Cloud-IoT integration for predictive analytics in smart city governance

Abstract

Smart city governance increasingly relies on seamlessly integrating Cloud computing and Internet of Things (IoT) technology to improve urban decision-making, resource management, and citizen engagement. Cloud-IoT integration enables the collection, storage, and processing of vast amounts of real-time data from diverse sources, such as sensors, smart meters, transportation systems, and public infrastructure. By leveraging predictive analytics, this data can be transformed into actionable insights, allowing city administrators to anticipate and respond proactively to challenges, optimize resources, and enhance the quality of life for citizens.

This paper explores the architecture, data management strategies, and analytical tools necessary for effective Cloud-IoT integration in a smart city context. It delves into how predictive analytics, supported by machine learning and Artificial Intelligence (AI) algorithms, empowers cities to predict traffic congestion, energy consumption patterns, and public safety risks. Additionally, the integration raises critical discussions about data privacy, security, and the technical requirements for scalability. Findings suggest that predictive analytics in smart city governance offers substantial benefits, including reduced operational costs, improved service delivery, and enhanced sustainability. This abstract overviews how cloud-IoT integration for predictive analytics can revolutionize urban governance by creating more responsive, efficient, and sustainable cities.