simulation space and handle most illumination issues with various light
sources. However, when diffusing or reflecting media are involved, challenges
arise (such as advanced optical materials) [31]. Additionally, steady-state
light distribution is assumed. These limitations may explain discrepancies
between observations and simulations in specific modeling scenarios.

The geometrical optics model was gradually implemented to address the global
illumination issue. Hurdles were posed by calculating surface reflectivity
according to the material. Different sub-models that detail their
bidirectional reflection distribution functions can be used to depict them
(BRDFs). The first steps were taken in computer graphics, but the results were
only able to assign colors (rasterization) and determine hard shadows [32].
Raytracing and radiosity, two of the most widely used illumination techniques
today, were later developed.

##### Lighting simulation algorithms and calculation tools

Researchers provided an early assessment of daylighting simulation resources
and algorithms as an inventory of approaches for comprehensive analysis tools.
Currently, there are three different categories of lighting simulation
methods. These are scene-dependent algorithms, view-dependent algorithms, and
direct calculations. Modern models can use one of them or a combination of
them. Although some phenomena (such as fog, specific diffusing materials,
etc.) are challenging to calculate using these methods alone, some
approximations reflect their effects [31, 24].

Direct calculations: They are used for lighting directly from light sources.
The sun, daylight apertures (which include both the sun and the sky), and
luminaires are some examples of these sources. The phrase is primarily used in
the text to link models that involve the design and calculation of luminaires.
They entail particular physical simplifications and formulations. They cover
the majority of ordinary circumstances and are frequently defined by national
standards. The lumen approach is one prominent illustration.

View-dependent algorithms: Algorithms like raytracing, are categorized based
on the angle from which the model computes rays in a scene. Rays are traced
either forward from the light source, backward from the observer’s eyes, or
both ways [33]. It is an image-bound approach as a result. It offers a method
for calculating light phenomena resulting from direct illumination, specular
surfaces, and reflections. When computing several diffuse reflections or
indirect lighting effects, there are, nevertheless, some restrictions.

Scene-dependant algorithms: Algorithms like radiosity were adopted from heat
transfer methods in order to simulate illumination, scene-specific procedures
[34]. They benefit from a scene-based approach. Surface elements can be used
to categorize the scene. Each surface’s radiometric values are calculated
without regard to the view. Its inability to effectively handle specular
reflections is one of its flaws. Surface meshes were used to introduce model
refinements. Umbras and penumbras can be calculated in this way [35]. The
method is mainly utilized for lighting calculations rather than image
production because the calculation methods are more complicated. Innovations
are being made to lighten the load on hardware and increase its effectiveness
[36].

Calculation tools: Statistical sampling is necessary to collect values within
an acceptable time frame for implementing these algorithms on current
computing systems. There are deterministic approaches to conventional
numerical techniques. However, the most popular methods are called Monte Carlo
methods. They are well-liked because the methodology presupposes that the
sample’s predicted value is the actual value for that sample. Once estimates
have been located, the problem can be solved by averaging the estimates. This
can produce precise findings, but the algorithms need to run long enough to
collect many samples. Correctors must be included since some areas can be left
with no values (referred to as “noise”). Although they have accuracy
limitations, Monte Carlo approaches are sometimes the best option for solving
some physical issues. A summary on lighting simulation algorithms is given in
Table III.

TABLE III: Lighting simulation algorithms currently available.

Algorithm | Value
---|---
View-dependent | Forward ray tracing
Backward ray tracing
Bi-directional ray tracing
Scene-dependent | Radiosity
Photon map
Integrative approaches
Multi-pass approaches
Direct Calculation | For artificial lighting follows national standards
Calculation Aids | Deterministic methods (Classical approaches)
Statistical sampling methods (Monte Carlo)

##### Lighting Simulation tools

This review includes 65 specialized research publications that apply
simulation to building lighting. The purpose of the survey, which focused on
learning about the simulation technologies used for lighting simulations, was
to thoroughly examine these 65 publications. According to the study, the
researchers employed both single tools and, in some cases, the combination of
two simulation tools (Integrated tools) to simulate illumination. MATLAB
(Matrix Laboratory), MATLAB-GUI (Graphical User Interface), EnergyPlus, Design
of Experiments (DOE), JAVA, Laboratory Virtual Instrument Engineering
Workbench (LabVIEW), Daylight Simulation (DaySIM), C Language Integrated
Production System (CLIPS), and Building Optimisation are the singly used
simulation tools (BuildOpt). In addition, EnergyPlus/Radiance,
EnergyPlus/MATLAB/LABVIEW, MATLAB/Transient System Simulation (TRNSYS), and
TRNSYS/Integrated Simulation Environment Language (INSEL) are integrated
simulation tools utilized for the simulation. Figure 2 depicts the information
retrieval for the frequency of simulation tools. It is percentage
representation of the combined data highlights the prevalence of the
simulation techniques examined in the literature.

##### Simple simulation tools

Twenty (30.8%) of 65 publications used MATLAB to simulate the prototypes. A
fourth-generation, multi-paradigm programming language for numerical computing
is called MATLAB.

The power needed by environmental parameters to maintain a comfortable indoor
environment with the control of associated actuators was calculated by Shaikh
et al. [37] using a fuzzy controller. The authors asserted a 31.6% energy
efficiency with an 8.1% increase in comfort score. By applying the enhanced
version of their earlier work, the optimization toolbox, Shaikh et al. [38]
claimed to save 32.69% of energy. They used the Multi-Objective Optimisation
Genetic Algorithm (MOGA) algorithm to create the Pareto front trade-off
solution set.

To estimate the system’s output signals that use appropriate fuzzy sets
represented by a rule set database and membership function, Rodriguez et al.
[39] employed the MATLAB fuzzy inference algorithm. The findings demonstrated
that the suggested system was able to maintain the required visual comfort
levels using the available resources while at the same time using less
artificial light, which reduced power consumption. Pennacchio and Wilkins [40]
employed the MATLAB closest neighbor approach for computational measurement.
Images were cropped and enlarged to $256\times 256$ to save the central square
region, and a 2D Fourier amplitude spectrum was produced for each image.

In a different study, Shaikh et al. [41] used fuzzy controllers to compute the
power requirement for regulating the appropriate subsystem actuator to
maintain the desired comfort. By enhancing the building intelligence, the
proposed Genetic Algorithm (GA) can balance the overall power consumption and
the user’s convenience. As a result, far less power is now required. The
computation of the power consumption for the artificial illumination control
system was also created by Shaikh et al. [42] utilizing the MATLAB fuzzy
inference system (FIS) controller. The implications of various membership
functions have also been used to validate the inference models for each agent.
Further described by Shaikh et al. [38] was a fuzzy inference method for
controlling actuator systems for visual comfort in addition to other comfort
parameters and power consumption. The Mamdani implication method was used with
approximate rule-based reasoning and the widely utilized centroid defuzzified
way. For the actuator to operate and to maintain the desired level of indoor
illumination comfort, required power and available power were synchronized.
According to the modeling results, the necessary power has fallen linearly.

The fuzzy control system was introduced by Kristl et al. [43] to create a
comfortable environment inside the structure. A secondary PID controller and
the primary fuzzy controller made up the control loop structure of the
illumination level. The fuzzy control system sets the placement of the shading
device and rendering parameters for indoor and outdoor environments. Shaikh et
al. [44] created the fuzzy-based model with a membership function and employed
the user-selected set points as inputs. At the same time, the fuzzy controller
used external environmental factors. The model was subjected to the
proportional control rule base to provide a discrete output. The retrieved
fuzzy control models include models for both comfort and power usage.

The fuzzy control system architecture (FCSA) was also presented by Shaikh et
al. [42] for resolving conflicts between energy usage and occupant comfort.
The control for visual comfort and other comfort factors has been designed
using FIS.

The Multi-Island Genetic Algorithm (MIGA) was devised by Ali and Kim [45] to
maximize occupant comfort while minimizing power usage. The error difference
between the user’s input parameters and the actual environment served as the
fuzzy logic’s input. By reducing the error difference with MIGA/GA, the trade-
off between comfort index and energy saving was achieved. Wang et al.’s [46]
proposal used model-based information fusion and the Ordered Weighted
Averaging (OWA) aggregation method to control the energy and comfort of indoor
building environments. Fuzzy controls and the power necessary to maintain the
highest level of comfort were used.

This method resulted in a 3% increase in overall comfort and a 9% reduction in
energy use. Wang and Wang [47] employed a Particle Swarm Optimization (PSO)
algorithm to achieve comfort and energy efficiency in a building environment
simultaneously. PSO is a computational technique that iteratively optimizes
issues. Utilizing the suggested local illumination controller agent can save
about 31% energy. In a different work, Z. Wang et al. [48] used PSO to improve
the entire system’s intelligence when combined with the building and microgrid
systems. Residents have access to a GUI-based portal where they can input
their preferences and track the outcomes.

According to the measured indoor illuminance, Lah et al.’s fuzzy controller
[49] was presented for the placement and decision-making of the roller blind
to keep the illumination within the chamber at the desired level. Depending on
the sky circumstances, maintaining the appropriate interior illumination
(between 750 and 1500 lx) with roller blind movement results in an interior
luminous efficacy ranging from 2 to 10 lm/W. The only exceptions are in the
mornings and evenings when the sun shines brightly, and the roller blind is
open 90% or more. Rutishauser and Joller proposed the fuzzy rule base, and
Douglas [50] evaluated system performance. The suggested fuzzy online system
used a real-time unsupervised learning technique to build a fuzzy rule base
from extremely sparse input in a non-stationary environment. There are two
types of rules: static and dynamic. Static rules set the system’s fixed bounds
but are active.

For changeable transparent aspects of the building envelope, Lah et al.’s [51]
proposal of fuzzy rule-based elements was made once more. In their pre-
simulation of the louver angle, Park, Augenbroe, and Messadi [52] used
RADIANCE and one of MATLAB’s constrained nonlinear optimization algorithms to
“identify a minimum of a constrained nonlinear multivariable function
(FMINCON).” To create a wisely calibrated fuzzy logic controller (FLC) to
control HVAC systems in terms of energy performance and indoor comfort
requirements, Alcala et al. [53] considered using a genetic algorithm. To
create a unique adaption system based on user preferences for blind position
learning, Guillemin and Molteni [54] used a genetic algorithm. The components
that made up the proposed adaptation process were a pre-processing wish
module, a GA engine module, and a sensitivity filter.

Figure 2: Lighting simulation tools publications (65).

##### Integrated simulation tools

Many researchers have embraced integrated simulation technologies to simulate
their prototypes. Several of these crucial tools are covered below:

EnergyPlus and Radiance: Four out of 65 publications (6.2%) describe the use
of the EnergyPlus and Radiance software for four cities with various
climates—Montreal, Canada; Boulder, USA; Miami, USA; and Santiago,
Chile—Bustamante et al. [55] examined two external and moveable CFSs. To
ensure visual comfort and reduce overall energy usage, the maximum allowed
incident irradiance has to be determined. To do this, mkSchedule was used to
create a timetable for the luminaires’ power fraction and CFS position [56].
EnergyPlus [57] was used for the energy simulations, and Radiance [58] was
used for the illumination simulations. Vera et al. optimized the fixed
exterior complex fenestration systems (CFS) of four separate offices—Montreal
(Canada), Boulder (USA), Miami (USA), and Santiago (Chile)—located in various
climatic regions. The total energy consumption and two visual comfort
criteria—annual sunlight exposure (ASE) and spatial daylight autonomy
(SDA)—were pursued through an optimization problem. The GenOpt engine was
paired with the hybrid PSOHook Jeeves (HJ) algorithm, mkSchedule, Radiance,
and EnergyPlus to perform integrated lighting and temperature calculations.

Vera et al. [56] presented the mkSchedule, which includes the three-phase
method and uses scripts built in the Lua programming language for the flexible
and prompt production of a schedule for the CFS position and luminaries’ power
fraction. Three case studies from a San Francisco (CA, USA) workplace were
used to apply the established technique to show its adaptability and time-
saving capabilities. For artificial illumination, on/off and dimmable control
schemes were investigated. The findings demonstrated that the CFS’s position
and luminaries’ power fractions responded appropriately to the control
algorithms of the CFS and luminaries. The schedules were successfully
constructed by mkSchedule and then imported to EnergyPlus and Radiance to
acquire the annual solar heat gains and illuminance. Huang and Niu used
integrated simulation platforms to research a numerically based silica aerogel
glazing system [59]. Investigated were the energy consumption and visual
comfort of glazing systems in commercial building envelopes. EnergyPlus was
used to test the glazing system’s energy efficiency. In contrast, the Radiance
tool measured the visual comfort index.

EnergyPlus and MATLAB: Two out of 65 publications (3.1%) combined the usage of
MATLAB and EnergyPlus. A non-dominated sorting genetic algorithm (NSGA-II)
code was built using MATLAB for the simulation-based optimization process by
Delgarm et al. [60] utilizing EnergyPlus software to conduct the simulation.
An Energy Management Control (EMC) system that incorporates predictive dynamic
building models, day-ahead forecasts of load, energy price, and occupancy
needs, and supports micro-zoning-based active occupant-driven control was
proposed by Ji, Lu, and Song [61]. The EMC functions as a high-level strategy
planner that enables the building automation system (BAS) to utilize all
available information for optimal planning and operation. It is based on a
dual-loop control design and implemented in the outer loop.

MATLAB and LabVIEW: One publication out of 65 (1.5%) combined the usage of
LabVIEW and MATLAB technologies. Xiong and Tzempelikos [62] combined MATLAB
and LabVIEW for their control platform. Since data measurements and controls
were handled through LabVIEW and the daylighting-glare model was created in
MATLAB, the two simulation platforms each offer unique performance
characteristics. The LabVIEW tool’s MathScript RT Module provided integration
inputs from the MATLAB model. Consequently, the models are run in real-time,
and the outcomes are collected using both tools.

MATLAB, EenergyPlus, Building Controls Virtual Test Bed (BCVTB): In one of 65
publications (1.5%), the proposed system was simulated using MATLAB,
EenergyPlus, and BCVTB. The energy and visual comfort of lighting and
daylighting management systems were analyzed by Shen, Hu, and Patel [63].
MATLAB, EnergyPlus, and BCVTB were used to model the proposed approach. To
conduct simulations for building energy management systems, users of BCVTB can
pair a variety of simulation programs and control algorithms. EnergyPlus
provided data on energy and daylight generation and the building model. In
contrast, MATLAB provided the ability to develop blind control algorithms with
dynamic statistical occupancy.

TRNSYS and MATLAB: One out of 65 publications (1.5%) employed the TRNSYS and
MATLAB tools for the illumination simulation. Dounis and Caraiscos [64] have
merged TRNSYS and MATLAB tools. The control system was implemented in MATLAB,
and the model acted as the existing system implemented in TRNSYS. The
simulation results demonstrated that the suggested intelligent control system
successfully balanced energy conservation and occupant comfort.

TRNSYS and INSEL: One out of 65 publications (1.5%) combined the usage of the
TRNSYS and INSEL tools. These tools were frequently used by Santos et al. [65]
to design and develop HVAC systems and construct rooms in the simulation mode.
These tools enable project planning for the ideal indoor lighting design.

DRIVE, WSE and OSGi: The integration of DRIVE (Distributed Responsive
Infrastructure Virtualization Environment), WSE (WebSphere Sensor Events), and
OSGi (Open Services Gateway Initiative) was employed for system simulation in
one out of 65 publications (1.5%). Using a research tool named DRIVE [66], H.
Chen et al. [67] created event processing logic as components and flow
diagrams. Runtime artifacts produced by this software comprise configured
scripts and codes that can be used with WSE and controlled by OSGi platforms.

####  IV-A3 Evaluation

We provide an in-depth discussion of the many simulation tools that various
researchers have used to simulate their modules and prototypes. Based on this
analysis, comprehensive data that includes all of the simulation tools used
for artificial lighting in buildings and the many factors that go along with
them have been summarized in Table VII and VIII. The primary goal of this
analysis is to identify the simulation tool that is most frequently used to
simulate illumination in building envelopes.

##### Accuracy validations and model comparisons

Comparing simulation models is a logical conclusion when there are numerous of
them available. Comparisons based on accurately simulating the built
environment and comparisons made in controlled laboratory environments make up
the two main categories of evaluations discussed in the literature. There are
benefits and drawbacks to each strategy. Due to inherent methodological
restrictions, as explained in the last section, comparing findings acquired
using different methods is challenging.

Validations with built realities: Most comparisons in this category look at
lighting simulation tools architects can use to create and assess designs. Roy
provided a classical, thorough analysis of the programs accessible at the
time. The “perfect rendering package” for usage in architecture was compared
with functionality, usability, and application to design settings [68].
Christakou and Amorim carried out a similar study in 2005 for the Brazilian
context [69].

Four simulation programs were also evaluated by Ashmore and Richens [70],
ADELINE- Radiance and three programs now unsupported: Lightscape, RadioRay,
and Microstation 7). As a standard, two artificially overcast skies over a
scale model were used. Using reflectances from the model, programs had to
simulate measurements of a small room next to a huge courtyard. The results
from simulation programs were decent overall, but poor near the window. The
size model and two artificial sky that these writers chose were acknowledged
as mistake sources.

Ng discovered that selecting the appropriate reflectances impacts simulation
outcomes. Heavily occluded locations in Hong Kong were examined using Desktop
Radiance and Lightscape. Measurements inside test environments were compared
to computer simulations. The sky model was cloudy and created using local
meteorological data. There were noted large relative inaccuracies [71].
Mardaljevic attributed these mistakes to the substantial role of the
reflectance estimation of nearby surfaces [72]. A model of a small Singapore
museum in cloudy weather was created by Ng et al. in 2001. Then, modeling
nearly matched daylight readings. According to Li et al., furniture
reflectances in a Hong Kong classroom also impacted accuracy findings [73].

The aforementioned illustrations of model validations show how they were
utilized in various scenarios faced by building designers and the kinds of
data they use. Additionally, they emphasize the need for models to adequately
depict reality while simultaneously accommodating user expectations for
program manageability.

Validations in controlled lab environments: In most cases, comparisons under
controlled conditions were conducted to show model accuracy in predicting
illumination data.

Under IEA-SHC Task 21, Fontoynont et al. conducted the first set of
standardized assessments for simulation tools. Testing of the tools was done
in an atrium with various surface finishes. Scale models served as benchmarks.
It was discovered that input data quality had an impact. Accuracy was affected
by careful modification of simulation parameters [74]. At the time, time-
static models were found to exist. Maamari and Fontoynont carried on Task 21’s
work by creating several test scenarios [75]. The CIE test scenarios would be
based on these. Two light simulation tools—one for general rendering and the
other for designing electric lighting—were contrasted to show how they differ
[76].

To create a dynamic simulation method, Reinhart and Walkenhorst did Radiance
validation of a considerable number of sky situations [77]. A specific full-
scale model with external blinds was the subject of this examination. Reinhart
tested standard equipment like shelves and curved curtains to validate
Radiance and 3D Studio (mental ray photon map) [78].

Developers can test algorithms under less unpredictable scenarios by
validating models under controlled circumstances. They should be carried out
in the preliminary stage before being incorporated into a complete simulation
model.

Standard comparison techniques: By comparing lighting simulation tools, it may
appear straightforward to rank or select among them. In actuality, a variety
of factors influence the result. According to Maamari and Fontoynont,
replicating built realities or scale models introduces uncertainty [75]. These
consist of measurements, geometry, and the material’s photometric qualities
(such as lamp depreciation and surface reflectivity). Most areas worldwide do
not have access to hourly data of good quality for daylighting simulations
[79]. On the other hand, while evaluating computer programs in a pure
laboratory setting, routine construction activity is disregarded [80].

There have been suggestions to standardize comparison techniques to avoid the
challenges mentioned above. The Cornell box was one of the first methods used
to test the early rendering algorithms [81]. The method’s primary use for
visual comparison is a drawback [27]. BRE proposed a dataset focused mainly on
daylight performance in particular circumstances [82]. A set of tests with
only artificial lighting were proposed by CIBSE [83]. Based on work by [75,
76], the CIE recommended a set of test cases to be used as benchmarks [84].
These test cases cover as many variants as possible while keeping everything
straightforward and repeatable. Both artificial and natural light and various
kinds of surfaces are present in the cases.

The validation techniques discussed, together with their drawbacks and
accuracy, are summarized in Table IV.

TABLE IV: Lighting simulation validation techniques Method |  Advantages |  Disadvantages |  Accuracy
---|---|---|---
Validation with built realities |  Tests on buildings, live measurement |  Hard to replicate somewhere else |  5–20%
Hard to replicate sky conditions
Hard to replicate reflections
Laboratory tests |  Controlled settings |  Detaches from model usability |  2–7%
Element testing |  Only possible with specialized tools and data

##### Methods for Solving Various Lighting Simulation scenarios

Certain issues can be resolved differently depending on the assumptions used
by current illumination simulation techniques about steady-state and
geometrical distribution. They include modeling of new, untested components,
numerous performance simulations, and time-dependent simulations (to analyze
user behavior and responsive elements) (prototyping). To duplicate effects
over a period of time, time-span calculations need to be broken up into very
small chunks. Consequently, a different strategy is needed.

Dynamic vs. static: Simulation algorithms and tools presuppose a steady-state
light distribution. For a lot of situations, this assumption may be accurate.
Problems involving daylight must take into account light’s dynamic behavior.
Annual irradiation studies, occupant reactions to variations in light quality,
and dynamic shadow components are a few examples.

Typically, the time period is broken up into manageable data chunks. On these
segments, the model runs simulations and compiles the findings. This is done
by obtaining information about sun radiation from local weather databases
(usually hourly averages). As permitted by the weather file, interpolations
anticipating sun position and radiation must be done for fewer time increments
[85]. Radmap and GenCumulativeSky are examples of tools that produce
cumulative sun radiation maps using such ideas [86].

Many research has been conducted to simulate blind deployment that user and
environmental conditions trigger. Daysim tool for annual daylight availability
and influence of automated lighting controls [77], daylight responsive dimmer
for electric lighting [87], automated blind control for user comfort [88, 89,
90], energy consumption triggered by luminaire dimming and occupancy sensors
[91], and considerations of multiple variables are a few examples of studies
looking at activation [92].

A building’s overall energy consumption will be impacted by occupant
interactions and activities, but few models take these activities’ effects on
each parameter into account [93]. Interaction experiments on lighting
simulation are concentrated on light switching and shade activation. To
forecast light switching, window opening, and blind shutting associated to the
time of year, temperature, and number of occupants, algorithms based on real
office studies were employed [94, 95, 96, 97].

Designing new components: Even though predictive rendering adheres to physical
rules, it cannot replicate every physically feasible aspect. This is
especially true for components like light concentrators that operate on
principles not included in the geometrical optical model. Additionally, it can
be challenging to characterize some components due to the calculating
techniques utilized by algorithms. For instance, modeling complicated curved
and specular surfaces using the Radiance model’s original formulation is
difficult or impossible due to backward raytracing and geometrical issues
[28]. Although large-diameter light pipes are still handled successfully [98],
thin light pipes and optic fiber are best described using the photon map
approach [99]. It makes sense to assume that, as of this writing, additional
initiatives are being taken to enhance the Radiance model and offer a
trustworthy approach to emulating these components.

Specific components need to be tested in the lab using goniophotometers for
the existing geometrical optical model to describe visual qualities
adequately. As in translucent panels [100] and many intricate fenestration
systems, BRDF and BTDF functions are determined (CFS).

Element prototyping: Several changes must be made to critical factors to find
the ideal or nearly ideal element sizes. Many researchers had to do
simulations that involved trial and error to accomplish this (e.g., [101, 102,
103, 104]. Optimization software is the ideal method to utilize when there are
too many test parameters. However, the type of optimization algorithm,
connectivity with optimizer software, and input received by the lighting
simulation program must be taken into account. Facades have been the subject
of optimization studies because of their intricacy and impact on lighting and
energy [105]. They include “random” window placement for low energy
consumption, intelligent facade optimization [106], lighting controllers
[107], visual discomfort and solar penetration, external horizontal shading
devices and their angles, and solar penetration [108].

####  IV-A4 Input in lighting simulation

Input will be outlined as the method by which data are extracted from reality
and abstracted for processing by a simulation model. The desired output
quality and model behavior might be affected by input techniques. Text files,
command-line interfaces, translators from computer-aided drawing (CAD) tools,
and graphical user interfaces (GUI) with or without their CAD system are some
of the often used techniques. Each input technique has benefits and drawbacks.
Despite using low computer resources, text files can be challenging to grasp.
GUIs require careful development because they range in complexity. Software
applications with their CAD systems can use DXF files from various CAD
systems. These GUI variations are “plug-ins” for well-known computer sketching
programs [109]. While other techniques, such as GUI input, tend to assume
variables for the user, input methods like text files and command prompt allow
direct manipulation of model variables. Results may be impacted by input
methods’ abstraction or simplification of reality. For instance, curves are
typically depicted using a lot of polygons. Reflectance must be measured or
weighted for exterior scene elements. Sky models are used to describe how the
length of the day changes, although there is a chance for a mistake,
especially when estimating the brightness of a sunny sky [80].

High dynamic range (HDR) photos can be utilized for scene analysis thanks to
advances in digital photography. Despite the need for specialized hardware and
software, the generated photographs enable a pixel-by-pixel analysis of many
illumination parameters [110]. Modern simulation models make it relatively
simple to specify typical lighting, glazing, and fenestration systems.
Programs like WIN [30] or WIS [111] can create customized glazing combinations
based on manufacturer databases for lighting and full-building simulations.
The accurate definition of optical characteristics, such as bidirectional
transmittance distribution functions (BTDF), is necessary for other system
types, such as complex fenestration systems (CFS) [100]. These are identified
using goniophotometer testing in a lab. Translucent panels [100], curved
reflective blinds [112], prismatic films [113], and laser-cut films are
examples of elements that fit this description. There is virtual
goniophotometer software; however, it needs experimental validation [114].

####  IV-A5 Output in lighting simulation

Humans will interpret the output as the way the model’s results are presented.
The output can be separated into two main categories: graphical representation
and text-only (quantitative work) (qualitative output). Each lighting
simulation program offers a method for setting up output, such as choosing a
format for text or graphics. Photometric data from points on a previously
established computation grid are typically contained in text-only files.
Results can be exported to word processors or spreadsheets. These external
programs modify data or display it (for example, in statistical analysis)
(e.g., through diagrams). Most often, the data contained in text-only files is
strictly numerical and presented in tabular format. Manipulating the
simulation parameters can impact the accuracy of the numerical findings. There
are various uses for quality output. A few instances include user preferences
for illumination across a variety of ages [115], evaluation of user
preferences for luminaire placement in offices [116], and simulation of
complex visual stimuli for patients in the medical field [117]. Interactive
images, rendering images, and images with data interpretation are three
categories of quality output.

The output data provided by current techniques are diverse, but findings
require a high level of knowledge to interpret [118]. There are only so many
tools for lighting simulation that give result interpretation or analysis
[17]. Interpretation of the output should be tailored to the user type. These
come in two primary categories: One category consists of architects who don’t
have a background in lighting, but research how natural and artificial light
affect their projects. The second group includes lighting/daylighting
researchers and physicists who favor validation and verification but are part-
time designers [119].

####  IV-A6 Applications of lighting simulation in architecture design process

In this section we study the application of lighting simulation in
architecture design process in order to provide new possibilities to improve
the process of designing lighting system and also new features to maximize the
quality of the users’ experience.

##### Early stages of design

Most computer tools available today are not suitable for use in the early
phases of architectural design. They require for a level of precision and
detail that wasn’t recognized at the time [120, 121]. Few made analyses or
solution suggestions [17]. They are single-output based [122] and targeted
towards expert usage [123]. Taking too long to compute is a drawback [121].
During the initial design stages, architects must interactively compare how
their goals turned out.

Lightsolve [124, 125] was suggested for design exploration. It provides
direction on how particular cutting-edge fenestration solutions impact
specific scenarios. Their goal is to implement a goal-based design approach
[126]. Sheng et al. presented a virtual heliodon as a “sketcher” that adjusts
the intensity with altitude and acts like a natural sun [127]. Realistic
illuminance forecasts were made in this manner. A suggested setting for
immersive VR is called SPOT [128]. Users may see the impact of altered
building geometry on sunlight. The facade combinations recommended by
NewFacades [129] show how they affect visual comfort and overall building
energy. Online overviews of how various lighting configurations seem under
different lighting circumstances are offered by the Virtual Lighting Simulator
and Daylight Variations Book [130].

Some models propose volumetric solutions that guarantee sun access to nearby
structures. They may be applied to urban planning and design’s first stages.
Solar masks and programs like Sustarc [131], Helios [132], and SunScapes [133]
allow users to compute the “solar rights” and “solar envelopes” concepts
[134]. These techniques were first created for very bright climes, where a
balance between summertime shade and wintertime access to solar radiation must
be achieved.

Models may provide architectural concepts a tangible validity. A range of
maximum building heights for high rises is suggested by the tool by Ng,
guaranteeing daylight access for lower levels [135]. By figuring out the
vertical daylight factor, it does this. Capeluto et al. advocate angled urban
layouts that also offer high density while ensuring sunlight access to all
levels based on their solar envelopes tool [136, 137]. According to shading
[138], the dimensions of urban squares or atriums should be chosen to maximize
solar insolation and the placement of urban vegetation.

##### Design development(DD) stage

After settling on fundamental concerns like massing, building placement,
window size, and orientation, simulation models for lighting can be applied.
The following thoroughly explains the factors and procedures for effective
daylight simulation [30]. Tools created specifically for this purpose make it
easier to explore artificial lighting configurations. Solar shading studies
may be completed quickly with CAD tools like AutoCAD and SketchUp. SketchUp
plug-ins, such as SunTools [139] offer more accurate daylight and electric
lighting studies within CAD applications. A building’s unique components
include glazing and redirection systems [140], shades and blinds [141], and
automated shading and lighting control modes to operate to their full
potential, experts are required [142, 90].

##### Adaptation to building codes

Lighting simulation can be used to validate code compliance once a building
project is finished and submitted for review by relevant authorities. The
simulation will become crucial as performance codes replace prescriptive ones
[122]. Models of solar rights offer a framework for approving volume concepts.
Steward and Donn presented a technique to determine whether or not extensive
simulations are required for compliance with interior daylight guidelines
[143]. Sky view factor and view solid angles can be used to analyze window
access to direct sun radiation [104, 144]. Compared to the traditional
60-degree obstruction restrictions, these methods successfully determine
compliance.

##### Building commissioning and functioning

Programs that simulate illumination are rarely used to simulate building
operations. As previously stated, it is challenging to portray human
involvement and occupancy using various control components like blinds,
moveable shades, sensors, and switches [145, 146, 147]. Doulos et al. provide
a study and comparison of the most recent modeling methods for lighting
control systems and daylight sensors [148]. They discuss current switching
patterns and their impact on energy use and evaluate various simulation
techniques. Daysim, one of these tools, analyzes different tactics employing
control aspects. Ehrlich et al. suggested radiation modeling of photosensors
to forecast their performance and account for locational impacts on walls or
ceilings [57]. Degelman proposed utilizing Monte Carlo probability to simulate
motion sensors and anticipate light switching [149]. In whole-building
simulation models that include both artificial and natural illumination,
occupancy, switches, and sensors can be represented in varying degrees of
abstraction.

##### Architecture specific lighting simulation

Rendering an architecture scene is the most important part of the lighting
simulation process since the visual presentation of the proposed environment
will be rated by users and will affect their experience in the study. This
pressures the render engines to be optimized as possible with visual quality
configurable during the experience. The rendering takes the majority of update
time since it will update by each change in real time so the choice of the
most efficient tools to optimize the process.

For the creation of such experience, game engines are sophisticated,
multifaceted tools. They provide a setting for effective development,
occasionally even without scripting skills. The design of the user interface,
rendering, physics, audio, animation, and other aspects of the game
development process should all be covered by the game engine.

The game engines are in rivalry with one another and also comparing them is a
complex undertaking because they are complicated tools. Unity has been the
most popular game engine in since 2016. Unity is so simple to use that anyone
can start producing with it; thus, it’s likely to stay this way. The Unreal
Engine, which only has 17% of Unity’s users, is its main rival. So, in this
thesis, I’m contrasting those two engines. The Cry Engine is also quite
effective and used. However, it is a fairly sophisticated program that is
difficult for amateurs to use; therefore, studios are the primary users.

Unity 3D Engine: The Unity game engine, developed by Unity Technologies
Corporation, is currently at LTS version 2022.3.18f1 as of January 24, 2024.
Unity holds the distinction of being the most widely adopted game engine in
contemporary usage, a position it has consistently maintained. Its initial
version was initially designed exclusively for OS X development, debuting at
an Apple conference in 2005. Over time, Unity has evolved and expanded its
compatibility, becoming accessible on 27 distinct platforms, including
extensive support for Virtual Reality (VR).

Unity’s primary strength lies in its remarkable user-friendliness, which
continues to be a pivotal feature of the engine. The engine excels in
accommodating rapid development, particularly within the realm of mobile
platforms, as exemplified by the extensive reference [150]. Unity projects and
games tend to be relatively compact in scale, while the export process remains
comparably straightforward. Its component-based design philosophy simplifies
the comprehension of its architecture. For scripting, Unity employs C#, an
agile and efficient programming language that expedites the development
process.

One of Unity’s distinguishing attributes is its substantial user base,
supplemented by vibrant online forums brimming with insightful solutions that
aid in streamlining the debugging process. Since its initial release, Unity
has consistently faced challenges in the domain of graphics compared to rival
game engines, notably Unreal and Cry Engine. Although Unity introduced
advanced features like the Unity Standard shader, physically based shading,
and image-based lighting techniques in its Unity 5 version, it still grapples
with certain shortcomings in comparison.

Regarding its lighting setup, Unity adopts a straightforward approach. A
solitary directional light source simulates the sun, casting soft shadows and
imparting a subtle yellowish tint to the scene. The sky plays a pivotal role
in illuminating the landscape, with Unity employing a spherical High Dynamic
Range (HDR) representation of the sky as ambient light. An intriguing feature
of Unity’s lighting is its capacity to pre-calculate real-time global
illumination, accomplished through the storage of a light map detailing the
direction and intensity of indirect light. The dynamic adjustment of light
attributes, such as brightness, color, position, or direction, does not
necessitate the time-consuming process of rebaking the lighting. This real-
time global illumination solution in Unity is aptly named “Enlighten” and is
developed by the company Geometrics, offering significant advantages in
lighting quality and efficiency [151].

Unreal Engine: Unreal Engine, a software framework developed by Epic Games,
has gained prominence for its versatile capabilities and is currently at
version 5.3 as of as of January 24, 2024. Notably, it excels in facilitating
the creation of immersive and realistic visual content, particularly in the
realms of lifelike imagery, intricate vegetation, and complex terrain
modeling. Consequently, Unreal Engine is particularly well-suited for handling
larger and more intricate projects that demand advanced graphical fidelity and
computational resources.

While Unreal Engine provides support for deployment on iOS and Android
platforms, it may not be the most suitable choice for the development of
compact mobile games owing to its inherent complexity and resource-intensive
nature. To streamline the process of game development, Unreal Engine employs
the Blueprint system, a visual scripting paradigm that enables developers to
construct logic through the arrangement of interconnected blocks within
graphical representations known as blueprints. This approach eschews
traditional script-based programming, offering an accessible and intuitive
means of designing intricate logic.

One of Unreal Engine’s standout features is its accessibility to the C++
source code, affording developers extensive control and customization
opportunities over the entire system, harnessing the power of the C++
programming language. However, it is noteworthy that mastering the
foundational aspects of Unreal Engine can be challenging due to its inherent
complexity. Additionally, Unreal Engine boasts a robust and swift post-
processing rendering technology that encompasses a rich array of features.

Furthermore, Unreal Engine provides a custom material editor, enabling users
to craft intricate visual materials through a node-based system that
simplifies shader programming using a visual interface. The engine’s lighting
capabilities are highly commendable, offering visually stunning lighting that
can be either pre-rendered or delivered in real-time. Unreal Engine’s
comprehensive toolset extends to visual debugging and optimization, offering
valuable resources for enhancing project performance.

Nonetheless, it is worth noting that the Unreal Engine community is relatively
smaller compared to some of its counterparts like Unity, and there may be gaps
in available documentation and support.

Unreal Engine’s enduring reputation in the field primarily hinges on its
visual capabilities, which allow users to create intricate materials through a
node-based shader programming approach with a visual interface. The engine
excels in rendering visually striking and dynamic lighting, offering the
flexibility to choose between baked or real-time lighting solutions. Notably,
Unreal Engine distinguishes itself through its extensive suite of built-in
post-processing effects, which significantly contribute to the overall visual
quality.

In the context of architectural visualization, Unreal Engine frequently serves
as the preferred platform for generating real-time walkthroughs, delivering
exceptional fidelity and immersion in architectural representations.

The lighting arrangement is straightforward. One directed light, the sun,
which creates shadows in real-time, and one ambient light are used to
illuminate the area and create an HDR sky. Because we can configure the baking
quality in the editor, the setup and baking are quicker than in Unity. Still,
each time we change, we have to recreate the lighting. Unity doesn’t need to
regenerate lighting after making light modifications. This method is also
offered for Unreal, but it costs money.

The Unreal engine implements an optimization of shadows called Cascade
Shadows. According to the camera distance, it creates different shadow maps so
that objects near the camera have sharper shadows than those farther away.
Additionally, there is a beautiful blend between the shadow maps, making the
map change practically imperceptible. This method is effective, especially for
the more significant scenes [152].

Comparison: Table V gives a brief comparison with a square table that lists
the most routine duties engines have, together with a summary of the solutions
each engine provides. Both engines are strong and offer a complete set of
resources for game development. Which tool the developer prefers is usually a
matter of personal preference. There are several places, though, where one of
the engines appears to offer a better or more comprehensive solution:

  * •

Unity supports many more target platforms, but Unreal also has the most
important of them.

  * •

Unreal’s material editor allows the developer to design new shaders while
considering materials. Compared to the Unity Standard Shader, this strategy is
significantly more potent.

  * •

Unity supports pre-baked real-time global illumination, but Unreal doesn’t
provide this feature.

  * •

Cascade particle system in Unreal is considerably better.

  * •

Unreal has excellent built-in post-processing effects. Similar effects are
only possible with third-party plugins in Unity.

Considering this comparison, Unreal seems to be a more efficient simulation
tool for the purpose of this review [153, 154].

TABLE V: Comparison of Unity Game Engine and Unreal Engine.

|  | Unity | Unreal
---|---|---|---
Platform | Desktop | Windows, macOS, Linux, WebGL | Windows, macOS, Linux, HTML 5
Mobile |  | Windows phone, iOS,
---
Android, Blackberry 10, Tizen
iOS, Android
Consoles | Xbox, Playstation, Nintendo | Xbox, Playstation, Nintendo
VR |  | SteamVR/HTC Vive,
---
Oculus Rift, Google VR/Daydream,
Samsung Gear VR, Hololens, Playstation VR
| SteamVR/HTC Vive, Oculus Rift,
---
Google VR/Daydream,
Samsung Gear VR, Playstation VR, OSVR
Editor | Tools | Unity Editor | Unreal Editor, VR Editor for HTC Vive
Features | Scene view and Game view as two windows | Possess & Eject, Simulate, Content browser
Scripting language |  | C#, Unity Script,
---
Java Script, Python
| C++, Blueprint,
---
Visual Scripting
Engine Code | Closed | C++ Source code available via Github
Rendering | Shading technique | Deferred Shading | Deffered Shading and Forward Shading for VR
Materials |  | Physically based, Standard Shader,
---
Tesselation Shader, Mobile Shader
| Physically based, Blueprint, Material editor,
---
Tesselation, Layred materials, Material instances,
Lit translucency, Subsurface shading model
Lighting |  | Directional light, Point light,
---
Spot light, Area light
| Directional light, Point light,
---
Spot light, Sky light,
IES light profiles
Shadows | Realtime, Hard/Soft shadows |  | Real time, Hard/Soft shadows,
---
Cascade shadow, Distance field shadows
Global Illumination |  | Image-based GI, Precomputed realtime GI,
---
Baked GI
Image-based GI, Baked GI
Reflection/Refraction | Reflection probes |  | Reflection probes, Screen space,
---
Reflections, Refraction
Particles | Sample curves based particle system | Cascade particle system with GPU particles

TABLE VI: Simple simulation tools for artificial lighting technology in
building applications and associated parameters — Part 1 (multiple citations).

Paper # Tool Features Light Source Building Type 20 MATLAB [155, 38, 53, 45,
54, 43, 51, 49, 52, 40, 39, 50, 42, 44, 41, 37, 48, 46, 47, 156] Energy
efficiency Natural and Artificial Commercial Industrial Office Residential
Illuminance Specific utility function User comfort parameters User
expectations and demands User preference 8 MATLAB/Simulink [157, 158, 159,
160, 161, 162, 163, 164] Energy efficiency Natural and Artificial Commercial
Industrial Office Residential Illuminance Occupant patterns User behavior User
occupancy and preference request Visual comfort 5 EenergyPlus [165, 166, 167,
168, 169] Energy efficiency Natural Commercial Industrial Office Illuminance
Occupants’ attitude/satisfaction level User-specified time steps Visual,
thermal and cooling comfort 4 MATLAB/GUI [170, 171, 172, 173] Energy
efficiency Natural and Artificial Commercial Industrial Office Residential
Illuminance Occupants preference Visual comfort 2 DOE [174, 175] Energy
(Window area) Artificial Office Illuminance User defined/Input parameters 2
JAVA [176, 177] Energy efficiency NA Commercial Office Residential Illuminance
User satisfaction / preference Visual comfort 2 Daysim [178, 179] Energy and
ligthing Natural and Daylight Office Residential

TABLE VII: Simple simulation tools for artificial lighting technology in
building applications and associated parameters — Part 2 (single citation).

Paper # Tool Features Light Source Building Type 1 CLIPS [180] Energy
efficiency NA Commercial Industrial Office Residential Illuminance User inputs
/ Requirements 1 BuildOpt [181] Energy efficiency NA Office Shading User-
specified limits Visual comfort Windows dimension 1 LabVIEW [182] Energy
efficiency NA Office Illuminance 1 LightWise [183] Consumer preference NA
Office Energy efficiency Illuminance 1 Prototype Software Tool [184] Energy
efficiency Artificial Commercial Illuminance User requirements and profile 1
Proteus [185] Energy efficiency Artificial Commercial Office Residential
Visual and other comforts 1 Hemera [186] Visual comfort Natural Office
Residential 1 SEMPER [187] Energy efficiency NA Commercial Office Illuminance
User input set values 1 Agent-Based Model Simulation [188] Energy efficiency
NA Office Illuminance Occupancy sensor camera 1 Integrated Environmental
Solution-Virtual Environment (IES-VE) [189] Visual comfort Natural Office 1
DIALux [190] Energy saving Natural, Artificial, and Daylight Office Luminaire
arrangments Occupants’ comfort

TABLE VIII: Integrated simulation tools for artificial lighting technology in
building applications and associated parameters.

Paper # Tool Features Light Source Building Type 4 EnergyPlus + Radiance [55,
191, 56, 59] Energy and lighting Natural, Artificial, and Daylight Commercial
Office Thermal comfort Visual comfort/discomfort 2 EnergyPlus + MATLAB [60,
61] Applied energy Artificial Office Energy saving in lighting 1 MATLAB +
LabVIEW [62] Glare constraints Natural Office 1 MATLAB + EnergyPlus + BCVTB
[63] Energy efficiency NA Office Visual and other comfort parameters 1 TRNSYS
+ MATLAB [64] Illuminance Artificial Commercial Industrial Office Residential
User preference and dependency 1 TRNSYS + INSEL [65] Energy efficiency
Artificial Office Illuminance 1 DRIVE + WSE + OSGI [67] Energy efficiency NA
Office Illuminance Occupants awareness

###  IV-B RQ2: How can XR technology be used for SBE lighting system modeling?

As underscored in in the background section, extended reality (XR) which is an
all-encompassing term that comprises virtual reality (VR), augmented reality
(VR), and mixed reality (MR), encompasses an assortment of cutting-edge
technologies that merge the physical and digital worlds, providing users with
immersive and interactive experiences. XR can be visualized through portable
devices such as smartphones and tablets, or through head mounted displays
(HMDs) [192].

In this section, we explore each immersive technology in detail, emphasizing
the pertinent research findings for each one, and offering illustrative
examples of their applications in the field of smart lighting design. VR, a
computer-generated environment, offers a simulated world that is fully
immersive, enclosing the user within a headset that obstructs the actual
environment. Through handheld controllers, users can manipulate objects and
interact within the virtual world. AR, on the other hand, superimposes digital
content onto the user’s real-world environment, often facilitated through a
smartphone or tablet device. AR utilizes the device’s camera to capture the
surroundings and integrate digital imagery, videos, or 3D models into the live
stream. MR is an integration of both virtual and augmented reality, allowing
digital objects to blend seamlessly into the real world. MR headsets have
cameras and sensors that track the user’s surroundings, enabling digital
objects to be placed and interact with the real world.

Lighting is a critical element in creating an immersive and realistic XR
experience. Light affects how we perceive objects, spaces, and materials. It
also helps create a sense of depth, texture, and atmosphere. Without proper
lighting, XR experiences can appear flat, lifeless, and unconvincing. In XR
applications, lighting can be used to convey mood, emotion, and tone, helping
to create an emotional connection between the user and the virtual environment
(VE). For instance, in a VR horror game, dimly lit corridors with flickering
lights can create an eerie and ominous atmosphere, increasing the user’s fear
and tension. Similarly, in an AR museum exhibit, soft and warm lighting can
create a calming and inviting ambiance, enhancing the user’s ability to
appreciate the art and artifacts on display. In MR training simulations,
lighting can affect the user’s sense of presence and immersion in the
scenario. For instance, realistic lighting conditions in a virtual
construction site can help trainees better understand the lighting and
visibility challenges that they might face in the real world.

Integrating smart lighting design into XR environments is a promising approach
to enhancing the user’s experience by enabling more precise control over
lighting conditions. Advanced smart lighting systems equipped with sensors and
algorithms can dynamically adjust lighting levels, color temperature, and
directionality in real-time based on the user’s position and movements. This
provides a more immersive and responsive experience, as lighting conditions
can be optimized to create an environment that more accurately simulates real-
world scenarios. Moreover, smart lighting design can enhance the overall
aesthetic of an XR environment by creating stunning visual effects through
creative use of color, brightness, and contrast. Additionally, energy-
efficient lighting systems can help reduce the carbon footprint of XR
environments, contributing to sustainability efforts. By incorporating smart
lighting design into XR environments, designers and developers can create more
engaging and immersive experiences that provide users with a heightened sense
of presence and realism.

In the realm of XR lighting, there are three primary types of lighting used in
XR: ambient lighting, directional lighting, and point lighting. Ambient
lighting is the overall lighting in the environment, and it sets the tone for
the scene. This type of lighting is usually used to create a sense of space
and mood. In XR, ambient lighting can be used to simulate natural lighting
conditions, such as daylight, dawn, dusk, and nighttime. Directional lighting
is used to create shadows and highlights on objects in the environment. This
type of lighting can be used to simulate the sun’s position in the sky, as
well as to create dramatic effects, such as silhouettes. Point lighting is
used to highlight specific objects or areas in the environment. This type of
lighting is often used to draw the user’s attention to a particular object or
feature in the environment.

Beyond these primary lighting methods, two additional techniques, linear
lighting and surface lighting, contribute to crafting realistic 3D graphics in
VE. Linear lighting, also referred to as physically based rendering (PBR),
replicates real-world lighting interactions by considering material properties
such as reflectivity and roughness, yielding more genuine lighting effects.
Meanwhile, surface lighting techniques encompass various approaches, including
masking-shadowing functions, subsurface scattering, and efficient irradiance
environment mapping.

Ambient occlusion is one such surface lighting technique, mimicking soft
shadows from nearby objects and adding depth and realism to XR scenes. Shadow
mapping ensures the precise rendering of object shadows, enhancing spatial
perception by considering light occlusion. High dynamic range (HDR) rendering
broadens the display’s brightness range, enabling the representation of bright
highlights and intricate details in the VE, boosting overall visual fidelity.
Finally, environment mapping reflects the surrounding environment onto object
surfaces, adding to the XR experience’s realism and utility, particularly for
reflective surfaces like mirrors or metallic objects. These techniques
collectively enrich the VE, offering a more immersive and visually appealing
experience.

Researchers are actively exploring XR and global illumination approaches to
expand and enhance options for studying interior lighting situations. Several
studies have been conducted to develop a sample VE that improves and evaluates
lighting and design training. These studies aim to offer a promising solution
for increasing experimentation in a crowded experience test-bed and reducing
the time required for user comprehension and understanding. By utilizing VR
and global illumination techniques, researchers can create immersive
environments that accurately simulate real-world lighting conditions and
enable the evaluation of various lighting scenarios. Such approaches provide a
valuable tool for designers, architects, and other professionals to test and
refine their ideas and concepts, ultimately leading to more efficient and
effective lighting design solutions.

The investigation of XR technologies in collaborative urban design and their
potential to meet people’s lighting needs is a new and emerging field of
research. As XR technologies become more advanced, there is an increasing need
to understand how they can be utilized to create more engaging and immersive
urban environments that meet the diverse needs of communities. Research in
this area aims to explore the use of VR and AR to simulate different lighting
scenarios and provide designers, planners, and communities with a more
comprehensive understanding of how lighting can affect the urban environment.
Lighting systems and its required parameters that may characterize the
quantity, and quality of light are necessary in a VE [193].

Wittkamper et al. [194] indicated lighting is a crucial design element that
may be utilized to set the mood and ambiance of a space, highlight particular
points of interest, or visually mute distracting elements. Since lighting
design is one of the XR technology’s most sophisticated use cases, its
lighting simulation skills must be given a lot of weight. They pointed out
that the XR system must keep the natural and virtual lighting conditions
closely following one another throughout time. The number of light sources
should not exceed the number of hardware-accelerated lights, as rendering must
be done in real-time. It is evident that real-time rendering is impossible for
the tens or even hundreds of fixtures provided in a lighting arrangement. It
is necessary to offer suitable techniques that significantly lower the number
of light sources used while minimizing the loss of visual quality [194].

Photo-realistic rendering is also important for XR rendering. It involves the
rendering of the scene using an inverse light transport equation. It has been
also known that the least square is able to solve the inverse photo-realistic
rendering effectively [195]. While working well in a controlled environment,
the practical rendering can be challenging for practical applications. The
variation of outdoor lighting intensity makes it difficult for XR app
designers to design one app to be suitable for all these variations. As such,
an adaptive lighting model is needed under this condition [196, 197, 198].
Second, lighting model will be also useful for the XR applications in dark
environments such as the applications used at night [199].

It is known that XR technology relies on a good amount of lighting to generate
high-precision spatial mapping. Poor lighting makes it difficult for the
generation of high-precision spatial mapping [199]. Without a correct spatial
mapping, it is infeasible to correctly place virtual objects into the scenes,
therefore, making it hard to use XR app under such conditions. To resolve this
problem, a scene illuminator needs to be used. Presently, Hololens is equipped
with scene illuminators for this purpose. The design of scene illuminators is
however challenging considering the variation of lighting conditions. The
design of scene illuminators can be made better produced through the use of
light simulation software. For designing such illuminators, lighting
simulation software can be used such as Radiosity and Photon Mapping.

Conversely, XR would not work well if there is too much light either. This
would occur when using the XR directly toward sunlight. Under this condition,
sunlight flux can become a major issue [200]. Most of XR device manufacturers
have clearly stated that the use of XR needs to be cautious while using the
device under direct sunlight [201]. However, it is not rare to find such
applications in practice, e.g., construction, farmers, and welding industries.
Therefore simulation of the light intensity and how it affects XR virtual
object rendering needs to be well-addressed. For simulation of the adverse
impacts of strong light flux on XR virtual object rendering, high-level
lighting simulation is not helpful because they are mostly designed as either
view or scene-based lighting simulation. To resolve this problem, low-level
lighting simulation software is needed.

Low-level lighting simulation for XR device simulates the light propagation of
XR device [202, 203, 204, 205, 206]. The light propagation of the XR device
follows the light diffraction rule. Light diffracts in the air consisting of
near-field diffraction (Fresnel diffraction) and far-field diffraction
(Fraunhofer diffraction). XR device follows the near-field diffraction rules.
To manipulate the projected virtual object intensity and counter the effect of
sunlight flux, light intensity from Fresnel diffraction needs to be well
controlled. For this reason, the light source, typically RGB LED diodes,
should be carefully chosen to satisfy the needed projection of object
intensity. Furthermore, adaptive light intensity filters are needed to enable
adaptive light projection. The simulation software needs to consider these
factors to get the appropriate light intensity in the presence of extreme
sunlight flux. To design XR device filters and light sources for adaptive
virtual object lighting intensity, professional ray tracing-based optical
device simulation Zemax is needed. Zemax is a proven software known able to
simulate optical devices with high accuracy. However, Zemax is a general-
purpose optical device simulation software. It means that it is not optimal
and time-consuming to use Zemax for the simulation of the XR device. More
specific AR-specific simulation software such as Synopsys LightTools should be
considered. Different from Zemax, Synopsys LightToolscan be used to simulate
the grating design of the AR device. The grating of the AR device is located
in the glasses of the AR device. The grating is a critical optical part to
display the images, especially in low or strong lighting conditions. Light
tools is able to simulate different ambient lighting conditions and how it
impacts the displayed virtual objects therefore useful to design AR devices.

####  IV-B1 Lighting in Virtual Reality

The use of immersive VR as a lighting design tool is a topic that has piqued
the scientific community’s curiosity. Simulating lighting in VR involves the
use of advanced graphics engines, computer-generated imagery, and real-time
rendering techniques to accurately replicate the behavior of light in a
simulated environment. This is achieved by modeling light sources, materials,
and their interactions with the environment to create a realistic and
immersive lighting simulation. Additionally, modern VR systems utilize high-
resolution displays and optics to present this simulated environment to the
user, further enhancing the sense of realism and immersion. VR Technology
would allow for a comprehensive analysis of lighting alternatives,
encompassing factors like aesthetics, environmental light pollution, and
energy efficiency. Additionally, this analysis would be intelligently
facilitated through complete computer assistance [207].

The ability to reproduce a light environment that appears to be real, a
correct light distribution from a photometrical standpoint, and the
impressions that people have in real locations are all important factors in
employing VR environments as a substitute for real ones. VR is emerging as a
valid alternative for evaluating the perception of the indoor visual
environment due to the ability to control selected variables, analyze cause-
effect relationships, and save time and cost [193]. In addition, immersive VR
makes it possible to circumvent some obstacles to conducting experiments in a
real-world setting, such as lighting, by modifying the visual environment and
securely investigating hazardous environments [208]. By enabling the control
of lighting conditions and rapid alternation of visual stimuli, as well as by
providing a high degree of immersion—a factor that has been identified as
crucial in recreating the experience of real space—this technology has the
potential to overcome significant obstacles in conducting experiments in real
environments [209].

VR can accurately replicate the lighting characteristics of a physical
setting, including open/closed, diffuse/glaring, bright/dim, and noisy/quiet
[210]. However, another study indicated that the main restriction on the use
of VR for lighting research is the lack of a standardized investigation
approach, along with the inability to identify the most valued setting
parameters (CCT, illuminance, source typology, daylight conditions), as well
as to observe people’s behavior with regard to their interactions with
lighting controls and shading devices [193]. The study [208] confirmed a
research gap in determining how accurately gaming engines reproduce the
dispersion of light; as a result, immersive virtual reality might be utilized
for light design considerably more. In [193] comparisons between luminous
conditions in VR and real environments has been conducted. Another study [211]
introduced RadVR, a VR tool for daylighting analysis that integrates
qualitative evaluations simultaneously with immersive real-time rendering to
offer potential support for tasks requiring spatial knowledge, navigation, and
analysis of the position of the sun in VR. Study [212] presented VRGlare, an
immersive lighting performance simulator that incorporates VR to provide real-
time glare modeling and analysis. While VR has shown promise in lighting
design, there is still a perceptual divergence between virtual and actual
lighting experiences that needs to be addressed. Future research should focus
on improving the accuracy of virtual simulations, exploring different display
technologies, and understanding user factors that influence perception. By
narrowing this perceptual gap, VR can become a valuable tool in the field of
lighting design, facilitating efficient prototyping and adaptation of lighting
designs.

####  IV-B2 Lighting in Augmented Reality

Most of the research has focused on exploring VR in the fields of lighting
design and research, while AR has not received an equal level of attention. AR
which refers to any interface that superimposes digital content onto real-
world as described in the reality-virtuality continuum [213], can be a
promising solution for digital twin technology where the real and the virtual
world are integrated.

Simulating lighting in AR involves the use of computer vision algorithms and
machine learning techniques to detect the physical environment and accurately
overlay virtual lighting elements onto real-world surfaces. By using the
camera and other sensors on an AR device, such as a smartphone or tablet, the
system can accurately detect the position and orientation of virtual light
sources and simulate the behavior of light in the real-world environment. This
allows designers and architects to create and visualize lighting designs in
situ, providing a more accurate representation of how lighting will appear in
real-world conditions.

The integration of digital twin and AR technologies in lighting design offers
several potential benefits, including the ability to create and test lighting
designs in a virtual environment before implementing them in the real world. A
digital twin is a virtual replica of a physical object or environment that can
be used to simulate and test different scenarios, while AR overlays virtual
elements onto the real world, creating an interactive and immersive
experience. By combining these technologies, designers can create a virtual
replica of a real-world environment, overlay virtual lighting elements onto
it, and use AR technology to visualize and test how the lighting design will
appear in the physical space. This approach can help designers identify
potential issues and optimize their lighting designs, leading to more
efficient and effective solutions.

Digital twin has emerged for modeling and simulating data and processes,
creating a variety of potential for assisting the decision-making process
based on the evaluation of realistic scenarios [214]. The same study presented
a digital twin light modeling for design in which researchers investigate the
design, development, and simulation of lighting interventions, as well as of
analyzing possible light effects in the urban/outdoor context. A study by
Sheng et.al [215] explored the use of a spatially augmented reality (SAR)
sketching interface for architectural daylighting design. The study focused on
the development and evaluation of an SAR system that allows architects to
sketch their designs and assess the impact of daylighting in real-time. The
system consists of a custom-built projector and a Microsoft Kinect sensor,
which are used to project virtual daylighting scenarios onto physical models
of buildings.

Another study [216] described LitAR, a system that addresses the spatial
variance problem in lighting reconstruction for mobile AR applications. The
system employs two-field lighting reconstruction, two noise-tolerant data
capturing policies, and two real-time environment map rendering techniques to
handle the computation requirement mismatch. LitAR offers knobs for developers
to make trade-offs between quality and performance. Evaluation results show
that LitAR achieves visually coherent rendering effects, significantly reduces
point cloud projection time, and outperforms a recent work in terms of Peak
signal-to-noise ratio (PSNR) value.

Similar to VR, the accuracy of AR in emulating real lighting conditions
requires careful evaluation to guarantee its dependability. AR’s strengths lie
in its capacity for on-the-spot collaboration and decision-making, yet there
is a clear need for more research to thoroughly assess its effectiveness and
establish optimal practices for its implementation in lighting design.

####  IV-B3 Lighting in Mixed Reality

Simulating lighting in MR involves the use of specialized headsets equipped
with cameras and sensors to track the user’s environment and integrate virtual
objects with the real world. The addition of spatial mapping and gesture
recognition enables the user to interact with virtual lighting elements in a
more natural and intuitive way, allowing for a more immersive and responsive
experience. By using these technologies, designers and architects can create
and test lighting designs in a VE that accurately reflects real-world
conditions. The study [217] in lighting design presents a method for
generating accurate shadows of virtual objects in a mixed reality environment.
Researchers in this study described a method for displaying realistic shadows
of virtual objects in a MR environment by reconstructing the light source
distribution of a scene in real-time through the segmentation and analysis of
a known occluding object’s shadows.

Park et al. proposed a method for estimating lighting in mixed reality
environments. The method uses a deep learning algorithm to estimate lighting
from a homogeneous-material object, which serves as a proxy for the real
environment. The paper describes the technical details of the method,
including the architecture of the deep learning model and the training
process. The method is evaluated using a series of experiments that
demonstrate its effectiveness in estimating lighting in a variety of MR
scenarios. The paper concludes that the proposed method is a promising
approach to solving the problem of lighting estimation in MR environments, and
that it has the potential to significantly improve the quality and realism of
MR experiences [218].

Another study [209] introduced a method for generating photo-realistic
renderings of MR scenes. The method uses image-based lighting and physically-
based rendering techniques to create realistic lighting effects consistent
with the real-world environment. The paper outlines the technical details of
the proposed method, including the process for capturing and processing the
real-world environment and the algorithm for rendering the mixed reality
scene. Santos et al. [219] addressed various display technologies, including
head-mounted displays, projection systems, and desktop displays, as well as
different rendering techniques, such as rasterization, ray tracing, and real-
time rendering. The paper also discusses the challenges associated with
displaying and rendering complex 3D models in MR environments, such as the
need for high-resolution textures and efficient rendering algorithms.

The fidelity of MR in simulating lighting conditions requires thorough
investigation to ensure that it can be trusted for use in design and research.
The advantage of MR lies in its ability to provide an immersive and
interactive experience that can be manipulated on the fly, but as with AR and
VR, further research is necessary to fully understand its capabilities and to
craft guidelines for its application in lighting design.

####  IV-B4 Lighting and Evaluation

Assessing the influence of lighting on the rendering of virtual objects,
whether in XR devices, is crucial for designers. The disparities between VR
and AR devices lead to variations in the generated content. In contrast to VR,
AR content is the overlay of the object information on top of real-world
objects, therefore the lighting model needs to consider the ambient background
information. The perception of VR-AR generated content can be precisely
captured and evaluated using a holistic approach. Such a holistic evaluation
approach attempts to model the scene rather than treating each object
evaluation separately, which is known ineffective to capture the actual user
perception of the AR.

Peak signal-to-noise ratio (PSNR) and structural similarity index measure
(SSIM) are two common measures for the evaluation of VR and AR image quality.
These two measures are able to evaluate the noises of the images and the
similarity of the rendered images in contrast to ground truth. These two
measures are known to work well and robust for not only AR image quality
evaluation but also applicable to many other fields as well such as microscopy
images, image denoise, super-resolution imaging, and image generation. To deal
with the specialty of VR and AR images, which are omnidirectional images,
Craster parabolic projection PSNR and a weighted-to-spherical PSNR namely CPP-
pSNR and WS-PSNR are also introduced through the evaluation process [220,
221].

Subjective measures are also used for omnidirectional image visual quality
assessment (VQA). Mean opinion score (MOS) and differential Mean opinion score
(D-MOS) are used for subjective user evaluation, where later accounts for the
differences of consequent images displayed to users [222, 223]. Specifically
for the assessment of omnidirectional images, the view angles and their
impacts on the opinion score are weighted in, yielding the new measure namely
O-MOS. The O-MOS is formulated by having users watch the baseline (good
quality) and impaired quality of the omnidirectional images, following
watching, the users are expected to assess the images. O-MOS value is
calculated by weighting the differences of the image quality [224, 225].
Furthermore, the separation of 360 video into different regions and performing
the subjective assessment of each such region is also important for detailed
omnidirectional image evaluation [226].

Deep learning-based evaluation is also used for omnidirectional image VQA.
Early attempts include the Convolutional Neural Network (CNN)-based
omnidirectional image salience (attention) deep learning method [227, 228].
The CNN-based deep learning while effective is not able to predict image
quality, it tends to over-fitting the generation of less confident prediction
results. It is because the high-frequency noise in the high-quality image
tends to cause over-fitting problems. To alleviate such concerns, a smoothing
term is introduced to smooth out the high-frequency noises thus reducing the
adverse impacts of the high-frequency noises to reduce the over-fitting
problem [229]. Some large data set has been created to fill the gap of small
data set problem since deep learning requires large data set for training and
validation, as such it has become more practical to use deep learning to
perform omnidirectional image VQA [230].

Adversarial (GAN network approach) learning is also used for the VQA of
omnidirectional images. This work consists of objective VQA and subjective
VQA. The learning-based framework uses the objective VQA to deceive the
subjective VQA, therefore, achieving high-quality and accurate objective VQA.
More specifically, the objective VQA uses positional and visual
characteristics of the omnidirectional image by encoding the positional
feature and visual feature of a patch on the omnidirectional image. It follows
by the deceiving process of the subjective VQA which serves as the guider of
the training process. Results show that the proposed adversarial learning is
able to achieve state-of-the-art results outperforming PSNR-based evaluation
methods [231]. Such adversarial learning is not only used for the assessment
of image quality but also the salience mapping, which is another important
measure of the VQA [232]. Furthermore, deep reinforcement learning has been
known able to extract the salience map effectively [233, 234].

In comparison with VR VQA, AR VQA uses similar measures. Among them, MOS is an
effective measure. Similar to VR VQA, both reference images and impaired
images are needed for image quality assessment. The difference is that VR VQA
uses omnidirectional images, whereas AR VQA requires the use of conFusion
images. The conFusion images are the fusion of both foreground image (virtual
object image) and background (real-world) images. Such conFusion images are
similar to the impaired images in VR VQA. For this, a large-scale state-of-
the-art ConFusing Image Quality Assessment (CFIQA) database has been
established. It has 600 reference images and 300 distorted images generated by
mixing reference images in pairs [235]. Use of the CNN-based deep learning
approach, it has shown that such a large database is able to achieve object
assessment score matching with subjective assessment score, therefore, showing
the effectiveness of the deep learning-based AR VQA [235]. This method is also
better than conventional AR VQA, which typically lacks of the background scene
and only the foreground image is evaluated [236].

While lack of literature dedicated particularly to how lighting influences the
VR and AR VQA, insights related to it still can be offered in the light of
aforementioned literature related to VR and AR VQA. The direct impact of
lighting on the results of image quality can be well simulated with scene-
based lighting simulation tools such as Radiosity and Photon Map. The
simulated lighting impacts on the user perception can be done through the
creation of large-scale simulated VR and AR images using similar procedures of
existing data set such as CFIQA [235]. Similar deep-learning frameworks such
as CNN, adversarial, and reinforcement learning can be implemented to bridge
the gap between object evaluation scores with subjective evaluation thus
achieving a state-of-the-art lighting-related VR and AR VQA. It is expected
that such kind of work will contribute to the understanding of the impacts of
lighting on human behavior and activities stated below.

###  IV-C RQ3: What are the effects of lighting on human behavior and
activities?

Modern lighting significantly impacts all aspects of human life as it improves
the quality of city users in SBE [237]. Light is the stimulus that affects
individuals’ perception, performance, and well-being in everyday life.
Numerous research has looked into how lighting affects people’s health [238,
239, 240, 241, 242]. The impact of light exposure on human health can be
either positive or negative, and direct or indirect [238] depending on
variables such as gender, age, culture, and so on.

This section examines light’s physical and psychological aspects,
investigating the connections between light-based emotions, behaviors, and
performance in illuminated environments in various sensory scenarios. The goal
is to compile the available research on the relationships between indoor
lighting (both natural and artificial) and overall health, well-being, and
productivity. As a main component of SBE, smart lighting’s primary objective
is to satisfy inhabitants’ needs while promoting user ease, comfort, and
security [243]. Therefore, the impact of light on SBE inhabitants requires
strong attention.

####  IV-C1 Mood and Emotions

Studies show that certain impressions and emotional reactions in people can be
triggered by lighting [239]. It can impact mood directly by influencing
neurotransmitter availability, such as serotonin, which is involved in mood
regulation, and indirectly by stabilizing circadian rhythms [244]. The results
from some related studies suggest that besides exploring traditional elements
of lighting quality, which explains the visual aspect of lighting, new
insights about a non-visual response to light are required to examine. This
includes the impact of light on emotional state.

Color temperature and light intensity are the two crucial lighting
characteristics that have a unique psychological and cognitive impact on
people. Researchers have looked into how the color of light affects mood. Knez
[242] examines how individuals’ self-reported mood, cognitive function, and
perception of room light were affected by morning color (‘warm,’ ‘cool,’ and
‘artificial daylight’ white illumination).

Since lighting significantly impacts the human condition, it is also used for
therapy. To treat mental diseases, light therapy (LT) has become more and more
popular over the past few decades [245, 246, 244]. Although the mechanics
underpinning LT require further study, there has been multiple research on
this area [239, 245]. Lighting can also help in reducing depression, anxiety,
and sleep disorders. Moreover, growing evidence shows that it can be used to
treat dementia, delirium, and attention-deficit/hyperactivity disorder [245].
There also exists evidence that chronic fatigue can be controlled by light
treatment. Fatigue can impair some elements of mood, cognitive, psychomotor
functioning, and work performance. According to research [247], using both
bright and dim light treatment may effectively lessen fatigue symptoms.

Positive technologies may improve social, psychological, or subjective well-
being (hedonia), according to some theories [248, 249]. VR is one of the
technologies mentioned in the context of “positive technologies,” particularly
as a “hedonic” technology that enables joyful experiences in the present. VR
is a good contender for developing positive experiences because of its
immersive capacity and the sense of presence that it creates. There is not a
fair amount of research evaluating humans’ positive and negative moods in VR
for accurate assessment. According to [249], VR can induce a range of positive
emotions, including joy, relaxation, and more complex feelings like awe (i.e.,
a sense of wonder when faced with vast and transcendent stimuli) and the
sublime (i.e., a sense of “amazement tinged with fear” in response to powerful
or large-scale stimuli). While AR might not be as immersive as VR, its
integration into daily life offers a seamless way to provoke positive emotions
and possibly even elevate moods.

####  IV-C2 Seasonal Affective Disorder

LT is primarily used to treat seasonal affective disorder (SAD), a type of
seasonal depression that is more prevalent in the fall and winter when people
are exposed to less sunlight. The leading cause of SAD is the lack of
synthetic hormones in the brain caused by the short daylight hours [10].
Numerous studies have demonstrated the benefits of full-spectrum illumination
on seasonal affective disorder [250]. SAD treatment using bright light therapy
(BLT) was first made available in 1984 [245, 251] and since then, a
significant number of researchers have explored the impact of LT on SAD [252,
253, 245, 10].

A 3D uniform light field can be used to provide healthy lighting space that
can be used for SAD therapy and circadian rhythm [241]. One of the advantages
of this approach is that the 3D uniform light source can treat multiple
patients simultaneously, even during walking. They evaluated the effectiveness
of their approach by measuring the optical parameters and comparing the result
to reality. The result indicates that the optical power, color temperature,
and divergence angle are very close to reality, meaning that the simulated
data could be trusted.

VR technologies are playing an increasingly important role in the diagnostics
and treatment of mental disorders. VR can be a game-changer in the growth of
strategies for treating depression because many symptoms of depression are
feelings-related. While ample studies indicate the effectiveness of VR
intervention to combat SAD [254], the reliability of VR as a tool for
assessing specific environmental aspects like lighting quality for this
population is still up for debate. While studies on VR’s effectiveness in
treating SAD suggest its promise, the use of AR and MR for similar purposes
remains less explored, especially concerning the assessment of environmental
factors like lighting. Therefore, ongoing research into the reliability and
effectiveness of AR and MR in these contexts is crucial to validate their role
in mental health treatment strategies.

####  IV-C3 Attention Deficit Hyperactivity Disorder

Studies have shown a correlation between light exposure and attention deficit
hyperactivity disorder (ADHD). ADHD is prevalent among adolescents and is
estimated to affect up to %5 of adults worldwide [255, 256]. High rates of
neuropsychiatric comorbidities, such as anxiety and depression disorders, are
seen in adults with ADHD [255, 257, 256]. The usage of BLT in ADHD treatment
has been addressed by multiple studies [258, 259] complementary treatment for
ADHD symptoms in adults. Morning BLT was associated with improved mood
symptoms, a considerable phase advance in circadian preference, and a
significant decline in subjective and objective indicators of core attention
deficit hyperactivity disorder pathology [258]. Given the current importance
of light therapy in treating ADHD, VR can be an excellent tool for simulating
morning light and for providing the ADHD population with personalized light
exposure based on their diverse conditions. AR could offer more flexibility
and practicality by overlaying therapeutic light settings onto a user’s real-
world routine, making it a convenient tool for continuous light-based
interventions. MR’s unique ability to blend real-world and virtual elements
might also enable more nuanced control over the light therapy parameters,
offering a tailored approach that can adapt to the dynamic conditions and
needs of those with ADHD. As with AR and VR, however, the application of MR in
this context requires thorough research to optimize its effectiveness and
usability.

####  IV-C4 Effect of Light on Social Life

Light has always been a stimulus for the emergence of social activity. We
perform a larger variety of activities in the settings we often inhabit. As a
result, lighting needs to be sensitive to adjust to the range of activities
being carried out. Researchers indicate the role of lighting on people’s
social life, but only a few studies examine the effects of lighting on
interpersonal communication [260, 261, 262, 263]. In [264, 263], researchers
represent that dim lighting lessens eye contact and increases conversation
latency. According to the study [265] people engage more with their
surroundings in light environments with high light ratios, where socially
significant objects are the major focal points. In contrast, people interact
more with each other in low-level lighting.

Multiple efforts have been made to evaluate the impact of XR technologies on
social life, but there is not a fair amount of data related to light exposure
to XR and its effects on human social life. Light can increase social
interaction in a VE due to inducing positive feelings. VR has become one of
the most well-popular digital social environments where individuals can
connect, engage, and socialize in novel and immersive ways. Based on existing
research findings, VR empowers us to get together with people from all over
the world and share experiences that aren’t possible to have in the actual
world.

####  IV-C5 Safety Perception

The correlation between lighting and perception of safety is a complex issue.
Any inhabited environment must have safe circumstances, so it is important to
consider how light affects safety. The environment ought to be planned to
compensate for human potential limitations at least partially. A person is
more likely to notice a hazard and take the necessary action if something
helps their vision. When lighting is involved in accidents, in many cases, low
illuminance levels or poor lighting quality can be blamed as the root cause.
There may be poor visibility as a result of all of these. Adequate lighting is
required for visual performance and safety and to prevent falls and injuries
[266].

Lighting significantly impacts people’s perceptions of safety after dark, but
the mechanisms by which lighting affects perceived safety are largely
unexplored. There has been limited research on indoor lighting design
concerning safety concerns, while most studies concentrate on outdoor lighting
and perceptions of safety. In one study, researchers have looked into how
humans perceive light, particularly how they perceive safety, depending on the
brightness, uniformity, and position of the light [208, 267]. Another study
reinforces the notion that artificial light at night exposure may negatively
affect psychological functions [268]. Since there is a strong relationship
between safety perception and lighting, safety consideration in lighting
design is a matter of growing concern. Recognizing the multifaceted impact of
lighting on safety, further exploration of these dynamics is essential to
create environments that promote well-being and security. Little efforts have
been made to evaluate the correlation between lighting and user perception in
XR technologies. Although findings are limited, there is an indication that
brighter lights and well-lit environments actually make people feel safer in
VR. In addition, VR and AR have the ability to successfully boost safety and
security in various industries [269].

####  IV-C6 Task Performance

Along with the effects on overall human health and well-being, lighting also
affects task performance [270]. Recent studies [271, 272, 273] have revealed
that serotonin and melatonin regulation, which are related to how sunlight and
light therapy affect mood, are also involved in cognition, which raises the
possibility that light may potentially have an impact on cognitive
performance.

The important components of lighting that have been shown to affect task
performance are illuminance and color temperature. Various studies show an
increase in task performance with higher illuminance. Hughes and McNelis [274]
reported improvements in performance with increased illuminance in difficult
paperwork of office workers. Vimalanathan and Babu [275] identified that a
higher illuminance significantly improves neurobehavioral test reaction time
and error response. Smolders et al. [276] showed the effects of illuminance on
subjective alertness and vitality, sustained attention in various tasks. The
underlying principle is that illuminance improves vision, enhancing the
ability to perceive optical information and increasing task performance [277].
However, when the illuminance is sufficient for visual performance, the
relative increase in performance decreases.

Another metric of lighting that affects task performance is color. Kwallek et
al. [278] investigated the influences of interior colors on workers’
productivity and found that the effects can vary among different individuals.
They also reported that the interior colors could have the opposite impact on
individuals’ long-term and short-term productivity. Viola et al. [279] found
that blue-enriched white light in the workplace improves self-reported
alertness and performance. Recent studies also demonstrated that long-
wavelength red light can increase objective and subjective measures of
alertness, resulting in increased task performance. Mills et al. [280]
reported improved self-reported concentration and task performance with
fluorescent light sources. Studies also evaluated the combined impact of
illuminance and color on task performance. Mills et al. [280] showed that
higher illuminance and cooler color temperatures enhance concentration levels.
Whereas lower illuminance and warmer color temperatures seem to improve
communication and social behavior [281]. Barkmann et al. [282] showed a
positive impact of variable lighting on standardized tests among school
students.

Task performance has been a major focus of previous work developing and
evaluating XR applications. It should be noted that performance is highly
affected by incorrect occlusion in AR, and this effect outweighed the
influence of spatial characteristics. In contrast, as more sensory inputs are
supplied, and simulation fidelity rises, objective and subjective performance
measurements in virtual reality environments rise.

####  IV-C7 Other Considerations

Although light treatment has been shown to be helpful, not everyone will
benefit from it. A more strong, bright, and more precisely focused light might
be required for some patients. The intense light may be too much for some
people to take, and it may also worsen some diseases by causing new symptoms
to emerge. Eye damage is also a possibility, but one that happens seldom
[283]. Another study reveals that many adult outpatients with ADHD report an
excessive sensitivity to light [259]. The same study indicated that people
with ADHD symptoms wore their sunglasses more during the day in all seasons.
This is associated with Photophobia which is related to the functioning of the
eyes, which mediate dopamine and melatonin production systems in the eye.

In [238], there is an indication that light exposure influences human health
positively or negatively. Improper lighting setup may cause visual discomforts
such as eyestrain and headaches or cause damage to the eye and skin. However,
other factors, such as stress, anxiety, and isolation are likely more
influential than lighting exposure. The severity of the effect of lighting
exposure can vary from short-lived and insignificant to long-term and fatal,
and the same lighting conditions can have different impacts on different
individuals. Gender, age, personality, psychological health, medical
conditions, and other human factors can cause a variation in the impact of
lighting.

A significant influence of gender on various room light estimate aspects
revealed that females saw the room light, across all light settings, as more
expressive than males. Short-term memory and problem-solving performance were
mainly affected by light hue, with individuals doing better in ‘warm’ than
‘cold’ and ‘artificial daylight’ white illumination. Men fared best in ‘warm’
and ‘cool’ white lighting, whereas women did better than men in ‘artificial
daylight’ white lighting. Gender also influences how social safety is
experienced, according to the research. In fact, it was discovered that gender
was one of the best predictors of perceived social safety, with women
typically feeling lower levels of social safety in the same lighting
conditions as men [284].

The measurement method also needs to be considered when considering the impact
of light exposure on SBE inhabitants. Current subjective measurement tools
used for measuring the psychological effect of lighting require re-evaluation
and improvement [239]. State-of-the-art approaches are required to achieve an
accurate and comprehensive specification of the luminous environment.
Techniques and analysis used in the past need to be combined into “a single
experimental protocol,” and newer techniques need to be supplemented to
successfully relate the psychological and behavioral effects of lighting.

Immersive virtual environments offer novel opportunities to realistically
experience various lighting design cases. With the advancement in XR
technologies color and illuminance levels could be accurately reproduced to
simulate lighting conditions. It can also be an effective tool to evaluate the
effect of lighting on visual perception and task performance [285].

##  V Discussion

We conducted a systematic review of total of 270 papers to investigate current
advancements in lighting simulation tools along with identifying new solutions
to satisfy the needs of modern lighting design features. Figure 3 demonstrates
the frequency of the papers per year. Based on reviewing the 65 number of
papers which comprehensively compared different tools and algorithms (apart
from the remaining 230 papers that scrutinized simulation techniques), in
turns out with modern lighting design growth and its complexities, it’s hard
to benefit from existing simulation tools to achieve an adequate analysis of
all the features and opportunities that modern lighting provides. So, a need
for new tools and approaches is inevitable to fill the current gaps in the
area of artificial lighting simulation. To investigate existing solutions we
reviewed 41 papers on extended reality technology and solutions it offers.
Based on the potential of XR technology to enhance user experience, a
comprehensive analysis of existing literature has revealed a wealth of
information pertaining to various lighting design techniques used in XR.
Nevertheless, this systematic review aims to provide a thorough investigation
into the role of smart lighting design in extended reality, with a specific
focus on the simulation and design of lighting conditions. The primary
objective of this research is to inspire other scholars to delve deeper into
the intersection of these two domains, in order to discover new avenues for
innovation and design.

On the other side of this study, we addressed reviewing 48 papers to
investigate the psychological and physiological effects of lighting design
strategies on users. Although the effectiveness of light and its
characteristics like color, intensity and temperature on users’ mental and
physical health is deniable, there is currently no consensus on how to
quantify the stimulus for the psychological effects of lighting. Our current
understanding indicate that people spend more time inside and are more likely
to experience depressive symptoms, feelings of loneliness, and isolation
during the long, gloomy winters, which makes them less healthy and sociable
and more likely to suffer from mental diseases like sadness and loneliness.
Although the effects of lighting on our mood and behavior have not yet been
thoroughly established, experts in the field advocate for a shift in the
lighting sector towards a greater focus on human health. The majority of
research on interior lighting has examined workplace settings and concentrated
on the visual qualities of the area to increase worker happiness and
productivity. There aren’t many research that have used real-world trials to
expose individuals to various lighting conditions and track how mood and
behavior alter in relation to home interiors. The review sheds light on
conducting a systematic review of the literature and synthesize the available
data regarding the relationships between indoor lighting, including both
natural and artificial light as well as nighttime lighting, and a variety of
health outcomes. To obtain data on various aspects of indoor lighting and its
impact on health, including lack of light, various types of illumination, and
light as a possible source of hazards, such as from indoor air pollution and