The DOI matches its Crossref record.
A smart city adaptive lighting system
Abstract
This paper presents an urban smart lighting system capable to autonomously control the street lamp lighting level by exploiting data related to vehicles (bus, car, motorcycle and bike) and/or pedestrians traffic in a specific area. The system is able to set the lighting level on the basis of required needs and allows one to reduce energy costs. To this end, it makes use of local controllers, motion sensors, video-camera and electronic devices for video processing. In this way the inputs coming from the sensors is processed in order to optimally dim the lighting by applying an 1–10 V control input voltage to the fixture or driver. The control can be performed either on a decentralized fashion on each single street lamp or on a group of them. Experimental results show that with respect to a traditional street lamp system, the presented architecture allows energy saving up to 65%.
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In the citing paper
“80% of the total energy generated will flow to the smart city grid.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Gagliardi et al. [84]
In the citing paper
“Gagliardi et al. [84] adds that the increase in energy consumption will focus on big cities, not small cities.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Gagliardi et al. [84]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 14
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- 0, DM6437 Digital Video Development Platform
- 10.1109/GHTC.2014.6970303 · DOI
- 10.1109/TPEL.2014.2312024 · DOI
- denardin, 2009, An Intelligent System for Street Light Monitoring and Control, Proceedings of 11th Brazilian Power Electronics Conference
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- 10.2174/2213275910801010032 · DOI
Cited by
Sorted by citations
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“Gagliardi et al. [84] adds that the increase in energy consumption will focus on big cities, not small cities.”
Background
Metadata
- Authors
- Gagliardi, G., Casavola, A., Lupia, M., Cario, G., Tedesco, F., Scudo, F. L., Gaccio, F. C., & Augimeri, A.
- Affiliations
- University of Calabria (IT)
- ORCID
- Venue
- 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 258-263
- OpenAlex
- W2806379381
- Cited by
- 29
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 9 · 2020: 6 · 2019: 2
- FWCI
- 7.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Fog and Mobile Edge Computing (FMEC) (publishedVersion)
- Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
- Topics
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Keywords
- smart cities, adaptive street lighting, energy conservation, traffic detection, vehicle detection, pedestrian detection, motion sensors, video processing, decentralized control, lighting dimming
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