Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm

Gaetanino Paolone, Romolo Paesani, Francesco Pilotti, Jacopo Camplone, Andrea Piazza and Paolino Di Felice ()
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Gaetanino Paolone: B2B S.r.l., 64100 Teramo, Italy
Romolo Paesani: Gruppo SI S.c.a.r.l., 64100 Teramo, Italy
Francesco Pilotti: Gruppo SI S.c.a.r.l., 64100 Teramo, Italy
Jacopo Camplone: B2B S.r.l., 64100 Teramo, Italy
Andrea Piazza: B2B S.r.l., 64100 Teramo, Italy
Paolino Di Felice: Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy

Future Internet, 2025, vol. 17, issue 2, 1-50

Abstract: Lighting Systems (LSs) play a fundamental role in almost every aspect of human activities. Since the advent of lights, both academia and industry have been engaged in raising the quality of the service offered by these systems. The advent of Light Emitting Diode (LED) lighting represented a giant step forward for such systems in terms of light quality and energy saving. To further raise the quality of the services offered by LSs, increase the range of services they offer, while at the same time consolidating their reliability and security, we see the need to explore the contribution that can be derived from the use of the Artificial Intelligence of Things (AIoT) emerging technology. This paper systematically reviews and compares the state-of-the-art with regard to the impact of the AIoT in the smart LS domain. The study reveals that the field is relatively new, in fact the first works date back to 2019. In addition to that, the review delves into recent research works focusing on the usage of Machine Learning (ML) algorithms in an edge Cloud-based computing architecture. Our findings reveal that this topic is almost unexplored. Finally, the survey sheds light on future research opportunities that can overcome the current gaps, with the final aim of guiding scholars and practitioners in advancing the field of smart LSs. The study is reported in full detail, so it can be replicated.

Keywords: Internet of Things; artificial intelligence; Machine Learning (ML); smart lighting systems; Edge Computing; edgeML; systematic literature review (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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