Authors:
Anjan Kumar Reddy Ayyadapu

Addresses:
Department of Information Technology, Cloudera Inc., Ashburn, Virginia, United States of America.                                      

Abstract:

Smart infrastructure powered by Artificial Intelligence (AI) and Machine Learning (ML) transforms modern urban environments by optimizing energy distribution, water management, transportation, and security systems. This study presents an AI-ML-driven architecture for smart infrastructure deployment, integrating real-time and historical data from various sensors and IoT devices. The proposed system employs advanced predictive modeling techniques to enhance decision-making processes. To develop and analyze this architecture, a combination of tools, including Python, TensorFlow, Scikit-learn, and SQL-based databases, was used for data processing, model training, visualization, and system design. The dataset consists of real-time sensor readings from smart grids, water distribution networks, traffic monitoring systems, surveillance cameras, and historical records from cloud storage and edge computing environments. Key findings demonstrate that AI-driven models significantly improve resource management’s predictive accuracy, reduce data processing latency, and enhance overall system efficiency. Graphical representations, such as deployment diagrams and time-series forecasting graphs, illustrate the interactions between AI models, infrastructure components, and deployment services. The results underscore the potential of AI-ML frameworks to optimize smart infrastructure, making them more adaptive and resilient.

Keywords: Smart Infrastructure; Artificial Intelligence; Machine Learning; Predictive Analytics; Automation and Traffic Monitoring Systems; Infrastructure Components; Real-Time Sensor Readings.

Received on: 04/03/2024, Revised on: 02/05/2024, Accepted on: 11/07/2024, Published on: 09/09/2024

DOI: 10.69888/FTSIN.2024.000283

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 3, Pages: 135-145

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