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DIZON, Eisley and PRANGGONO, Bernardi (2021). Smart streetlights in Smart City: a case study of Sheffield. Journal of Ambient Intelligence and Humanized Computing.
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Abstract
Smart streetlights can be used to enhance public safety and well-being. However, not only it is one of the most draining structures in terms of electricity, but it is also economically straining to local government. Typically, many councils adopt a static or conventional approach to street lighting, this presents many inefficiencies as it does not take into account environmental factors such as light levels and traffic flows. This paper will present the utilities of a streetlights in Sheffield and how different councils tackle the issue by using different lighting schemes. Investigation of current implementations of information and communication technologies (ICT) such as Internet of Things (IoT) in streetlights will be necessary to understand different proposed models that are used in ‘smart’ street lighting infrastructure. Case studies from Doncaster and Edinburgh are explored as they are using similar technology and having a similar sized topology as Sheffield. To analyze different models, StreetlightSim, an open-source streetlight simulator, is used to present different lighting schemes. There will be four time-based schemes: Conventional, Dynadimmer, Chronosense and Part-Night which have varying capabilities that will be simulated to present a plethora of solutions for Sheffield’s street lighting problem. The results from the simulations showed mixed readings, the time-based schemes showed reliable data from StreetlightSim’s own evaluations, however its adaptive approach will need to be further analyzed to demonstrate its full capability.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0801 Artificial Intelligence and Image Processing; 0805 Distributed Computing |
| Identification Number: | https://doi.org/10.1007/s12652-021-02970-y |
| SWORD Depositor: | Symplectic Elements |
| Depositing User: | Symplectic Elements |
| Date Deposited: | 26 Feb 2021 10:22 |
| Last Modified: | 17 Mar 2021 14:31 |
| URI: | https://shura.shu.ac.uk/id/eprint/28231 |
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