From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929255390117888 |
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| author | Dias, Tiago Fonseca, Tiago Vitorino, João Martins, Andreia Malpique, Sofia Praça, Isabel |
| author_facet | Dias, Tiago Fonseca, Tiago Vitorino, João Martins, Andreia Malpique, Sofia Praça, Isabel |
| contents | The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants. This work introduces an intelligent city management system that provides a data-driven approach to three use cases: (i) analyze traffic information to reduce the risk of traffic collisions and improve driver and pedestrian safety, (ii) identify when and where energy consumption can be reduced to improve cost savings, and (iii) detect maintenance issues like potholes in the city's roads and sidewalks, as well as the beginning of hazards like floods and fires. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights that enhance security, energy efficiency, and sustainability, while highlighting the potential of AI and IoT-driven solutions for smart city development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_04653 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities Dias, Tiago Fonseca, Tiago Vitorino, João Martins, Andreia Malpique, Sofia Praça, Isabel Machine Learning Computer Vision and Pattern Recognition Systems and Control The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants. This work introduces an intelligent city management system that provides a data-driven approach to three use cases: (i) analyze traffic information to reduce the risk of traffic collisions and improve driver and pedestrian safety, (ii) identify when and where energy consumption can be reduced to improve cost savings, and (iii) detect maintenance issues like potholes in the city's roads and sidewalks, as well as the beginning of hazards like floods and fires. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights that enhance security, energy efficiency, and sustainability, while highlighting the potential of AI and IoT-driven solutions for smart city development. |
| title | From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities |
| topic | Machine Learning Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2306.04653 |