Cloud-based energy efficient smart street lighting system
Open accessAbstract
Background/Objectives: Pakistan is experiencing significant energy crises for the last two decades. The increasing power demand cannot be controlled with traditional energy techniques. The conventional street lighting system remains ON all over the night. Consequently, a huge amount of energy is wasted. To overcome the shortage of energy, an efficient smart lighting system is proposed. Methods: The prime thought to accomplish is ``Energy on-demand\'\' i.e. handed-in wherever, whenever required. To achieve this goal, the cloudbased smart street lighting system provides a feasible solution. The proposed system uses an Arduino and its various shields based on a movement-based actuation system like Light Detection and Ranging (LiDAR), automate the street lighting system. This design is executed and implemented on a one-way road and its results are carried out at three different scenario cases. The analysis is done by a decision-making module that obtains results from the sensor (LiDAR). Findings: The availability of low cost advanced devices like LiDAR, Arduino, cloud storage, and the accessibility of wired and wireless connection, smart street lighting system becomes a reality. The proposed system is beneficial to overcome the CO2 emission. Further, it will also be helpful to avoid the unnecessary consumption of streetlight as well as to reduce power utilization and save energy up to 99%. Applications: This proposed framework has an immense potential to revolutionize street lighting and to achieve the demand for a smart street lighting system that is easy to maintain, durable, and reliable. Keywords: IoT; cloud; LiDAR; street lighting system; Arduino
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Because street lamps are a necessary facility of a city, the consumption of streetlamps will also affect the economic condition of a country.
“Omar et al. [61] revealed that because street lamps are a necessary facility of a city, the consumption of streetlamps will also affect the economic condition of a country.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Omar et al. [61]
Cites
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
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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 · see citing works
“Omar et al. [61] revealed that because street lamps are a necessary facility of a city, the consumption of streetlamps will also affect the economic condition of a country.”
Background
- Authors
- cholar, D. O. C. S., & Umar, M.
- Affiliations
- Shah Abdul Latif University (PK)
- Corresponding author
- Umar, Muhammad
- Venue
- Indian Journal of Science and Technology
- Published
- 2020-06-20
- Volume
- 13
- Issue
- 23
- Pages
- 2311-2318
- ISSN
- 0974-5645 (ISSN-L)
- 0974-6846
- OpenAlex
- W3040712559
- Cited by
- 8
- References
- 1
- Citations by year
- 2025: 1 · 2023: 4 · 2022: 2 · 2021: 1
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Indian Journal of Science and Technology (publishedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- smart street lighting, energy efficiency, cloud-based control, LiDAR, Arduino, Internet of Things, motion detection, energy conservation, CO2 emission reduction
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