An Improved Large Scale Data Analytics for Smart Cities with Multimodal Data Fusion

Massive amounts of data are produced by smart cities from a variety of sources, including sensors, social media, and IoT devices. The data analysis can offer insightful information for raising the quality of life and improving urban services. However, data fusion and analysis are made more difficult by the large-scale nature of smart city data. An improved method for big data analytics for smart cities is presented in this abstract, with a particular emphasis on the fusion and analysis of multimodal data. Integrating heterogeneous data from several sources, such as text, photos, videos, and sensor measurements, is known as multimodal data fusion. A more complete understanding of the city can be attained by merging these many data modalities. To establish a single representation for analysis, the fusion process uses feature extraction, data preprocessing, and integration techniques. There are several opportunities created by the analysis of multimodal data in smart cities. It makes it possible to find intricate patterns like social behavior, environmental pollution, and traffic congestion. Predictive models that foretell future occurrences and optimize resource allocation can be constructed by utilizing cutting-edge machine learning and deep learning techniques. Additionally, data analytics may support decision-making by giving city officials and urban planners up-to-the-minute information and practical advice. To create strong data analytics frameworks and integrate them into the architecture of smart cities, interdisciplinary collaboration between data scientists, urban designers, subject matter experts, and policymakers is necessary. In conclusion, large-scale data analytics for smart cities that combine and analyze multimodal data have significant promise for enhancing urban services and building sustainable, livable communities.