IoT-Driven Street Lighting Optimization Using AI in Urban Areas

Authors

https://doi.org/10.22105/scfa.v1i2.36

Abstract

Urban street lighting systems play a crucial role in ensuring safety and improving the quality of life within cities. Nevertheless, conventional lighting methods often result in inefficiencies, high energy consumption, and greater operational expenses. This paper introduces an Internet of Things (IoT)-based optimization model that leverages Artificial Intelligence (AI) to improve the management of street lighting in urban settings. The suggested system incorporates smart sensors, real-time data analysis, and machine learning techniques to modify lighting levels according to environmental factors and the presence of pedestrians. A case study performed in a mid-sized city revealed a 30% decrease in energy consumption and enhanced lighting quality, which in turn has led to greater public satisfaction. The findings suggest that the application of AI and IoT technologies can substantially improve urban streetlight management, thereby assisting in the development of sustainable cities. This research highlights the groundbreaking potential of intelligent systems in refining urban infrastructure, paving the way for smarter and more efficient urban areas.

Keywords:

Internet of things, Street lighting optimization, Artificial intelligence, Urban infrastructure, Energy efficiency

References

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Published

2024-10-10

Issue

Vol. 1 No. 2 (2024): Soft Computing Fusion with Applications

Section

Articles

How to Cite

IoT-Driven Street Lighting Optimization Using AI in Urban Areas. (2024). Soft Computing Fusion With Applications , 1(2), 112-125. https://doi.org/10.22105/scfa.v1i2.36

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