# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 2 Background
### Smart Lighting Systems
Processing the data collected by indoor/outdoor luminaires is a precondition for the following:  
* Understanding lighting-usage patterns by people in order to improve energy efficiency, as noted in [38];
* Carrying out predictive maintenance of LED lights, as emphasized in [2];
* Obtaining fault notifications in real time in order to limit illuminance outages [2]. Previous research has clarified the link between ML and Human Activity Recognition (HAR), Computer Vision (CV), Voice Recognition (VR), and Speech Recognition (SpR), four fields of AI that have a growing importance in modern smart LSs [51; 52; 53]. For example, by using digital images from cameras and DL models, it is possible to train computers to classify objects and then be able to react to what they \"see\". In practical terms, image recognition allows one to modify the light intensity in daily situations because of the human presence in a room or a pedestrian walking along a city street [52]. [PERSON] et al. [53] discuss the transformative influence of CV methods on well-established ML methods with respect to building versatile ML models that leverage the strengths of both disciplines. Ref. [54] discusses an AIoT face-recognition-based LS which potentially can create light conditions which adhere to intensity values initially set by home inhabitants. A review of the state-of-the-art on both traditional and DL-based methods of speaker recognition may be found in [55; 56]. [PERSON] and [PERSON] [57] investigated the effectiveness of Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) classifiers in solving speech-recognition problems. In the smart LS context, SR is frequently used for turning on/off lights in a room to enhance user experience. [PERSON] et al. [4] pointed out that ML together with speech recognition, voice recognition, and face recognition look very promising for enhancing people's comfort. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application Domain** & **Use Case** & **Studies** \\ \hline Smart city & Smart street LS & [39; 40; 41; 42; 43; 44] \\ Smart city & Smart building & [39; 45; 46; 47] \\ Smart education & Smart lab & [48] \\ Smart agriculture & Monitoring of lettuce growth & [49; 50] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recent publications on smart LSs. Smart LSs are a domain of primary importance in the context of the creation of smart cities. The EU, for example, is investing a lot of money to support projects consistent with this objective ([[https://commission.europa.eu/eu-regional-and-urban-development/topics/cities-and-urban-development/city-initiatives/smart-cities_en](https://commission.europa.eu/eu-regional-and-urban-development/topics/cities-and-urban-development/city-initiatives/smart-cities_en)]([https://commission.europa.eu/eu-regional-and-urban-development/topics/cities-and-urban-development/city-initiatives/smart-cities_en](https://commission.europa.eu/eu-regional-and-urban-development/topics/cities-and-urban-development/city-initiatives/smart-cities_en)) (accessed on 10 November 2024)). Manufacturers of smart LS products claim that this technology brings substantial cost savings through energy efficiency and improved maintenance, enhances public safety, and contributes to environmental sustainability goals. The present SLR aims at assessing whether the state-of-the-art of the research confirms all these wonderful promises.