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Title	Sustainable Urban Street Lighting Through Predictive Maintenance Using Iot And Ai
ID_Doc	54097
Authors	Sudharson D.; Jananiksha P.; Mukilarasu R.; Sushmita V.; Vaishali V.; Rini Vaishnavi S.R.
Year	2024
Published	2nd International Conference on Emerging Research in Computational Science, ICERCS 2024
DOI	http://dx.doi.org/10.1109/ICERCS63125.2024.10895512
Abstract	The potential for transformative urban management is enhanced through the integration of IoT and AI, creating smart ecosystems that revolutionize intelligent lighting systems. This paper explores predictive maintenance solutions for urban street lighting, leveraging advanced machine learning models such as Temporal Fusion Transformer (TFT), Temporal Aggregation and Decomposition-based Attention (TADA), and Graph Neural Networks (GNNs). By analyzing historical operational data, these models detect subtle fault patterns and predict failures, improving system reliability, reducing maintenance costs, and optimizing energy use. The work contributes to United Nations SDGs 11 and 9 by promoting sustainable urban infrastructure and smart city development, fostering a future where IoT and AI systems enhance sustainability and resilience. © 2024 IEEE.
Author Keywords	Artificial Intelligence (AI); Graph Neural Networks (GNNs); Internet of Things (IoT); Smart cities; Sustainability; Temporal Aggregation and Decomposition Attention (TADA); Temporal Fusion Transformer (TFT); Urban Lighting

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