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Smart city article details
| Title | Quantitative Impact Of Artificial Intelligence On Smart Cities: A Comparative Study Using Federated Learning |
|---|---|
| ID_Doc | 43968 |
| Authors | Banala S.; Gutta L.M.; Kanchepu N.R.; Bhattacharya P.; Dutta P.; Whig P. |
| Year | 2024 |
| Published | IET Conference Proceedings, 2024, 37 |
| DOI | http://dx.doi.org/10.1049/icp.2025.0853 |
| Abstract | This comparative study investigates the quantitative impact of Artificial Intelligence (AI) on smart cities, with a specific focus on the implementation of federated learning in IoT-based energy management systems. Our research examines AI's effects on urban infrastructure, governance, and public services, while proposing a novel framework that leverages federated learning to enhance privacy, efficiency, and scalability in smart city ecosystems. The study analyzes data from diverse urban settings, revealing significant improvements in key metrics. AI-driven systems have reduced energy consumption by an average of 15% across studied cities, while AI-enhanced traffic management algorithms have decreased congestion by up to 25%. These results demonstrate the substantial operational efficiencies achieved through AI integration in urban environments. Our proposed framework introduces a federated learning approach to address privacy concerns and data silos in smart cities. This decentralized AI technique allows for model training across multiple IoT devices without centralizing sensitive data. The architecture consists of three main components: energy devices, energy edge servers, and energy cloud servers. Energy devices collect and generate data, edge servers process local data and host federated learning models, and cloud servers coordinate the overall learning process and provide additional computational resources when needed. The software model for this IoT-based energy management system includes four layers: sensing, network, cognition, and application. Federated learning is implemented primarily in the cognition layer, where edge servers perform local model updates based on device data, and the cloud server aggregates these updates to improve the global model without accessing raw data. © The Institution of Engineering & Technology 2024. |
| Author Keywords | Artificial Intelligence; governance; infrastructure; public services; quantitative analysis; smart cities; urban development |
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