# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 8 Conclusions  
The authors carried out an SLR to answer the following RQs:  
(RQ1) What is the map of published primary and secondary studies about AIoT-based LSs? (RQ2) What are the main topics addressed for AIoT-based LSs? (RQ3) What are the key ML methods enabling the implementation of AIoT-based LSs? (RQ4) Do the selected studies through RQ3 implement the EdgeML computing paradigm? \begin{table}
\begin{tabular}{l l l} \hline \hline
**ID** & **Topic** & **Main Issue, [4]** \\ \hline
1 & Anomaly detection (intrusion detection) & User comfort \\
2 & Detection of occupancy sensor signal & User comfort, light control \\  & anomalies & User comfort, light control, energy \\
3 & Energy-use behaviour & User comfort, light control, energy \\  & & consumption reduction \\
4 & Energy efficiency & Energy consumption reduction \\
5 & Horticultural lighting system & Light control \\
6 & Human activity recognition & User comfort, light control \\
7 & Personalized service provision & User comfort, light control \\
8 & Highway digitalization & User comfort, light control \\  & Smart lighting system for the learning & User comfort, light control \\
9 & context & User comfort, light control \\
10 & Smart village micro-utilities & User comfort, light control \\
11 & Voice-controlled lighting system & User comfort, light control \\
12 & Edge Computing & Light control, lighting network \\
13 & Image processing & User comfort, light control, energy \\
14 & Model compression & Light control \\
15 & Facial recognition & User comfort, light control, energy \\
16 & Classification performance of ML models & Light control \\
17 & Privacy protection & User comfort \\ \hline \hline \end{tabular}
\end{table} 
Table 41: Topics in Table 33 against the issues in [4]. Overall, 28 publications were selected for analysis in depth. Twenty-one of them came from the Scopus database, while 7 were found by carrying out forward snowballing. From the analysis, we learnt the following:  
* The penetration of the AIoT in the lighting domain is still in an infancy stage. * The top three investigated topics are, in decreasing order, energy efficiency, human activity recognition, and personalized service provision. * The top three application domains are smart home/building, smart healthcare, and smart city. * The Supervised ML methods are the most investigated, followed by the DL methods, then the Ensemble Learning methods. * The Cloud-only computing paradigm is largely the most adopted one. We found that just 7 primary studies included in the SLR propose AIoT-based smart LSs, leveraging an actual cooperation between the Cloud and the edge, so they promote EdgeML. About the future research necessary to promote the EdgeML paradigm in the LS domain, it should focus, primarily, on methods suitable for (a) supporting predictive maintenance, (b) protecting the personal data of the users of these systems, (c) generating high-quality and trustable open-real-world datasets necessary to training ML models, (d) raising the level of protection of these systems against cyber-attacks, (e) promoting the adoption of TinyML-based architectures, (f) reducing the size of ML models without reducing their accuracy, (g) being deployed on a federated learning architecture. 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. This research received no external funding. Not applicable. [PERSON], [PERSON] and [PERSON] were employed by the company B2B S.r.l. ; [PERSON] and [PERSON] were employed by the company Gruppo SI S.c.a.r.l. The authors declare no conflicts of interest.