An Indexing-Based Architecture for Fast Data Retrieval in Smart Cities

Abstract

A Smart City leverages Information and Communication Technologies (ICT) to enhance the quality of life for its residents by improving operational efficiency and providing reliable services. The primary objective of a Smart City is to use technology for optimization and decision-making. This involves deploying a network of sensors connected to the internet to collect real-time data from the environment, which is then shared across various systems to make intelligent decisions and improve city operations. Smart City applications encompass smart homes, transportation systems, traffic management, power consumption management, water supply networks, and other community services. The resulting vast amounts of data, known as Big Data, present significant challenges in terms of acquisition, management, and real-time analysis. Despite various proposed solutions by researchers, efficient data handling remains a critical issue. In our study, we propose an innovative architecture based on indexing to enhance data analytics for Smart City applications. This architecture covers data acquisition, storage, and retrieval. We validate our proposed architecture through the analysis of diverse datasets related to Smart Cities, demonstrating its effectiveness in managing and processing large-scale data efficiently.