Optimizing Real-Time Data Processing in Smart Cities Using Internet of Things (IoT) for Scalable, Efficient, and Sustainable Urban Service Delivery
DOI:
https://doi.org/10.32595/jcait/v2i2.2026.31Keywords:
Internet of Things, Smart Cities, Data Processing, machine learningAbstract
With the rapid increase of city population growth on a global scale, effective, scalable and sustainable systems to manage urban environments have become a high priority for cities. Smart cities utilize the Internet of Things (IoT) to gather information about their environment (real-time data) and make decisions based on that information. The information generated by IoT is then analyzed and presented back to decision-makers for use in managing the city. However, the amount, speed and diversity of data created by IoT present challenges. The biggest challenges are (1) how to process such a large volume of information in real-time, and (2) how to analyze such diverse sources of data to generate accurate and useful insights. In this study, we present an approach to improving urban service delivery through improved Real-Time Data Processing for Smart Cities using IoT technologies. Through a combination of edge computing, cloud architecture, and effective techniques for data stream processing, the researchers aim to reduce delays in the delivery of these services and improve the overall responsiveness of systems. With the implementation of edge devices, the processing of data will occur as near the data generating source as possible. Therefore, there will be minimal congestion within the network and decision-making will happen more quickly. Additionally, the system utilizes intelligent filtering, prioritization, and other data handling techniques for processing large amounts of data efficiently, thereby enabling better scalability to handle increasing demands for data within smart city systems, without degradation in performance Optimizing IoT-based data processing can enhance resource utilization and support sustainable urban development through real-time monitoring and adaptive control of urban services. This study shows that IoT-enabled solutions can lead to improved urban management, which results in smarter, more responsive, and more environmentally friendly cities.