Development of a Smart City Platform: IoT Integration and AI-Driven Predictive Analytics for Environmental Sustainability
Session
Computer Science and Communication Engineering
Description
The rapid advancement of technology has transformed various aspects of daily life, giving rise to the concept of “smart” systems. From smartphones to smart homes and workplaces, technology has evolved to enhance efficiency and convenience. One of the most significant developments in this domain is the emergence of smart cities, which leverage a network of interconnected IoT devices and sensors to collect real-time data, improving urban management, public services, and the overall quality of life for citizens. This study focuses on the development of a platform for the UBT Smart City, designed to manage and visualize real-time data from various IoT sensors. In addition to providing a flexible solution for smart city management, the platform’s sensor data will be used to train machine learning models for applications such as air quality prediction, energy consumption prediction and agricultural monitoring. These AI-driven insights will further optimize resource usage and promote sustainability initiatives. This research highlights the potential of IoT and AI integration in creating smarter, more sustainable urban environments, and offers practical insights into the implementation of advanced technologies within urban and academic settings.
Keywords:
Smart Cities, IoT, Machine Learning, Real-time Sensor Data, Green IoT, Predictive Analytics
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-15-3
Start Date
25-10-2024 9:00 AM
End Date
27-10-2024 6:00 PM
DOI
10.33107/ubt-ic.2024.394
Recommended Citation
Hajrizi, Edmond; Qehaja, Besnik; Zylfiu, Blerim; and Boshnjaku, Elton, "Development of a Smart City Platform: IoT Integration and AI-Driven Predictive Analytics for Environmental Sustainability" (2024). UBT International Conference. 10.
https://knowledgecenter.ubt-uni.net/conference/2024UBTIC/CS/10
Development of a Smart City Platform: IoT Integration and AI-Driven Predictive Analytics for Environmental Sustainability
The rapid advancement of technology has transformed various aspects of daily life, giving rise to the concept of “smart” systems. From smartphones to smart homes and workplaces, technology has evolved to enhance efficiency and convenience. One of the most significant developments in this domain is the emergence of smart cities, which leverage a network of interconnected IoT devices and sensors to collect real-time data, improving urban management, public services, and the overall quality of life for citizens. This study focuses on the development of a platform for the UBT Smart City, designed to manage and visualize real-time data from various IoT sensors. In addition to providing a flexible solution for smart city management, the platform’s sensor data will be used to train machine learning models for applications such as air quality prediction, energy consumption prediction and agricultural monitoring. These AI-driven insights will further optimize resource usage and promote sustainability initiatives. This research highlights the potential of IoT and AI integration in creating smarter, more sustainable urban environments, and offers practical insights into the implementation of advanced technologies within urban and academic settings.