# 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? Table 33 recaps the content of the column \"Topic\" in Table 28, while Table 34 presents the topics in Table 33 against the selected 26 primary studies. The \"+\" in box <_i,j_> of Table 34 denotes that primary study \"_i_\" deals with topic \"_j_\". As we can see, the matrix is very sparse, which denotes that the analyzed studies mostly focus on unrelated topics. The top three investigated topics are, in decreasing order, energy efficiency (13/26), human activity recognition (7/26), and personalized service provision (6/26), while privacy protection occupies the bottom position. Below, we elaborate a bit more on these points. The present SLR:  
* Confirms what is largely reported in previous studies, namely that energy efficiency is the most investigated research topic in connection with smart LSs [4; 98]. * Shows that HAR is playing an increasing role in the domain of smart LCs because it is a preliminary step towards the automation of a variety of functions in smart homes/buildings for lighting control based on human occupancy and/or human actions. * Reveals that personalized service provision is in the third position. Personalized services are services tailored to individual users' interests and preferences. In the case of smart LSs, personalized services mean offering light conditions that meet daily human habits in indoor/outdoor environments in order to increase their comfort. In ref. [4], [PERSON] et al. state that high user comfort is still in the infancy stage. In fact, reaching such an ambitious goal requires further progress in the field of HAR in combination with optimized ML models. Table 35 maps the 17 topics in Table 33 to well-known application domains, while in Table 36 the latter are listed according to their number of occurrences. Hereinafter, we recap how the EdgeML solution is implemented in these 7 primary studies. The synopsis refines the information collected in Tables 28-30. In [80], the authors adopt a Cloud-aided edge RL framework to support the downloading of the global consensus model from the Cloud and integrate it into the edge learning process. The efficiency and effect of applying the downloaded pre-trained model are boosted by applying an input-expansion strategy followed by an output-correction strategy. The approach's effectiveness and performance are shown through experiments on data generated by the open software DAILux. [PERSON] et al. [82] suggest the adoption of the MEC paradigm to implement the smart LS component of a digital highway. The IoT architecture is structured as a network of wireless highway light controllers. Each controller comprises a sensor node and an edge device-based vision node, both embedding a computing unit. The sensor nodes integrate the sensors (e.g., LDR sensors, light dimmers, motion sensors). The edge device-based vision node is responsible for processing the data coming from the sensors and the internal camera by making use of DL algorithms. Then, accordingly, it sends information to the computing unit of the sensor node that, in turn, uses it to adapt the light intensity on the highway lane.