# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 4 Results
### (RQ2) What Are the Main Topics Addressed for AIoT-Based LSs?
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ML Category** & **ML Sub-Category** & **Method Name** & **Primary Studies** \\ \hline \multirow{8}{*}{Supervised Learning} & & DT & [72,90] \\  & & NN & [72,79,85] \\  & & ANN & [88] \\  & & KNN & [74,75,77,81,90,91,93] \\  & & SVM & [75,90] \\  & & LR & [75] \\  & & NB & [77,88,90] \\ \hline \multirow{8}{*}{Ensemble Learning} & Regression & LiR & [72] \\  & & SR & [87] \\ \hline \multirow{2}{*}{Ensemble Learning} & Bagging & RF & [72,73,75,76] \\  & Boosting & Gradient boosting & [73,92] \\  & & XGBoost & [73] \\ \hline \multirow{4}{*}{Deep Learning} & Not specified & & [70,82] \\  & DNN & & [86,91] \\ \cline{1-1}  & CNN & & [88,92,95,96] \\ \cline{1-1}  & Recurrent NN & LSTM & [27,75,94] \\ \hline \multirow{2}{*}{Reinforcement Learning} & \multirow{2}{*}{Evolutionary computing} & PSO & [83,84] \\ \cline{1-1}  & & RF & [71] \\ \cline{1-1}  & & DRL & [80] \\ \hline \hline \end{tabular}
\end{table} 
Table 37: A summary table linking primary studies and ML method. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Edge Cloud-Based (EdgeML)** & \multicolumn{2}{c}{**Cloud-Based**} \\ \hline
[80] & [86] & [77] \\
[82] & [88] & [90] \\
[79] & [81] & [94] \\
[95] & [96] & [70] \\
[91] & [84] & [71] \\
[74] & [75] & [76] \\
[93] & [85] & [87] \\  & [83] & [78] \\  & [72] & [92] \\ \hline \hline \end{tabular}
\end{table} 
Table 38: Aggregation of the selected 26 primary studies by computing paradigm. In ref. [79], [PERSON] et al. propose a low-cost solution to voice-activated LED lighting. As hardware, Raspberry Pi Pico W, a microphone, an LED light bar, the power supply, and the breadboard are used. Raspberry Pi Pico W captures audio inputs from the microphone, processes the audio signals, and controls the LED lights. The LED light bar provides visual feedback according to voice commands. Data collection, data preprocessing, and model training are carried out through the Edge Impulse EON Tuner platform ([[https://docs.edgeimpulse.com/docs/edge-impulse-studio/eon-tuner](https://docs.edgeimpulse.com/docs/edge-impulse-studio/eon-tuner)]([https://docs.edgeimpulse.com/docs/edge-impulse-studio/eon-tuner](https://docs.edgeimpulse.com/docs/edge-impulse-studio/eon-tuner)) (accessed on 10 Novemeber 2024)), which allows model design and optimization on edge devices. Edge Impulse uses an NN classifier to perform voice command recognition and control LED lights. The ML model is trained on a large dataset of voice commands. The NN classifier is responsible for classifying the audios into different classes (e.g., start, stop, noise, and other). [PERSON] et al. [95] propose a way to guarantee the preservation of occupants' privacy in smart LSs. Anonymization is brought through image pixelation. The images are elaborated by means of the CNN model. The adopted solution implements a camera-based smart LS within the Edge Computing architecture. The edge environment is where the images received by Rasppicam are processed. Raspberry Pi 3B+ is used as an edge processor. In ref. [91], the authors introduce an Edge Computing architecture for smart lighting control (EdgeSL) based on the CIMA model presented in [90]. Moreover, they describe a new distillation algorithm of the KNN model (DistilKNN) as a compression method to deploy CIMA in a NodeMCU part of the EdgeSL architecture. Experiments running DistilKNN on NodeMCU returned better performance than those obtained using Cloud computing: (a) best accuracy compared to methods using quantization and pruning, and (b) better average processing time. The work by [PERSON] et al. [74] introduces a variant of the NearCount sampling method (NearCount-PoS) as a compression strategy to reduce the number of samples needed to feed the ML model to be run in an Edge Computing-based smart LS. The MQTT publish/subscribe protocol is used to connect IoT edge devices over the Internet and with the Cloud. NearCount-PoS returns better prediction accuracy than KNN at sample numbers above 9000. [PERSON] et al.