Intelligent control of streetlights based on sensor fusion using fuzzy clustering
This project introduces an Intelligent Streetlight System that seamlessly integrates Cloud and Internet of Things technology to address the inherent limitations of traditional streetlights. Conventional streetlights are renowned for their excessive energy consumption and the absence of automated fau...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2023
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| Online Access: | http://eprints.utar.edu.my/6041/ http://eprints.utar.edu.my/6041/1/fyp_CS_2023_SWC.pdf |
| _version_ | 1848886572647186432 |
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| author | Saw, Wei Chin |
| author_facet | Saw, Wei Chin |
| author_sort | Saw, Wei Chin |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | This project introduces an Intelligent Streetlight System that seamlessly integrates Cloud and Internet of Things technology to address the inherent limitations of traditional streetlights. Conventional streetlights are renowned for their excessive energy consumption and the absence of automated fault detection. To optimize energy utilization while upholding safety standards, the system incorporates sensor fusion technology, which aggregates data from multiple sensors, thereby ensuring more dependable outcomes. This fusion of sensor technology with the Intelligent Streetlight System significantly elevates the system's reliability, availability, and safety. Furthermore, the project implements a meticulously designed fault tolerance mechanism, underpinned by fuzzy rules, enhancing the system's dependability by substantially reducing the likelihood of potential failures. The culmination of this project yields a fully operational prototype of the Intelligent Streetlight System, featuring an embedded fault tolerance architecture based on fuzzy logic. These innovations collectively represent a sustainable, efficient, and highly reliable alternative to conventional streetlighting, offering benefits including reductions in energy consumption, streamlined maintenance processes, and an elevated standard of safety. Significantly, this project seamlessly integrates Cloud Technology and IoT capabilities, facilitating data collection, real-time monitoring, and collaborative data sharing among streetlights, thus enhancing the system's capabilities for urban infrastructure management. This initiative presents an innovative response to pressing urban and rural infrastructure challenges and holds the potential to make substantial contributions to the advancement of sustainable development goals. |
| first_indexed | 2025-11-15T19:40:38Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6041 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:40:38Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-60412024-01-04T14:56:34Z Intelligent control of streetlights based on sensor fusion using fuzzy clustering Saw, Wei Chin T Technology (General) TD Environmental technology. Sanitary engineering This project introduces an Intelligent Streetlight System that seamlessly integrates Cloud and Internet of Things technology to address the inherent limitations of traditional streetlights. Conventional streetlights are renowned for their excessive energy consumption and the absence of automated fault detection. To optimize energy utilization while upholding safety standards, the system incorporates sensor fusion technology, which aggregates data from multiple sensors, thereby ensuring more dependable outcomes. This fusion of sensor technology with the Intelligent Streetlight System significantly elevates the system's reliability, availability, and safety. Furthermore, the project implements a meticulously designed fault tolerance mechanism, underpinned by fuzzy rules, enhancing the system's dependability by substantially reducing the likelihood of potential failures. The culmination of this project yields a fully operational prototype of the Intelligent Streetlight System, featuring an embedded fault tolerance architecture based on fuzzy logic. These innovations collectively represent a sustainable, efficient, and highly reliable alternative to conventional streetlighting, offering benefits including reductions in energy consumption, streamlined maintenance processes, and an elevated standard of safety. Significantly, this project seamlessly integrates Cloud Technology and IoT capabilities, facilitating data collection, real-time monitoring, and collaborative data sharing among streetlights, thus enhancing the system's capabilities for urban infrastructure management. This initiative presents an innovative response to pressing urban and rural infrastructure challenges and holds the potential to make substantial contributions to the advancement of sustainable development goals. 2023-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6041/1/fyp_CS_2023_SWC.pdf Saw, Wei Chin (2023) Intelligent control of streetlights based on sensor fusion using fuzzy clustering. Final Year Project, UTAR. http://eprints.utar.edu.my/6041/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Saw, Wei Chin Intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| title | Intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| title_full | Intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| title_fullStr | Intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| title_full_unstemmed | Intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| title_short | Intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| title_sort | intelligent control of streetlights based on sensor fusion using fuzzy clustering |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6041/ http://eprints.utar.edu.my/6041/1/fyp_CS_2023_SWC.pdf |