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IoT and AI-Enabled Smart Pavement for Energy Harvesting and Autonomous De-Icing in Smart Cities

Khan, Ashraf Hossain (2025)

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Khan, Ashraf Hossain
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025052315439
Tiivistelmä
Cities around the world are growing fast and with this growth come new challenges like energy shortages, unpredictable weather and the need for safer roads. To deal with these issues, many cities are turning to smart technologies to improve how infrastructure works. This thesis explores one of those ideas: a smart pavement system that can create its own energy and melt ice automatically. This can help cities save power and make roads safer, especially in winter. The main goal of this research is to design a pavement concept that can collect energy from footsteps and car movements. This would be done using special materials called piezoelectric and triboelectric nanogenerators. These materials can turn pressure and motion into electricity. The energy collected could then be used to power things like sensors, and then heat the pavement and remove ice when it’s cold. The system uses small sensors and Internet of Things (IoT) technology to detect when the temperature is low and ice might form. It can then start a heating process to melt the ice before it becomes dangerous. This thesis includes a review of different energy harvesting technologies and de-icing methods used today. It also looks at current smart pavement systems and their challenges. Based on this information, the thesis proposes a new system that combines both energy harvesting and automatic de-icing features. It also includes IoT sensors to help monitor and control the system in real-time. Although this is a design-level study and not a working prototype, the concept shows great promise.

The smart pavement system presented in this thesis could help cities reduce energy costs, lower accident risks from icy roads, and support sustainable development. The design can be developed further through testing and real-life implementation. It also offers ideas for future studies such as using machine learning to make the system smarter and more efficient.
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