Data Fusion for Enhanced Public Safety Analytics in Smart Cities Integrating AI, IoT, and Big Data

Authors

Ravikanth Konda

Abstract

The rapid evolution of urban environments requires the integration of advanced technologies to ensure public safety within smart cities. The advent of data fusion techniques offers an innovative solution to streamline the aggregation of information from various sources, such as Internet of Things (IoT) devices, artificial intelligence (AI) algorithms, and big data systems. This paper explores the potential of data fusion to enhance public safety analytics, enabling cities to leverage these technologies for real-time decision-making, predictive analytics, and optimization of resources. We propose a comprehensive framework for the integration of IoT, AI, and big data to create a holistic public safety system capable of addressing complex urban challenges. This paper also provides a detailed review of the literature, methodology, results, and discussions around the framework's performance in real-world scenarios. The findings show that the proposed data fusion techniques significantly improve the accuracy and efficiency of public safety systems, reducing response times and preventing incidents before they occur. The paper concludes with insights into the future directions of data fusion in smart cities, with an emphasis on overcoming existing challenges and ensuring sustainability and scalability in public safety operations.

Keywords

Data Fusion for Enhanced Public Safety Analytics in Smart Cities Integrating AI, IoT, and Big Data. Ravikanth Konda. 2024. IJIRCT, Volume 10, Issue 6. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2505014