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Review

A Map of the Research About Lighting Systems in the 1995–2024 Time Frame

by
Gaetanino Paolone
1,†,
Andrea Piazza
1,†,
Francesco Pilotti
2,†,
Romolo Paesani
2,†,
Jacopo Camplone
1,† and
Paolino Di Felice
3,*,†
1
B2B S.r.l., 64100 Teramo, Italy
2
Gruppo SI S.c.a.r.l., 64100 Teramo, Italy
3
Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Computers 2025, 14(8), 313; https://doi.org/10.3390/computers14080313 (registering DOI)
Submission received: 29 April 2025 / Revised: 17 July 2025 / Accepted: 21 July 2025 / Published: 1 August 2025

Abstract

Lighting Systems (LSs) are a key component of modern cities. Across the years, thousands of articles have been published on this topic; nevertheless, a map of the state of the art of the extant literature is lacking. The present review reports on an analysis of the network of the co-occurrences of the authors’ keywords from 12,148 Scopus-indexed articles on LSs published between 1995 and 2024. This review addresses the following research questions: (RQ1) What are the major topics explored by scholars in connection with LSs within the 1995–2024 time frame? (RQ2) How do they group together? The investigation leveraged VOSviewer, an open-source software largely used for performing bibliometric analyses. The number of thematic clusters returned by VOSviewer was determined by the value of the minimum number of occurrences needed for the authors’ keywords to be admitted into the business analysis. If such a number is not properly chosen, the consequence is a set of clusters that do not represent meaningful patterns of the input dataset. In the present study, to overcome this issue, the threshold value balanced the score of four independent clustering validity indices against the authors’ judgment of a meaningful partition of the input dataset. In addition, our review delved into the impact that the use/non-use of a thesaurus of the authors’ keywords had on the number and composition of the thematic clusters returned by VOSviewer and, ultimately, on how this choice affected the correctness of the interpretation of the clusters. The study adhered to a well-known protocol, whose implementation is reported in detail. Thus, the workflow is transparent and replicable.

1. Introduction

Lighting Systems (LSs) are everywhere: inside cities, in homes, in offices, in public buildings, and along streets and highways. The advent of LSs has sped-up human progress, improving, at the same time, the quality of life [1]. Both industry and academia are engaged in the advancement of this relevant category of applications. From the industry side, it can be observed that the lighting industry worldwide is expected to reach a projected revenue of USD 223.3 million by 2030. A compound annual growth rate of about 7.0% is expected for the worldwide lighting industry from 2025 to 2030 (source: https://www.grandviewresearch.com/horizon/outlook/lighting-market-size/global, accessed on 20 March 2025). From the academic side, it can be observed that the research domain of LSs has been active for many years, and this has produced a huge number of studies published in journals, conference proceedings, and book chapters [2].
Bibliometric Analysis (BA) is a popular and rigorous method for exploring and analyzing large volumes of scientific data (hundreds, if not thousands) [3,4,5]. BA comprises two complementary techniques: Performance analysis and Science mapping. The present work focuses on keyword co-occurrence analysis, which is one of the pillars of Science mapping [6,7,8]. Co-keyword analysis, combined with network analysis, can uncover the knowledge produced by research in a given scientific field [9]. The VOSviewer open-source software is used to construct easy-to-interpret bibliometric networks [10]. VOSviewer is largely used in bibliometric studies [11,12,13,14,15,16,17,18,19,20].
The present study has the following merits:
  • It provides a map of the state of the art of research in the domain of LSs, within a 1995–2024 time frame. The reported bibliometric research is somewhat urgent because, as will be proven subsequently, the extant literature about LSs is becoming huge; nevertheless, as far as we know, our research is the first study in such a category. In recent years, a recurrent refrain has been that the Internet of Things (IoT) and Artificial Intelligence (AI) are the technologies to rely on, so that traditional LSs can make a qualitative leap by becoming smart and at the same time sustainable. This study highlights that the state of the art of LSs confirms this widespread thinking, but at the same time it tells us that the relevance of AI is still marginal. Such a conclusion confirms previous reports from both academia and industry.
  • It meets the rigor requirement for this type of literature review. This objective was pursued by adopting a well-known protocol, whose implementation is reported in full detail. Thus, the investigation is transparent and replicable.
  • It delves into the impact that the use/non-use of a thesaurus of authors’ keywords had on the number and composition of the thematic clusters returned by VOSviewer and, ultimately, on how this choice affected the correctness of the interpretation of the clusters. Published articles adopting keyword co-occurrence analysis can be split in two groups: the smaller group mentions the usage of a thesaurus of authors’ keywords [9,11,17,18,21,22,23], while the larger group does not. References [7,12,15,16,20,24,25,26,27,28,29]. Among the seven works belonging to the first group, references [11,17,23] just mentioned that a thesaurus was used, reference [22] focused on the relevance of adopting a thesaurus for data clean-up, finally, references [9,18,21] detailed the steps needed to build a thesaurus of authors’ keywords that complies the VOSviewer’s format. Ref. [18] listed, in addition, the 34 terms which comprised the thesaurus.
  • The choice of the number of clusters of authors’ keywords was obtained by balancing the numerical value of four independent methods belonging to the family of so-called “clustering validity approaches” [30,31,32,33,34] with what the experts (the authors on this occasion) judged to be a meaningful partition of the input dataset. Such an approach was recommended in [35,36] for overcoming the issue pointed out in ref. [37]. In the latter work, the authors observed the absence of a reason behind their choice, common to most published articles talking about bibliometric analyses, of the value of the minimum number of occurrences for an authors’ keyword to be included in the BA.
  • Regarding the latter two points, it follows that the present study also gives two recommendations from a methodological perspective. First, it points out that keyword co-occurrence analyses must be driven by a robust thesaurus of keywords. Second, the VOSviewer parameters must be optimized.
The remaining part of this work is structured as follows: Section 2 introduces the background necessary to delve into later sections. Section 3 describes the Research Method adopted in the study. Section 4 presents the results of the bibliometric analysis and then replies to the research questions. Section 5 analyses potential threats to the validity of the findings of the present study, while Section 6 concludes the paper. Two appendices complete this paper. Abbreviations lists the used acronyms, while Appendix A shows the entire thesaurus of authors’ keywords entered as input to VOSviewer.

2. Background

This section first recalls the major features of the VOSviewer software, then it focuses on methods that previous research suggested as necessary to accomplish a correct estimation of the number of clusters of authors’ keywords.

2.1. VOSviewer Terminology [38]

This software allows one to create, visualize, and explore networks. A network is composed of nodes and links. Nodes are the objects of interest (authors’ keywords in the present study), while links express a co-occurrence between pairs of nodes. VOSviewer provides a Network Visualization, an Overlay Visualization, and a Density Visualization. Below, we spend a few words about the first two visualization categories.
In Network Visualization, nodes are represented by a circle (the default) and by their label (i.e., the name of the keyword). Nodes are grouped into non-overlapping clusters, labeled as positive numbers. Clusters are colored, the color of a node is the same of the cluster it belongs to. This type of visualization is drawn according to the values of three numeric weight attributes: Links, Total link strength, and Occurrences. The Occurrences attribute denotes the number of papers in which a keyword occurs. The value of this attribute for a node indicates the relevance of the latter in the network. In the visualization, nodes (as well as the associated label) with higher Occurrences are larger than nodes with lower Occurrences. Let us call n a generic node, the value of the Links and Total link strength attributes indicate, respectively, the number of links involving n and the total strength of those links. VOSviewer represents the strength of a link as a positive numerical value. The higher this value, the stronger the link. The thickness of the link between two nodes represents the strength of the link. The strength of a link indicates the number of publications in which two keywords occur together. VOSviewer takes a distance-based approach to visualizing bibliometric networks. The distance between two nodes/keywords in the visualization roughly indicates the relatedness of the keywords in terms of co-occurrence links. In general, the closer two keywords are located to each other, the stronger their relatedness.
The Overlay Visualization depicts a network structurally identical to the previously described network, except that nodes are differently colored. In VOSviewer, by default, colors range from blue (lowest score) to green to yellow (highest score). Colors are shown in the bar located in the bottom right corner of the visualization. The colors are determined by the value of three numeric score attributes: Avg.pub.year, Avg.citations, and Avg.norm.citations. (The latter two attributes are not relevant to the present BA, therefore they are ignored). When Avg.pub.year is selected in the Scores drop down list in the right-hand panel of VOSviewer, then the color of a node/keyword indicates the average publication year of the articles in which that keyword occurs, with yellow indicating terms that mainly occur in recent publications and blue indicating terms that mainly occur in older publications.

2.2. On the Choice of the Number of Clusters

Selecting an appropriate number of clusters (let us call this C ) ensures that similar data points are grouped together, without overfitting or underfitting. A poorly chosen C may result in clusters that do not represent meaningful patterns in the data. In the case of networks of keyword co-occurrences, the output of the clustering stage is determined by the value of the minimum number of occurrences (briefly, the T threshold) for a keyword to be admitted to the BA. Most published articles on bibliometric analysis do not provide any explanation about the rationale behind the choice of T . The explanations in these works often look like the following: “the minimum occurrence threshold of authors’ keywords was set at […]” [39] or other equivalent sentences [8,40]. Lim et al. [9] suggested adjusting the T value in order to have a network at least of 20 nodes/keywords, but no theoretical foundation was provided to justify their proposal. Frequently, the T value changes with the study. For example, in the three articles just mentioned, T was equal to 10, 30, and 3, respectively. Such a variety of choices denotes that there is no one-size-fits-all case, which, in turn, raises the issue of how the T value should be set. To overcome this issue, in the present study, four independent methods were considered. They are briefly recalled in the following:
  • Elbow method [41]
    This method first measures the Within-Cluster Sum of Squares (WCSS) for a varying cluster number, then it selects the cluster for which the change in WCSS starts to decrease. The mathematical definition of WCSS may be found in [33]. In simple words, we can say that WCSS measures how well the data points are clustered around their respective centroids. With respect to leveraging the Elbow method, it is necessary to note that it provides a starting point, but since, in some cases, the Elbow point may not be distinctly visible, a subjective interpretation is then required. In addition, it is useful to remember that such a method is specifically tailored for centroid-based clustering algorithms like KMeans, so it does not necessarily work well for other methods [36].
    To reinforce the clustering decision, the Davies–Bouldin index, the Silhouette score, and the Calinski–Harabasz index [33] are frequently adopted, either individually or together.
  • Davies–Bouldin Index [42]
    The Davies–Bouldin (DB) index is defined as the ratio between the intra-cluster distance and the inter-cluster distance. The former distance is calculated as the mean distance between each element in a cluster to the centroid of that cluster. (It offers insights into how closely grouped the elements within a single cluster are); while the latter distance measures how far apart each cluster’s centroid is from the other clusters. (The larger this distance, the more separated the clusters).
    A lower score of DB index signifies a better cluster formation, with zero being the absolute ideal score. It has been remarked [43] that relying solely on the DB index for cluster analysis would be imprudent, because such an index is only effective with clusters of convex shape. The DB index should be used in conjunction with other metrics for a more holistic evaluation.
  • Silhouette score [44]
    The Silhouette score (Sscore) of an input dataset measures how dense and well separated the clusters are. The mathematical definition of this metric may be found in [35]. For the purposes of the present study, it is sufficient to recall the following: Sscore is defined as [(b − a)/max(a,b)], where “a” and “b” denote, respectively, the mean intra-cluster distance and the mean nearest-cluster distance for each sample in the input dataset. To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of. Sscore takes values in the range [−1, 1]. A higher score indicates better clustering. Values near 0 indicate overlapping clusters, while negative values generally indicate that a sample has been assigned to the wrong cluster.
  • Calinski Harabasz Index [45]
    The Calinski–Harabasz (CH) index is calculated as the ratio of the sum of inter-cluster dispersion and the sum of intra-cluster dispersion for all clusters (where the dispersion is the sum of squared Euclidean distances). In simple words, the CH index measures how dense and well separated the clusters are. The mathematical definition of this metric may be found in [35]. A higher CH score means better clustering.
In conclusion, it can be said that the attention that should be given to the selection of a strategy for finding the C value is well summarized in ref. [35]. In that article, the authors state that the selection of the C value should be performed by balancing the suggestions from the application of cluster validity indices with what experts judge to be a meaningful partition of the specific dataset. The same recommendation ended the more recent ref. [36].

3. Research Method

The research methodology followed a well-established protocol [4,5] comprising the following stages:
  • Definition of the research objectives.
    The present work is the outcome of a cooperation between the Department of Industrial & Information Engineering & Economics of University of L’Aquila (Italy) and an Italian SME (B2B S.r.l). Recently, B2B S.r.l stated a desire to obtain an unbiased map about the state of the art of LSs. Their interest originated within national IT projects that B2B S.r.l. is involved in. The aim of the research is represented by the two Research Questions (RQs) this study aimed to answer:
    RQ1. What are the major topics explored by scholars in connection with LSs in the 1995–2024 time frame?
    RQ2. How do they group together?
  • Literature search and data collection.
    In ref. [4], it was clarified that it is not necessarily convenient to use more than one scientific database in bibliometric research. A more serious issue comes from the inevitable duplication of publications, which makes the findings of the analysis debatable. In the case of Scopus and Web of Science, for example, the percentage of duplicates tends to be large, because most of journals are scanned in both databases. A previous research work by Singh et al. [46] showed that about 99% of the journals indexed in Web of Science are also indexed in Scopus. Moreover, the integration of items belonging to files obtained by querying distinct databases is time consuming, since they provide article information in a different format. In the present study, we queried Scopus rather than Web of Science due to its significantly greater coverage of published literature in the field of LSs, as will be proven subsequently.
    In ref. [47], it was remarked that a search string can be either generic or specific. We opted for the first option, since generic search strings maximize the “recall” value (i.e., the fraction of the documents that are relevant to the query that are successfully retrieved). The search string was the following: (“Lighting system” OR “Light control”). These terms are directly related to the study RQs. Previous research has stressed that to extract the literature relevant to the review aim, the terms in the search string are crucial [4]. The above search string was restricted by adding filters for publication type (articles, conference papers, review, and book chapters) and language. These filters represent what in ref. [4] the authors called the inclusion/exclusion criteria to be applied in bibliometric research. The final search string, plus the filters as written by Scopus, were as follows:
    TITLE-ABS-KEY ((“Lighting system” OR “Light control”)) AND
    PUBYEAR > 1994 AND PUBYEAR < 2024 AND (LIMIT-TO (DOCTYPE, “ar”) OR
    LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR
    LIMIT-TO (DOCTYPE, “ch”)) AND (LIMIT-TO (LANGUAGE, “English”))
    Scopus returned 12,148 documents, which were saved to the following file:
    Scopus12148.csv.
  • Data screening and Preprocessing
    When multiple databases are queried, cleaning the output dataset is mandatory to ensure accuracy. Basically, it is necessary to remove duplicates and correct authors’ names. This effort was not required in our case, since we only queried Scopus. About the preprocessing, it first included the semi-automatic construction of a thesaurus of authors’ keywords, then the investigation of the best T value. Section 4 discusses both these arguments in connection with the clustering effectiveness. In practical terms, this review answers a further relevant research question:
    RQ3. What is the impact of the thesaurus of authors’ keywords and the T value on the clustering effectiveness?
  • Selection of Bibliometric techniques
    The Scopus engine computes statistics for the “publication” and “citation” performance of authors, journals, institutions, and countries. These bibliometric indicators were developed by running a Performance analysis on the dataset output of the search [4]. To match the aim of the present review and, hence, to be able to answer the RQs, the Science Mapping analysis procedure was selected [4,5]. Specifically, keyword analysis was chosen, since this bibliometric method allows mapping relevant publications on a given topic ([3,25]) (in our case the LSs domain), because the authors’ keywords represented the major themes of the underlying publication (before delving into the investigation of its content through a systematic literature review). Moreover, such an analysis allows tracking the evolution of the reference research field over time, and hence recognizing emerging trends.
  • Data analysis, Visualization, and Reporting
    Data analysis and visualization were carried out leveraging VOSviewer. Preliminary, the authors entered the Scopus12148.csv file (returned by Scopus) into VOSviewer. The following options were selected in sequence: (a) create a map based on bibliographic data; (b) read data from the bibliographic database file. As the counting method, full counting was selected. The interpretation of the results is the subject of Section 4, where the three RQs are also answered.

4. Results

This section goes into detail about (a) data collection; (b) data preprocessing (i.e., thesaurus construction and computation of the number of clusters); (c) analysis of the network of the keyword co-occurrences without a thesaurus; and (d) analysis of the network of the keyword co-occurrences in the presence of the thesaurus. The latter argument is split into a node analysis, link analysis, and temporal evolution of the research topics. By summarizing the results, it will be possible to answer the three RQs (Section 6).

4.1. Data Collection

As mentioned in the previous section, the Scopus reply to the input search string was the Scopus12148.csv file composed of 12,148 documents. (Queried with the same search string, Web of Science returned 5577 items, i.e., less than half of the items returned by Scopus.) In addition, Scopus provided the following statistics (built by performing a Performance analysis on the .csv file): documents by year; documents per year by source; documents by author; documents by affiliations; documents by country or territory; document by type; documents by subject area; documents by funding sponsor. Table 1 and Table 2 show, respectively, “documents by year” and “documents venue” to provide an overview of the research performance in the LS domain.

4.2. About Data Preprocessing

As said in Section 3, the preprocessing of the Scopus12148.csv file was carried out in two sequential steps. They are described in the following:

4.2.1. The Thesaurus

In the LSs domain, there are no publicly available thesauri of terms, hence the need to create one. It has been noted that the semi-automatic construction of thesauri using bibliometric analysis tools produces good results and in less time than completely manual creation [48]. The approach followed in this work for the construction of the thesaurus of the authors’ keywords is in line with [48].
Initially, a thesaurus of the authors’ keywords was built. The construction was semi-automatic, as explained hereafter. Using VOSviewer, a network of keyword co-occurrences was built starting from the Scopus12148.txt file. VOSviewer returned 22,999 distinct keywords. Setting T = 5 (similar values have been used in many previously published articles), 1096 keywords satisfied the constraints. Of these, 1089 were interconnected. We chose to limit our attention to these keywords. The 1089 keywords were copied into a word table. Then, the keywords column was sorted in ascending order and, subsequently a second column was added.
Eventually, the thesaurus was built manually and copied into a Thesaurus.txt file. The latter has the structure required by VOSviewer [10]. The contents of the Thesaurus.txt are given in Appendix A, which also shows the synonyms of each keyword in the thesaurus. Table 3 lists the 87 distinct keywords that are part of the built thesaurus, together with the number of synonyms of each keyword. By considering the synonyms, it turned out that the 87 keywords in the thesaurus covered 312 keywords (i.e., the 28.65%) in the text file returned by VOSviewer. The ignored 777 keywords were either too generic or outside of scope of the present BA. Table 4 shows examples of these two categories.
In Section 1, it was remarked that despite use of a thesaurus of authors’ keywords being highly recommended (see, for instance, ref. [10]), its adoption is rare. Refs. [18,22] are the only two articles that we found where a thesaurus about authors’ keywords was not just mentioned but also built and used.

4.2.2. Computation of the Number of Clusters

Figure 1 shows the dataset of the 87 keywords returned by VOSviewer, starting from the 12,148 documents, plus the thesaurus. The points are very dense. This statement is confirmed by the negative result of the test for finding outliers performed by calling the LocalOutlierFactor function, which is part of sklearn.neighbors.
The computation of the four methods recalled in Section 2 was performed using the Colab hosted Jupyter Notebook service that provides free-of-charge access to computing resources (namely: CPU, Python, and a rich pool of ML libraries). Preliminary, the elbow method was applied to the input dataset (Figure 1). Figure 2 shows a plot of WCSS against the number of clusters. The point on the plot where the WCSS curve starts to flatten suggests that C = 5 was the “optimal” number of clusters in our case. After this step, we made further measurements to consolidate the somehow subjective interpretation of the Elbow plot. Table 5 reports the results. As the T value, we adopted 70. This value produced C = 3 , which represented the best trade-off between the suggestion coming from the application for the three selected cluster validity indices and our judgment about a satisfactory partition of the input dataset (Section 2.2).
The next two subsections discuss, in order, the keyword co-occurrence network in the absence and presence of the thesaurus of authors’ keywords.

4.2.3. Analysis of the Network of Keyword Co-Occurrences Without a Thesaurus

Initially, the analysis of the keyword co-occurrences was performed without the thesaurus, as done in a large number of published articles. It has already been mentioned that by submitting the Scopus12148.txt file to VOSviewer and setting T = 5 , the software returned 1096 keywords, of which 1089 were linked to each other. The 1089 keywords were aggregated into 16 distinct thematic clusters. Table 6 shows the top five keywords of each cluster, according to the value of the VOSviewer Occurrences attribute. In [49], the authors also focused their discussion on the top five keywords. The table also shows, for each cluster, the number of involved keywords. Table 7 shows the top 30 keywords (2.7%), selected according to the decreasing value of the Occurrences attribute; while Figure 3 shows the network of the thematic clusters returned by VOSviewer.
Interpretation of the 16 thematic clusters returned by VOSviewer was difficult, due to the simultaneous presence of synonyms, out-of-scope keywords, and too-generic keywords. Let us look, for instance, at Cluster 1 (Figure 4 and Table 8). The following remarks can be made: Cluster 1 is composed of 160 heterogeneous keywords. Examining Table 8, it emerges that Cluster 1 is largely focused on traffic control and traffic light control. This is testified by the following elements:
-
the top keyword is “traffic light control” (oc = 183);
-
14 keywords are synonyms of the “traffic light control” keyword (Table 9);
-
19 keywords are synonyms of the “traffic control” keyword (Table 9).
Table 9. Some synonyms of keywords in Cluster 1.
Table 9. Some synonyms of keywords in Cluster 1.
Synonyms of …KeywordSynonyms of …Keyword
adaptive traffic light controlTraffic light controlcongestionTraffic control
intelligent traffic light intelligent traffic
intelligent traffic light control pedestrian detection
intelligent traffic system road traffic congestion
smart traffic light simulation of urban mobility (SUMO)
smart traffic lights smart traffic
traffic light SUMO
traffic light control traffic congestion
traffic light control (tlc) traffic control
traffic light control system traffic control systems
traffic light control systems traffic flow
traffic lights traffic management
traffic lights control traffic monitoring
traffic signal control traffic network
traffic optimization
traffic simulation
urban traffic
urban traffic control
In addition, Cluster 1 contains keywords addressing transportation either directly (“intelligent transportation systems”, “intelligent transportation system”, “intelligent transport systems”, “intelligent transportation system”, “its”, “transportation”, “intelligent transportation”, “smart transportation”, and “vehicles”) or indirectly (“urban intersections”, “urban mobility”, “vehicle density”, “vehicular networks”, “camera”, “context-aware”, “vehicle detection”, “image acquisition”, and “image segmentation”). There was a further issue that made the interpretation of the thematic clusters returned by VOSviewer difficult in the absence of a thesaurus of keywords. In this latter case, it frequently happened that synonyms belonged to distinct thematic clusters. Table 10 shows a few examples. Last but not least, it is worth noting that most of the keywords mentioned above are outside the scope of the present BA that focused on LSs. In summary, Cluster 1 raises more questions than it answers.

4.2.4. Analysis of the Network of the Keyword Co-Occurrences in Presence of the Thesaurus

Using VOSviewer, a keyword co-occurrence network was built starting from the Scopus12148.csv file, while providing the software with the Thesaurus.txt file as input. This time, T = 70 (see Section 4.2.2). VOSviewer returned a network composed of 25 keywords. The 25 keywords were partitioned into three distinct thematic clusters (Figure 5). The study by Triantafyllopoulos et al. [37] adopted a thesaurus of authors’ keywords that was used together with the Elbow method to decide the number of thematic clusters. Incidentally, in that study, the authors also obtained C = 3 clusters. As in all networks, keyword networks comprise nodes and links between pairs. Next, the nodes are analyzed.
  • Node analysis
Table 11 and Table 12 list the 25 keywords returned by VOSviewer for T = 70 . In the first table, the rows are sorted by cluster number and, within each cluster, by decreasing value of the Occurrences attribute; in the second table, the 25 rows are sorted by decreasing value of the Occurrences attribute.
The following is devoted to analyzing the three clusters composing the keyword co-occurrence network (Figure 5) returned by VOSviewer in the presence of the thesaurus. The three clusters are represented in different colors (red, green, and blue, respectively) based on the link strength between the keywords. Figure 6 shows only the keywords/nodes of the network to better figure out the boundary of the clusters.
The keywords inside each cluster are arranged in a logical way to form statements that capture the essence of the research and scope of the clusters. This approach is becoming popular. Two examples of recent studies are [25,26]. The former study conducted a bibliometric investigation of the state of the art regarding entrepreneurship and religions; while the latter reported on the co-occurrence of keywords in the domain of customer engagement. In 2024, this an approach was called “sensemaking” [50]. Carrying out the sensemaking step enables developing a preliminary understanding of the field’s intellectual structure, highlighting the connection of various research topics, before delving into a reading of the full papers (i.e., before starting a systematic literature review). It has been remarked ([3,25]) that keyword co-occurrence analysis reflects the present of the research domain under investigation. In this specific case, through the present BA, this review aimed to answer RQ1 and RQ2 (Section 3).
Figure 7. Cluster 1 for T = 70 .
Figure 7. Cluster 1 for T = 70 .
Computers 14 00313 g007
The “led lighting system” keyword stood out in Cluster 1, since its value for the Occurrences VOSviewer attribute was the highest among the 25 keywords (oc = 1282). This assertion is confirmed by the findings in [2,51], where the authors stated that LSs are predominantly LED-based today. Moreover, from the cluster, it was reveled that lots of research has been devoted to “lighting control system(s)” (oc = 862) with the aim of reducing energy consumption by mixing “daylight” with “artificial lighting” to make the public LSs of cities more sustainable, ([52,53]) increasing user comfort at the same time [51].
Figure 8. Cluster 2 for T = 70 .
Figure 8. Cluster 2 for T = 70 .
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The nine keywords that compose Cluster 2 tell us that, so far, a lot of papers have investigated the adoption of IoT technology for the development of smart LSs, both for outdoors (the public domain) and indoors (i.e., the smart building domain). Refs. [2,51] confirm this a claim. The fifth keyword in the ranking (i.e., “WSN”) finds an explanation in the review by Gupta et al. [54], where the authors clarified that IoT technology, when incorporated with WSNs, extends its communication capabilities. The adoption of fuzzy logic for smart lighting control (i.e., “fuzzy control”) dates to 2000, when Wu et al. [55] proposed a fuzzy-logic-driven converter for photovoltaic-powered LS applications. More recently, Mohandas et al. [56] investigated a fuzzy logic controller finalized to control the light luminance level in street LSs based on human presence and light condition. Lighting sensors were used as input. The solution combined fuzzy logic with the ANN model. The seventh and eighth keywords refer to the hardware largely used in IoT-based LSs, while “zigbee” is one of the most widely used communication protocols, since it enables low-cost and low-power wireless IoT networks.
Figure 9. Cluster 3 for T = 70 .
Figure 9. Cluster 3 for T = 70 .
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The six keywords in Cluster 3 tell us that to implement future smart cities, both academia and industry are investigating strategies to leverage AI methods (i.e., “computer vision”, “image processing”, and “ML”). This claim is in line with the findings in [2,51].
In light of the above considerations, the following it can be said: The reduced number of keywords and their thematic cohesion allow us to identify, for each of the three clusters, the basic theme that unites the research described by the authors’ keywords in the cluster. Cluster 1 is characterized by research adopting LED-based smart LSs, both for outdoor and indoor applications. Cluster 2 collects studies that propose solutions leveraging IoT technology. Lastly, the third cluster emphasizes the role of Artificial Intelligence in the implementation of smart cities. As a synthesis, it can be said that, from the BA, it emerges that most of the research efforts describe ways to deploy sustainable smart LSs in our cities by combining three technologies, namely LED, IoT, and AI (Figure 10). This finding is in line with the conclusions of two recent systematic literature reviews [2,51].
  • Link analysis
After commenting on the nodes of the network of authors’ keyword co-occurrences, it is worth spending a few words on the links among them. Table 13 lists the 25 keywords returned by VOSviewer for T = 70 , in decreasing order of the value of the Links attribute. From graph theory, we know that a graph with 25 nodes can comprise at most 300 edges (25 × 24/2). From Table 13, it emerges that the total number of links among the 25 keywords is 420. After removing repetitions, the number of distinct links becomes 210. Therefore, the degree of completeness of the keyword co-occurrence network is 70%. This means that, on average, each keyword co-occurs with the 70% of the remaining keywords. Of course, there are keywords above the average and keywords below the average. The following considers the nine top keywords in Table 13:
“Lighting control system”, “energy management”, and “IoT” are linked with all the remaining 24 keywords, hence they are also linked to each other. This tells us that all the articles returned by the Scopus engine (and mentioning these three keywords) adopted an IoT-based architecture to control lights, with the final aim of managing their energy consumption. The next three keywords are linked to 91.7% of the remaining keywords. This value reaffirms that almost all the selected studies referred to LED-based smart street lighting control systems. The next three keywords affirm that, in about 80% of instances, “illumination control” included daylight in order to implement “smart buildings”. An examination of the bottom side of Table 13 tells us that, so far, the number of studies proposing the adoption of AI/ML is not optimal. Accentuating research in which IoT and AI are the pillars of future LSs is a priority, both for industry and academia, to speed up the development of sustainable cities.
  • Temporal evolution of the research topics
Below, we complete the discussion of the results (returned by the analysis of the co-occurrences of authors’ keywords) by examining the temporal evolution of the research topics in the articles about LSs. This information comes to light by reading column Avg.pub.year in Table 14, where the values of the Avg.pub.year score attribute are sorted in ascending order. As described in Section 2.1, the generic value of this attribute denotes the average publication year of the articles in which the keyword on the same row of Table 14 occurs, with yellow indicating terms that mainly occur in recent publications. Therefore, for example, it can be observed that the research topics on lighting control systems initially focused on the use of LEDs (Avg.pub.year = 2016) and also making use of “daylight” (Avg.pub.year = 2016), with the primary intent of containing the energy consumption of such systems (“energy management”; Avg.pub.year = 2017). Then, research turned towards the production of “street lighting control systems” (Avg.pub.year = 2017), “smart buildings” (Avg.pub.year = 2018), and “smart cities” (Avg.pub.year = 2020). The use of the IoT was consolidated in the same period (Avg.pub.year = 2020). Regarding the presence of AI in the LS domain, the overlay visualization (Figure 11) shows that “image processing” (Avg.pub.year = 2015) and “computer vision” (Avg.pub.year = 2016) were investigated first, while ML (Avg.pub.year = 2021), RL (Avg.pub.year = 2021), and DL (Avg.pub.year = 2022) came later.
Table 15 shows the link strength of the “lighting control system” keyword (which represents the heart of the investigation domain of the present work) with the other 24 keywords. The strength of a link indicates the number of publications in which two keywords occur together (Section 2.1). In this case, one of the two keywords was “lighting control system”. The rows of Table 15 are sorted by decreasing values of the VOSviewer Link strength attribute. A comment restricted to the first three rows of the table follows: In the extant literature about LSs, there is a consolidated connection between the “lighting control system” keyword and LED technology to limit energy consumption. Such a goal has been largely investigated by combining artificial light with “daylight”.

4.2.5. Comparison of the Two Paths

Hereafter, Path 1 denotes the approach without the thesaurus, while Path 2 is the approach with the thesaurus. The comparison of the two paths with respect to the number of thematic clusters and the number of keywords that composed the thematic network returned by VOSviewer is given as follows: 16 clusters (Path 1) vs. 3 clusters; and 1089 keywords (Path 1) vs. 25 keywords. The strong reduction in the number of keywords in Path 2 with respect to Path 1 differently impacted on the values of the Links and Occurrences VOSviewer attributes (Table 16). Indeed, in the second case compared to the first, the value of the Links attribute dropped considerably, while the value of the Occurrences attribute increased significantly. The explanation of these numbers is given in the previous two sub-sections.

5. Limitations

The present BA suffers from two limitations. Firstly, the conclusions were shaped by the bibliometric data retrieved from the Scopus database that it relied upon. The reasons behind such a choice have been explained in Section 3 and supported by prior works; nevertheless, the possibility of missing out LS articles that may have been published in locations not indexed by Scopus cannot be excluded. The other limitation came from restricting the BA to the co-occurrences of the authors’ keywords. The latter technique, by definition, misses information about future research directions. This shortcoming is not relevant from the perspective of the stakeholders of the present study, while it is often an integral part of BAs.

6. Conclusions

The Scopus database was queried to reveal the research on LSs within the 1995–2024 time frame. A total of 12,148 items were retrieved. To map this huge production of knowledge, a co-occurrence analysis of the authors’ keywords was conducted. Three thematic clusters were returned by VOSviewer. Their interpretation allowed answering the following two research questions.
RQ1. What are the major topics explored by scholars in connection with LSs in the 1995–2024 time frame?
RQ2. How do they group together?
Inspired by previous research, this review also aimed to answer a third relevant research question:
RQ3. What is the impact of a thesaurus of authors’ keywords and the T value on clustering effectiveness?
The summary of the key findings of the present work follows:
  • (Answer to RQ1) The trade-off between the numerical value of four independent methods belonging to the family of clustering validity approaches and the authors’ evaluation of a meaningful partition of the input dataset resulted in three independent clusters, collecting 25 authors’ keywords. The most relevant topics in Cluster 1 were “led lighting system” and “energy management”; while the most relevant topics in Cluster 2 were “IoT”, “smart building”, “street light control system”, “WSN”, “Arduino”, and “MCU”. Finally, the most relevant topics in Cluster 3 were “smart city, “computer vision”, “image processing”, “ML”, “DL”, and “RL”.
  • (Answer to RQ2) Most of the research about LSs within the 1995–2024 time frame was devoted to proposing ways to deploy sustainable smart LSs by combining LED technology with the IoT and AI. This finding is in line with the conclusions of two recent systematic literature reviews [2,51].
  • (Answer to RQ3) It was proven that, in absence of a thesaurus of authors’ keywords, the interpretation of the thematic clusters returned by VOSviewer became difficult due to the simultaneous presence of synonyms, out of scope keywords, and too-generic keywords. The only way to prevent this situation consisted in making use of the thesaurus. Regarding the choice of the T value, this work implemented recommendations from previous research in the field of clustering validity approaches.
  • Regarding the construction of the thesaurus, it is worth noting that this is an iterative process. The factors to be taken into account in the workflow are the following: It is fundamental to keep the T value low, in order to include a large number of documents. In the present review, the authors started from 12,148 documents retrieved from Scopus. Here, the issue that must be addressed is making the realization of a thesaurus manageable, since the task is not fully automatic. In the present study, T was set to 5. By setting T = 3, the number of keywords to be considered in the construction of the thesaurus rose from 1096 to 4693, which made the manual work much more tedious and error-prone.

Author Contributions

Conceptualization, P.D.F. and G.P.; methodology, P.D.F. and G.P.; validation, A.P., R.P. and J.C.; formal analysis, P.D.F.; investigation, P.D.F., G.P., A.P., R.P. and J.C.; data curation, F.P.; writing—original draft preparation, P.D.F. and F.P.; visualization, R.P. and F.P.; supervision, F.P. and G.P.; funding acquisition, G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available from the authors.

Conflicts of Interest

Author Gaetanino Paolone, Andrea Piazza, and Jacopo Camplone are employed by the company B2B S.r.l. Author Francesco Pilotti and Romolo Paesani are employed by the company Gruppo SI S.c.a.r.l. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
ANN(s)Artificial Neural Network(s)
CNNConvolution NN
CVComputer Vision
DNNDeep Neural Network
DLDeep Learning
DRLDeep Reinforcement Learning
FLFederated Learning
IoTInternet of Things
LDRLight Dependent Resistor
LEDLight Emitting Diode
LS(s)Lighting System(s)
MCUMicrocontroller Unit
MLMachine Learning
MQTTMessage Queuing Telemetry Transport
NN(s)Neural Network(s)
PIRPassive Infrared
PSOParticle swarm optimization
RFRandom Forest
RLReinforcement Learning
RNN(s)Recurrent Neural Network(s)
SMESmall and Medium-sized Enterprise
SMLSupervised ML
WSNWireless Sensor Network

Appendix A. The Thesaurus

labelreplace by
2D code
2D code
2D materials
3D display
3D printing
3D reconstruction
6lowpan
absorption
accident prevention
active clamp circuit
adaptive
adaptive control
adaptive driving beam
adaptive front-lighting system
adaptive lighting
adaptive lighting system
adaptive optics
adaptive systems
adaptive traffic light control
adaptive traffic signal control
additive manufacturing
afs
agriculture
ahp
ai
aircraft
airfield lighting
airports
algae
algorithm
algorithms
ambient intelligence
android
android applicationandroid application
ansys
anthocyanin
antioxidant
antioxidant capacity
application
applications
approach lighting system
arabidopsis
arabidopsis thaliana
architectural lighting
architecture
arduinoarduino
arduino microcontrollerarduino
arduino nanoarduino
arduino unoarduino
arm
artificial intelligenceartificial intelligence
artificial lightartificial lighting
artificial light at nightartificial lighting
artificial lightingartificial lighting
artificial lighting system
artificial neural networkANN
artificial neural networksANN
artificial neural networks (anns)ANN
augmented reality
automatic control
automation
automotive
automotive lighting
automotive lighting system
autonomous distributed control
autonomous vehicles
availability
azobenzene
ballast
battery
beam shaping
behavior
behaviour
bidirectional converters
big data
bi-level optimization
biodynamic lighting
biomass
blue light
bluetoothbluetooth
boost converter
bright light
brightness
broiler
buck converter
buck-boost converter
building
building automationSmart building
building energybuilding energy management
building energy consumptionbuilding energy management
building energy efficiencybuilding energy management
building energy managementbuilding energy management
building energy management systembuilding energy management
building energy savingbuilding energy management
building envelope
building information modeling
building performance simulation
building simulation
buildings
calibration
camera
can bus
carbon emission reduction
carbon emissions
carbon footprint
carotenoids
case studies
case study
cc2530
cct
cellular automata
cfd
cfl
charge controller
children
chlorophyll
chlorophyll fluorescence
chromaticity coordinates
circadian
circadian clock
circadian light
circadian lighting
circadian rhythm
circadian rhythms
circadian stimulus
classification
climate change
cloudcloud computing
cloud computingcloud computing
clustering
cmos
co2 emission
co2 emissions
color
color controllighting control system
color mixinglighting control system
color renderinglighting control system
color rendering indexlighting control system
color temperaturelighting control system
colorimetrylighting control system
colour
colour temperaturelighting control system
comfortUser comfort
commercial buildings
communication
compact fluorescent lamp
compact fluorescent lamp (cfl)
component
computational fluid dynamics
computational fluid dynamics (cfd)
computer simulation
computer visioncomputer vision
conflict resolution
congestion
connected lighting
connected lighting systems
conservation
construction
consumption
context-aware
control
control algorithm
control circuit
control network
control strategies
control strategy
control system
control systems
controlled environment
controlled environment agriculture
controller
controlling
controls
convolutional neural networkCNN
convolutional neural networksCNN
cooling
cop1
correlated color temperaturelighting control system
correlated color temperature (cct)lighting control system
correlated colour temperaturelighting control system
cost
cost analysis
cost benefit analysis
COVID-19
cri
cryptochrome
cultivation
cultural heritage
cyanobacteria
cyber-physical systems
daily light integraldaylight
dali
data fusion
data-driven
daylightdaylight
daylight factorDaylight
daylight harvestingDaylight
daylight simulationDaylight
daylightingDaylight
dc lighting
dc/dc converters
dc-dc converter
dc-dc converters
dc-dc power conversion
dc-dc power converters
decision making
deep learningDL
deep neural networksDL
deep q-learningdeep q-learning
deep q-networkdeep q-learning
deep reinforcement learningDRL
deep reinforcement learning (drl)DRL
defect detection
degradation
delirium
demand response
demand side management
dementia
design
development
dialux
dialux evo
dialux evo software
diffraction
diffuser
diffusers
digital control
digital twinDigital twin
dimmer
dimmingdimming control
dimming controldimming control
dimming leveldimming control
direct current
discomfort glare
discontinuous conduction mode (dcm)
displays
distributed control
distributed generation
distributed systems
dna
domotics
driver
driving simulator
dynamic control
dynamic lightlighting control system
dynamic light controllighting control system
dynamic lightinglighting control system
dynamic traffic light control
economic analysis
economic evaluation
edge computingedge computing
edge detection
eeg
efficacy
efficiency
efficiency evaluation
efficient lightingenergy management
electric lighting
electrical energy
electrical lighting
electricity
electricity consumptionenergy management
electrochromism
electroluminescence
electrolytic capacitor
electromagnetic interference
electromagnetic interference (emi)
electronic ballast
electronic ballasts
embedded systemembedded system
embedded systemsembedded system
emergency lightingemergency lighting
emergency vehicle
emergency vehicles
emission
emission reduction
emissions
energy
energy audit
energy certification
energy conservation
energy consumption
energy conversion
energy demand
energy efficiency
energy efficiency in buildings
energy efficient
energy efficient lightingenergy management
energy harvestingenergy management
energy managementenergy management
energy management systemenergy management
energy modeling
energy optimizationenergy management
energy performanceenergy management
energy policy
energy retrofit
energy savingenergy management
energy savingsenergy management
energy simulation
energy storage
energy useenergy management
energy use efficiencyenergy management
energy-efficiency
energyplus
energy-savingenergy management
environment
environmental monitoringenvironmental sustainability
environmental protectionenvironmental sustainability
environmental sustainabilityenvironmental sustainability
esp8266
ethernet
evaluation
experimental measurements
experimental study
face recognitionface recognition
fatigue
fault detection
fault tolerance
feedback
feedback control
fiber optic
fiber optic lighting
fiber optics
field study
flavonoids
flicker
flowering
fluorescencefluorescent lamp
fluorescentfluorescent lamp
fluorescent lampfluorescent lamp
fluorescent lampsfluorescent lamp
flyback
flyback converter
fog computingfog computing
formal methods
formal verification
fpgaFPGA
freeform optics
fresnel lens
fuzzy controlfuzzy control
fuzzy controllerfuzzy control
fuzzy logicfuzzy control
fuzzy logic controllerfuzzy control
gallium nitride
game theory
gas exchange
gene expression
genetic algorithmgenetic algorithm
genetic algorithmsgenetic algorithm
genetic code expansiongenetic algorithm
germination
gis
glare
glass
gold nanoparticles
gprs
gps
graphene
graphical user interface
green buildinggreen building
green buildingsgreen building
green energy
green lightDaylight
green lightingDaylight
green technology
green wave
greenhousegreen building
greenhouse gases
greenhousesgreen building
growth
growth chamber
gsm
gsm module
harmonic
harmonics
haul road
headlamp
headlamps
health
heat sink
heat transfer
heating
hid
hid lamps
high power ledLED
high pressure sodiumhigh pressure sodium lamp
high pressure sodium lamphigh pressure sodium lamp
high-pressure sodium lamphigh pressure sodium lamp
highway
historical building
historical buildings
holography
home
home automationhome automation
home energy management systemSmart building
horticulture
hospitals
hps
hps lamp
human centric lightinghuman centric lighting
human computer interaction
human detectionmotion detection
human-centric lightinghuman centric lighting
hvac
hy5
hybrid lightinghybrid lighting system
hybrid lighting systemhybrid lighting system
hybrid system
hydroponics
hyperspectral imaging
ieee 802.15.4
illuminanceillumination control
illuminance distributionillumination control
illuminance levelillumination control
illuminance sensorillumination sensor
illuminance uniformityillumination control
illuminationillumination control
illumination controlillumination control
illumination designillumination control
illumination sensingillumination sensor
illumination systemillumination control
image acquisitionimage processing
image analysisimage processing
image processingimage processing
image segmentationimage processing
incandescent
india
indoor air quality
indoor environment
indoor environmental quality
indoor farming
indoor lightingindoor lighting system
indoor lighting systemindoor lighting system
indoor localization
industrial lightingindustrial lighting
industry
infrared
infrared sensorinfrared sensor
infrared sensorsinfrared sensor
integrative lighting
intelligent
intelligent building
intelligent buildings
intelligent control
intelligent lightSmart lighting control system
intelligent lightingSmart lighting control system
intelligent lighting controlSmart lighting control system
intelligent lighting systemSmart lighting control system
intelligent lighting systemsSmart lighting control system
intelligent street lightingSmart lighting control system
intelligent system
intelligent systems
intelligent traffic
intelligent traffic control
intelligent traffic light
intelligent traffic light control
intelligent traffic system
intelligent transport system
intelligent transport systems
intelligent transportation
intelligent transportation system
intelligent transportation system (its)
intelligent transportation systems
intensity
interaction design
interactive lighting
interface
interior lightingIndoor lighting system
interior lighting systemIndoor lighting system
internet of thingsIoT
internet of things (iot)IoT
internet of vehicles
internet-of-thingsIoT
intersection
intersections
inverter
iotIoT
ir sensorPIR sensor
irradiance
its
junction temperature
knx
label
labview
lamp
lamps
laser
laser diode
lca
lcd
ldrLDR sensor
ldr sensorLDR sensor
ledLED lighting system
led (light emitting diode)LED lighting system
led arrayLED lighting system
led dimmingLED lighting system
led driverLED lighting system
led driversLED lighting system
led illuminationLED lighting system
led lampLED lighting system
led lampsLED lighting system
led lightLED lighting system
led light sourceLED lighting system
led lightingLED lighting system
led lighting controlLED lighting system
led lighting systemLED lighting system
led lighting systemsLED lighting system
led lightsLED lighting system
led luminaireLED lighting system
led luminairesLED lighting system
led matrixLED lighting system
led street lightingLED lighting system
led systemLED lighting system
led systemsLED lighting system
led technologyLED lighting system
ledsLED lighting system
leni
lenses
lettuce
library
life
life cycle assessment
life cycle cost
life cycle cost analysis
life-cycle assessment
lifetime
li-fi
light
light controllighting control system
light control film
light control systemlighting control system
light controller
light dependent resistorLDR sensor
light dependent resistor (ldr)LDR sensor
light dimmingDimming control
light distribution
light emitting diodeLED lighting system
light emitting diode (led)LED lighting system
light emitting diodesLED lighting system
light emitting diodes (leds)LED lighting system
light environment
light guide
light intensity
light pipe
light pollution
light quality
light regulationlighting control system
light scattering
light sensorlight sensor
light sensorslight sensor
light shelf
light signaling
light source
light sources
light spectrum
light therapy
light-control
light-emitting diodeLED lighting system
light-emitting diode (led)LED lighting system
light-emitting diodesLED lighting system
light-emitting diodes (led)LED lighting system
light-emitting diodes (leds)LED lighting system
lighting
lighting comfortUser comfort
lighting conditionslighting control system
lighting controllighting control system
lighting control systemlighting control system
lighting control systemslighting control system
lighting controlslighting control system
lighting design
lighting efficiencyenergy management
lighting energyenergy management
lighting qualitylighting control system
lighting retrofit
lighting simulation
lighting simulations
lighting systemlighting control system
lighting system design
lighting systemslighting control system
lighting technology
lights
linear programming
liquid crystal
liquid crystal display
liquid crystals
localization
long lifetime
lonworks
loraLoRa
lorawan
low power consumptionenergy management
lumen maintenance
luminaire
luminaires
luminance
luminescence
luminous efficacyUser comfort
luminous environment
luminous flux
luminous intensity
lux
m2m
machine learningML
machine visionComputer vision
maintenancemaintenance
management
management system
markov decision process
matlab
maximum power point tracking
mcuMCU
measurement
melanopsin
melatonin
mesopic vision
metamaterials
metasurface
metasurfaces
microalgae
microcontrollerMCU
microcontrollersMCU
microgrid
micropropagation
mobile applicationmobile application
mobility
model
model checking
model predictive control
modeling
modelling
monitoring
monitoring system
motion detectionmotion detection
motion sensor
mpptMPPT
mqttMQTT
multi agent systemmulti agent system
multi-agentmulti agent system
multi-agent reinforcement learningmulti agent system
multi-agent systemmulti agent system
multi-agent systemsmulti agent system
multi-objective optimization
multiple intersections
museum
museum lightingmuseum lighting system
myopia
nanocrystals
nanomaterials
nanoparticles
nanophotonics
natural convection
natural lightDaylight
natural lightingDaylight
natural ventilation
nb-iotNB-IoT
negotiation
network
neural networkNN
neural networksNN
nitric oxide
nodemcu
node-red
nonimaging optics
non-imaging optics
nonlinear optics
non-visual effects of light
numerical simulation
object detectioncomputer vision
occupancyoccupant behavior
occupancy and daylight adaptationoccupant behavior
occupancy detectionmotion detection
occupancy sensingoccupant behavior
occupancy sensorsoccupant behavior
occupant behavioroccupant behavior
occupant behaviouroccupant behavior
off-grid
office
office building
office buildings
office environment
office lightingoffice lighting
offices
oled
oleds
open source
opencv
optical
optical communication
optical communications
optical design
optical fiber
optical fibers
optical properties
optical system
optics
optimal control
optimisation
optimization
optochemical biology
optoelectronics
optogenetics
organic light emitting diodes
outdoor lightingoutdoor lighting system
packaging
par
particle swarm optimizationPSO
pattern recognition
payback period
pedestrian
pedestrian crossing
pedestrian detectionmotion detection
pedestrian safety
perception
performance
performance evaluation
pervasive computingubiquitous computing
petri net
petri nets
phenolic compounds
phosphor
phosphors
photobiology
photobioreactor
photocatalysis
photochemistry
photodynamic therapy
photoluminescence
photometry
photomorphogenesis
photonic crystal
photonics
photoperiod
photopharmacology
photoreceptor
photoreceptors
photosensors
photosynthesis
photosynthetic photon flux density
photosynthetically active radiation (par)
phototaxis
photovoltaic
photovoltaic (pv)
photovoltaic cell
photovoltaic cells
photovoltaic system
photovoltaic systems
photovoltaics
phytochrome
pic microcontrollerPIC MCU
pirPIR sensor
pir sensorPIR sensor
plant factory
plant growth
plant lightingplant lighting
plants
plasmonics
plc
poe
polarization
polarized invisible code
polarized light control
polymer-dispersed liquid crystal
position estimation
poultry
power
power consumption
power electronics
power factor
power factor correction
power factor correction (pfc)
power led
power leds
power line communication
power managementenergy management
power quality
power savingenergy management
ppfd
precision agriculture
prediction
predictive control
predictive maintenancepredictive maintenance
principal component analysis
privacy
production
productivity
public lightingpublic lighting system
public lighting systempublic lighting system
pulse width modulation
pulse width modulation (pwm)
pv
pv panel
pv system
pwm
pwm dimming
pythonpython
q-learningq-learning
quality
quality control
quantum dots
radar
radiance
radiated emission
radiometry
rare earths
raspberry piraspberry pi
ray tracing
reactive oxygen species
reactive power
real-time
real-time control
real-time monitoring
real-time systems
red light
reflectance
reflection
reflective
reflector
refractive index
regression analysisregression analysis
regulation
reinforcement learningRL
reinforcement learning (rl)RL
relays
reliability
remote controlremote monitoring
remote monitoringremote monitoring
remote sensing
renewable energies
renewable energy
renewable energy sources
renovation
requirements engineering
retina
retrofit
retrofitting
return on investment
rfid
rgb
risk
road lightingPublic lighting system
road safety
road traffic congestion
road tunnel
roadway lightingPublic lighting system
robustness
rural electrification
safety
saving energyenergy management
scada
scattering
scheduling
school buildingspublic building
secondary metabolites
secondary optics
securitysecurity
segmentation
self-assembly
self-powered
sensitivity analysis
sensor
sensor fusion
sensor network
sensor networks
sensors
servo motor
shading
signal control
signal transduction
signalized intersection
simulation
simulation of urban mobility (sumo)
simulink
single chip microcomputer
single intersection
single stage
skyglow
sleep
sleep quality
smart
smart buildingsmart building
smart buildingssmart building
smart citiessmart city
smart citysmart city
smart control
smart environments
smart gridsmart grid
smart gridssmart grid
smart homesmart building
smart homessmart building
smart light
smart lightingsmart lighting control system
smart lighting controlsmart lighting control system
smart lighting systemsmart lighting control system
smart lighting systemssmart lighting control system
smart lightssmart lighting control system
smart meters
smart street lightsmart street lighting system
smart street lightingsmart street lighting system
smart street lighting systemsmart street lighting system
smart street lightssmart street lighting system
smart streetlightsmart street lighting system
smart systems
smart traffic
smart traffic light
smart traffic lights
smart transportation
smart window
smart windows
smartphone
soft-switching
software
software goniophotometer
solar
solar cell
solar cells
solar collector
solar concentrator
solar energyDaylight
solar lightingDaylight
solar lighting systemDaylight
solar panel
solar photovoltaic
solar power
solar radiationDaylight
solar street lightingsmart street lighting system
solid state lighting
solid-state lighting
spatial light modulator
spectral power distribution
spectrum
stability
stadium
standards
statistical analysis
stm32STM32
stray light
stray light analysis
stray light controllighting control system
street lamp
street lightstreet lighting control system
street light control systemstreet lighting control system
street lightingstreet lighting control system
street lighting controlstreet lighting control system
street lighting systemstreet lighting control system
street lighting systemsstreet lighting control system
street lightsstreet lighting control system
streetlightstreet lighting control system
streetlightsstreet lighting control system
stress
structured light
style
sumo
sunlightdaylight
supercapacitor
supplemental lighting
surface plasmon resonance
surgical lighting
survey
sustainabilitysustainability
sustainable buildingsmart building
sustainable developmentsustainability
sustainable energyenergy management
sustainable lightingenergy management
switched-mode power supply
synthetic biology
system
system design
system reliability
technology
telescope
temperature
temperature sensor
temporal light modulation
thd
thermal analysis
thermal comfort
thermal conductivity
thermal design
thermal management
thermal resistance
thin films
thingspeak
tomato
total harmonic distortion
total harmonic distortion (thd)
total internal reflection
tracking
traffic
traffic congestion
traffic control
traffic control systems
traffic density
traffic flow
traffic light
traffic light control
traffic light control (tlc)
traffic light control system
traffic light control systems
traffic light controller
traffic light system
traffic lights
traffic lights control
traffic management
traffic monitoring
traffic network
traffic optimization
traffic safety
traffic signal
traffic signal control
traffic simulation
transcription
transfer learning
transition metal dichalcogenides
transmittance
transportation
triboelectric nanogenerator
tunable
tunnel lightingtunnel lighting system
tunnel lighting systemtunnel lighting system
tunnel management
uav
ubiquitous computingubiquitous computing
ultrasonic sensor
ultraviolet
ultraviolet radiation
unidirectional observation
unified glare rating
uniform illuminationUser comfort
uniformity
urban intersections
urban lightingPublic lighting system
urban mobility
urban traffic
urban traffic control
urbanization
user comfortUser comfort
user experience
user interaction
user satisfactionUser comfort
uv-b
validation
vanet
vehicle density
vehicle detection
vehicle safety
vehicles
vehicular communications
vehicular networks
ventilation
vertical farming
vertical illuminance
vhdl
virtual prototyping
virtual reality
visibility
visible
visible light
visible light communication
visible light communication (vlc)
visible light communications
vision
visual comfortUser comfort
visual fatigue
visual inspection
visual perception
visual performanceUser comfort
visualization
vlc
voltage regulation
waiting time
waste
wavefront shaping
wavelength
welfare
well-being
white led
white leds
white light
wi-fi
wind energy
window signage
windows
wireless
wireless communicationwireless communication
wireless networkWsn
wireless networksWsn
wireless sensorWsn
wireless sensor and actuator networkWsn
wireless sensor networkWsn
wireless sensor networksWsn
wireless sensorswsn
wsnwsn
xbee
yield
yolo
zigbeezigbee
zigbee networkzigbee
zigbee technologyzigbee
The thesaurus keeps the distinction between the strings “lighting control system” and “smart lighting control system”. The former refers to authors’ keywords that do not mention the adjectives intelligent or smart to describe the lighting system, while the latter do. The terms smart or intelligent are largely used to refer to lighting control systems that combine the IoT technology with some form of AI [2]. It can certainly be said that the “smart lighting control system” term is more recent than the other term.

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Figure 1. The points corresponding to the 87 keywords returned by VOSviewer.
Figure 1. The points corresponding to the 87 keywords returned by VOSviewer.
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Figure 2. WCSS against the number of clusters.
Figure 2. WCSS against the number of clusters.
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Figure 3. The network of the thematic clusters returned by VOSviewer when T = 5 .
Figure 3. The network of the thematic clusters returned by VOSviewer when T = 5 .
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Figure 4. Cluster 1 in the absence of a thesaurus and T = 5 .
Figure 4. Cluster 1 in the absence of a thesaurus and T = 5 .
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Figure 5. The network of the keyword co-occurrences returned by VOSviewer for T = 70 .
Figure 5. The network of the keyword co-occurrences returned by VOSviewer for T = 70 .
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Figure 6. The boundary of the three clusters for T = 70 .
Figure 6. The boundary of the three clusters for T = 70 .
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Figure 10. The three main topics characterizing the state of the art on the LSs of sustainable cities.
Figure 10. The three main topics characterizing the state of the art on the LSs of sustainable cities.
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Figure 11. Overlay visualization centered on the “lighting control system” keyword.
Figure 11. Overlay visualization centered on the “lighting control system” keyword.
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Table 1. Distribution of papers over the years.
Table 1. Distribution of papers over the years.
YearDocumentsYearDocuments
20249232009301
20238952008263
20228732007245
20217612006221
20207842005172
20198232004151
20187782003116
2017712200296
2016594200189
2015548200091
2014536199985
2013501199884
2012430199774
2011414199693
2010339199582
Table 2. Aggregation of papers by type.
Table 2. Aggregation of papers by type.
Document TypeDocumentsDocument TypeDocuments
Conference Paper5699 (46.9%)Book Chapter286 (2.4%)
Article5786 (47.6%)Review377 (3.1%)
Table 3. The distinct keywords in the thesaurus (NoS = Number of Synonyms).
Table 3. The distinct keywords in the thesaurus (NoS = Number of Synonyms).
KeywordsNoSKeywordsNoS
android application1LoRa1
ANN3maintenance1
arduino4MCU3
artificial intelligence1ML1
artificial lighting3mobile application1
bluetooth1motion detection4
building energy management6MPPT1
cloud computing2MQTT1
CNN2multi agent system5
computer vision3museum lighting system1
daylight15NB-IoT1
deep q-learning2NN2
Digital twin1occupant behavior6
dimming control4office lighting1
DL2outdoor lighting system1
DRL2PIC MCU1
edge computing1PIR sensor3
embedded system2plant lighting1
emergency lighting1predictive maintenance1
energy management21PSO1
environmental sustainability3public building1
face recognition1public lighting system5
fluorescent lamp4python1
fog computing1q-learning1
FPGA1raspberry pi1
fuzzy control4regression analysis1
genetic algorithm3remote monitoring2
green building4RL2
high pressure sodium lamp3security1
home automation1Smart building7
human centric lighting2smart city2
hybrid lighting system2smart grid2
illumination control8smart lighting control system11
illumination sensor2smart street lighting system6
image processing4STM321
indoor lighting system4street lighting control system9
industrial lighting1sustainability2
infrared sensor2tunnel lighting system2
IoT4ubiquitous computing2
LDR sensor4user comfort8
LED1wireless communication1
LED lighting system33WSN8
light sensor2zigbee3
lighting control system25
Table 4. Examples of ignored keywords.
Table 4. Examples of ignored keywords.
Out-of-scope keywordsadaptive opticscultural heritage
agriculturetraffic control
aircrafttraffic light control
airportstraffic congestion
algaetraffic light
antioxidanttraffic light control
COVID-19intelligent transportation
Too-generic keywords2D codedesign
2D materialsdriver
3D displayphotosynthesis
3D printingvisible light communication
3D reconstruction biomasspower quality
data-drivenlight
Table 5. Summary of the measurements for varying T values. (NoC = Number of Clusters; NoK = Number of Keywords; DB score = Davies–Bouldin score; Sscore = Silhoutte score; CH score = Calinski–Harabasz score).
Table 5. Summary of the measurements for varying T values. (NoC = Number of Clusters; NoK = Number of Keywords; DB score = Davies–Bouldin score; Sscore = Silhoutte score; CH score = Calinski–Harabasz score).
T NoCNoKDB ScoreSscoreCH Score
511872.4280.03425.090
109732.2990.07526.140
157631.7740.13827.104
207561.1880.18429.540
256501.1840.17022.042
505330.9490.21622.951
604290.6950.30523.381
654270.7610.27920.508
703250.8180.40126.500
753220.8960.34016.611
1003151.3380.0895.621
Table 6. The top five keywords of each cluster in the absence of a thesaurus of authors’ keywords. (NK = Number of keywords; Occ = Occurrences).
Table 6. The top five keywords of each cluster in the absence of a thesaurus of authors’ keywords. (NK = Number of keywords; Occ = Occurrences).
ClusterNKTop Five KeywordsOcc
1160traffic light control183
optimization91
smart city86
reinforcement learning81
machine learning79
2121led lighting156
street lighting139
solar energy91
led driver60
power quality59
3112light control165
solid-state lighting32
optogenetics30
energy efficient26
outdoor lighting26
4 lighting system238
energy saving208
lighting control135
simulation98
energy consumption90
5 leds73
light-emitting diodes55
photosynthesis55
light-emitting diode54
light quality46
6 internet of things174
smart lighting156
iot145
microcontroller79
sensors72
7 lighting381
light78
melatonin32
environment31
circadian rhythm27
8 led434
energy management49
design39
tunnel lighting35
luminance33
9 energy efficiency389
sustainability74
visual comfort56
artificial lighting43
energy audit42
10 light emitting diode60
visible light communication57
intelligent lighting54
safety32
daylight harvesting25
11 illumination93
energy57
efficiency47
building automation43
performance evaluation22
12 sensor46
control system40
pwm35
neural network31
wireless communication29
13 illuminance88
daylight76
light pollution60
dimming45
intelligent lighting system33
14 lighting systems148
zigbee78
intelligent control35
artificial neural networks18
calibration18
15 renewable energy62
public lighting49
light emitting diodes43
photovoltaic37
smart grid33
16 oled15
light sources14
cct7
rare earths7
lifetime5
Table 7. The top 30 authors’ keywords in the absence of a thesaurus.
Table 7. The top 30 authors’ keywords in the absence of a thesaurus.
KeywordsOccurrencesLinks
LED434401
energy efficiency389312
lighting381361
lighting system238231
energy saving208231
traffic light control183118
internet of things174207
light control165133
led lighting156170
smart lighting156173
lighting systems148150
Iot145186
street lighting139168
lighting control135164
simulation98122
illumination93122
optimization91129
solar energy91124
energy consumption90136
energy savings88125
illuminance88122
smart city86118
daylighting85107
reinforcement learning8176
machine learning79104
microcontroller79109
light7894
zigbee78103
image processing7788
daylight76104
Table 8. The 160 keywords in Cluster 1. (Occ = Occurrences).
Table 8. The 160 keywords in Cluster 1. (Occ = Occurrences).
KeywordsOccKeywordsOcc
traffic light control183signalized intersection9
optimization91smart transportation9
smart city86traffic signal9
reinforcement learning81waiting time9
machine learning79deep q-network8
image processing77defect detection8
fuzzy logic66intersections8
deep learning64light sensors8
wireless sensor network57smart light8
artificial intelligence47smart traffic lights8
deep reinforcement learning47traffic network8
traffic control46cellular automata7
intelligent transportation system42convolutional neural networks7
machine vision42deep neural networks7
traffic light42deep reinforcement learning (drl)7
computer vision41fog computing7
traffic congestion37fuzzy controller7
traffic lights control36green wave7
traffic signal control32image acquisition7
fuzzy control31intelligent traffic control7
genetic algorithm29intelligent traffic light7
sumo28intelligent transport system7
plc27intelligent transportation7
traffic management26python7
intelligent transportation systems25requirements engineering7
traffic lights25smart streetlight7
adaptive control21smart traffic light7
artificial neural network21traffic light control systems7
fpga21transfer learning7
object detection21vhdl7
vanet21visual inspection7
digital twin20conflict resolution6
neural networks20data fusion6
q-learning20deep q-learning6
traffic light control system20markov decision process6
wsn20multiple intersections6
adaptive traffic light control19pedestrian detection6
traffic flow19predictive maintenance6
congestion17real-time6
fuzzy logic controller17real-time control6
intersection17reflection6
optimal control17scada6
rfid17signal control6
component16single intersection6
its16smart traffic6
multi-agent system16traffic control systems6
particle swarm optimization16traffic monitoring6
traffic16urban traffic6
ambient intelligence15urban traffic control6
embedded system15urbanization6
intelligent transport systems14vehicles6
intelligent transportation system14adaptive systems5
multi-agent14adaptive traffic signal control5
traffic simulation14algorithms5
edge computing13arduino microcontroller5
traffic light controller13bi-level optimization5
transportation13building energy consumption5
big data12context-aware5
camera12distributed systems5
multi-objective optimization12dynamic control5
adaptive11emergency vehicle5
cloud computing11emergency vehicles5
embedded systems11image segmentation5
intelligent systems11intelligent traffic system5
intelligent traffic11internet of vehicles5
motion detection11model5
multi-agent reinforcement learning11multi agent system5
traffic optimization11negotiation5
vehicle detection11privacy5
yolo11real-time systems5
genetic algorithms10reinforcement learning (rl)5
intelligent traffic light control10road traffic congestion5
multi-agent systems10simulation of urban mobility5
opencv10smart street lights5
autonomous vehicles9style5
clustering9traffic light control (tlc)5
edge detection9urban intersections5
petri nets9urban mobility5
quality control9vehicle density5
scheduling9vehicular networks5
Table 10. Examples of synonyms belonging to distinct thematic clusters.
Table 10. Examples of synonyms belonging to distinct thematic clusters.
Synonym KeywordsCluster
lighting system4
lighting systems14
led8
leds5
led lighting2
light-emitting diode5
light-emitting diodes5
light emitting diode10
light emitting diodes15
Table 11. The 25 keywords returned by VOSviewer for T = 70 , sorted by cluster number. (TLS = Total link strength; Occ = Occurrences).
Table 11. The 25 keywords returned by VOSviewer for T = 70 , sorted by cluster number. (TLS = Total link strength; Occ = Occurrences).
KeywordsClusterLinksTLSOcc
led lighting system1225931282
lighting control system124556862
energy management124481522
daylight120292372
illumination control119187222
user comfort116135130
public lighting system11599113
sustainability1135288
artificial lighting1125872
dimming control1137471
iot224364382
smart lighting control system222292310
street lighting control system222189236
smart building220165166
wsn216153146
fuzzy control21996120
arduino2149595
mcu2177995
zigbee21610889
smart city318146148
computer vision31359101
image processing3155695
rl393086
ml3145480
dl3155371
Table 12. The 25 keywords returned by VOSviewer for T = 70 , sorted by Occ value. (Occ = Occurrences).
Table 12. The 25 keywords returned by VOSviewer for T = 70 , sorted by Occ value. (Occ = Occurrences).
KeywordsOccKeywordsOcc
led lighting system1282public lighting system113
lighting control system862computer vision101
energy management522arduino95
iot382mcu95
daylight372image processing95
smart lighting control system310zigbee89
street lighting control system236sustainability88
illumination control222rl86
smart building166ml80
smart city148artificial lighting72
wsn146dimming control71
user comfort130dl71
fuzzy control120
Table 13. Number of links among the 25 keywords.
Table 13. Number of links among the 25 keywords.
KeywordsLinksKeywordLinks
lighting control system24 (100%)user comfort16 (66.7%)
energy management24 (100%)zigbee16 (66.7%)
IoT24 (100%)public lighting system15 (62.5%)
led lighting system22 (91.7%)image processing15 (62.5%)
smart lighting control system22 (91.7%)DL15 (62.5%)
street lighting control system22 (91.7%)Arduino14 (58.3%)
daylight20 (83.3%)ML14 (58.3%)
smart building20 (83.3%)computer vision13 (54.2%)
illumination control19 (79.2%)sustainability13 (54.2%)
fuzzy control19 (79.2%)dimming control13 (54.2%)
smart city18 (75.0%)artificial lighting12 (50.0%)
MCU17 (70.8%)RL9 (37.5%)
Table 14. The average publication year of the articles included in the BA (AVG = Avg.pub.Year).
Table 14. The average publication year of the articles included in the BA (AVG = Avg.pub.Year).
KeywordAVGKeywordAVG
image processing2015.5158street lighting control system2017.9873
wsn2015.9795public lighting system2018.4159
lighting control system2016.2193artificial lighting2018.5139
dimming control2016.2394smart lighting control system2018.5613
illumination control2016.3649smart building2018.6265
daylight2016.5108smart city2020.0405
computer vision2016.7030sustainability2020.1477
user comfort2016.9538iot2020.5288
zigbee2016.9551arduino2020.7263
led lighting system2016.9633rl2021.1047
energy management2017.1494ml2021.3750
mcu2017.4421dl2022.0704
fuzzy control2017.6500
Table 15. Link strength of the “lighting control system” keyword with the remaining 24 keywords.
Table 15. Link strength of the “lighting control system” keyword with the remaining 24 keywords.
KeywordLink StrengthKeywordLink Strength
LED Lighting system129Zigbee10
Energy management99MCU9
daylight45Sustainability8
illumination control44arduino8
smart building33computer vision8
user comfort29dimming control8
smart lighting control system26artificial lighting7
IoT25RL3
WSN24smart city3
fuzzy control11ML2
image processing11street lighting control system2
public lighting system10DL2
Table 16. Comparison of the 20 top authors’ keywords in the two paths. (Occ = Occurrences).
Table 16. Comparison of the 20 top authors’ keywords in the two paths. (Occ = Occurrences).
Without ThesaurusLinksOccWith ThesaurusLinksOcc
led401434led lighting system701282
energy efficiency312389lighting control system69862
lighting361381energy management58522
lighting system231238iot63382
energy saving231208daylight42372
traffic light control118183smart lighting control system57310
internet of things207174street lighting control system45236
light control133165illumination control34222
led lighting170156smart building44166
smart lighting173156smart city42148
lighting systems150148wsn34146
iot186145user comfort33130
street lighting168139fuzzy control35120
lighting control164135public lighting system25113
simulation12298computer vision24101
illumination12293image processing2395
optimization12991arduino2795
solar energy12491mcu3695
energy consumption13690zigbee3589
energy savings12588sustainability1688
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Paolone, G.; Piazza, A.; Pilotti, F.; Paesani, R.; Camplone, J.; Di Felice, P. A Map of the Research About Lighting Systems in the 1995–2024 Time Frame. Computers 2025, 14, 313. https://doi.org/10.3390/computers14080313

AMA Style

Paolone G, Piazza A, Pilotti F, Paesani R, Camplone J, Di Felice P. A Map of the Research About Lighting Systems in the 1995–2024 Time Frame. Computers. 2025; 14(8):313. https://doi.org/10.3390/computers14080313

Chicago/Turabian Style

Paolone, Gaetanino, Andrea Piazza, Francesco Pilotti, Romolo Paesani, Jacopo Camplone, and Paolino Di Felice. 2025. "A Map of the Research About Lighting Systems in the 1995–2024 Time Frame" Computers 14, no. 8: 313. https://doi.org/10.3390/computers14080313

APA Style

Paolone, G., Piazza, A., Pilotti, F., Paesani, R., Camplone, J., & Di Felice, P. (2025). A Map of the Research About Lighting Systems in the 1995–2024 Time Frame. Computers, 14(8), 313. https://doi.org/10.3390/computers14080313

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