# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 1 Introduction  
Lighting Systems (LSs) are one of the most revolutionary inventions ever; they have sped up human progress, improving, at the same time, quality of life [1]. LSs are everywhere: at home, in public buildings, inside cities, along streets and highways. The side effect from the massive, and often incorrect, use of LSs is quantified by the huge energy consumption associated with them. In 2022, 25% of EU total energy consumption in households was for electricity (source: [[https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households)]([https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households)) (accessed on 10 November 2024)). [PERSON] et al. [2] report that street lighting has a tremendous impact on energy consumption. In the EU, for instance, street lighting alone accounts about 40% of municipal electricity bills. Today, both academia and industry are engaged in a converging effort to turn LSs intosustainable systems, reducing their energy consumption, increasing at the same time their security, and improving the overall quality of service by preventing outages, preserving citizens' personal data, and also keeping their daily management as easy as possible. The present Systematic Literature Review (SLR) aims at investigating the state-of-the-art in the adoption of Internet of Things (IoT), Machine Learning (ML) methods, and the Edge Computing paradigm in reaching a target that cannot be postponed any further. The research described in this paper is not focused on a specific perspective, in contrast with previous reviews. [PERSON] et al. [3], for example, investigated the effects of smart LSs on energy saving and people's well-being in residential buildings by reviewing studies published in the range January 2001 to February 2021. Ten studies, out of the 13 included in the review, focus on energy outcomes, either alone or combined with people's well-being. In ref. [4], it has been pointed out that despite several studies having surveyed the smart lighting literature, up to the end of 2021 none of them had investigated the adoption of ML algorithms in such an application domain. Moving from such an observation, [PERSON] et al. carried out an in depth SLR looking for ML methods suitable for increasing people's comfort, a socially relevant topic. The SLR covers the period 2014-2021. The present study has the following merits:  
* It reports on an SLR realized with following the well-known method proposed by [PERSON] in 2007 [5]. This method allows one to select, study, and summarize the state-of-the-art with respect to a given set of research questions in a way that is unbiased and (to a large extent) repeatable. It has been remarked that often SLRs are conducted without following a well-defined methodology [6]. * It promotes knowledge transfer from academia to firms. Knowledge transfer is a motivating factor in the economic growth of Small and Medium-sized Enterprises (SMEs) all over the world. In the case of the EU, they represent 99% of all businesses (source: [[https://single-market-economy.ec.europa.eu/smes/sme-fundamentals/sme-definition_en](https://single-market-economy.ec.europa.eu/smes/sme-fundamentals/sme-definition_en)]([https://single-market-economy.ec.europa.eu/smes/sme-fundamentals/sme-definition_en](https://single-market-economy.ec.europa.eu/smes/sme-fundamentals/sme-definition_en)) (accessed on 10 November 2024)). Knowledge transfer between academia and industry has received a lot of attention by scholars [7; 8]. Ref. [8] reports on a review that summarizes the ways in which the transfer of knowledge between academia and industry takes place.