# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 6 Related Works  
The two secondary studies selected through the present SLR constitute the related work. They are recapped below. Ref. [89] is a 23-page-length review which references 74 papers. Neither the period covered by the study, nor the scientific databases queried by authors are declared. It has accumulated 425 citations, up to the end of October 2024. This study summarizes the extant AIoT techniques for smart transportation, where LSs are one of the six components. Specifically, the study talks about smart streetlights. The pillar topics within the frame of the study are road lighting and energy efficiency. Ref. [4] is a remarkable SLR that has accumulated 47 citations up to October 2024. The 42-page-length study covers the time interval from 1993 up to 2021 and references 434 papers taken from Google Scholar. The authors declare that 332 studies (out of 434) are specific to answering their RQs. However, it is worth noticing that the actual number of studies where there is a confluence of IoT and AI/ML in the advancement of LSs goes down to 196, since the first studies belonging to this category date back to 2019 [76; 83].  
The investigation is carried out by answering three RQs (Table 23) whose aim is to learn, first, the topics discussed in studies about smart LSs and then the ML algorithms implemented to improve user comfort. Based on the results of text mining on the keywords of the retrieved studies, [4] classifies them into four main issues (user comfort, light control, lighting network, and energy-consumption reduction), four main implementation domains (smart city, smart home, smart building/office, and smart street lighting), and six main technologies (sensors, LED lights, IoT, intelligence, energy harvesting, and renewable energy). Table 41 links the topics in Table 33 to the previous four main issues. As we can see, 13 topics (out of 17) relate to light control, while 12 (out of 17) relate to user comfort.  
The present SLR:  
* Investigates the state-of-the-art in the adoption of AIoT technology in the development of the next generation of smart LSs leveraging the EdgeML paradigm. The latter term is not mentioned in [4], a topic to which the same authors are currently devoting a lot of effort [74; 91; 93; 95].
* Is orthogonal to [4], in the sense that the findings from our study cross different application domains (smart home/building, smart healthcare, smart city, smart transportation, smart classroom, public safety, smart farm, smart village--Table 36), while [4] focuses on the use of the AIoT to control LSs to increase people's comfort.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Open Issue** \\ \hline Lack of methods for the automatic support of predictive maintenance polices of these systems \\ Protection of personal data of users of these systems \\ Lack of high-quality open-real-world datasets \\ Lack of protection methods against attacks on the integrity of training datasets \\ Leveraging TinyML in the development of these systems \\ Reducing the size of ML models without reducing their accuracy \\ Exploring the usage of federated learning architectures \\ Investigating the current trends coming from the manufacturers’ side \\ \hline \hline \end{tabular}
\end{table}
Table 40: Open issues related to future EdgeML smart LSs.