Empowering Smart Cities with Edge Computing-Based IoT Systems: A Focus on Data Analytics and Machine Learning Techniques

The rise of the Internet of Things (IoT) has led to the development of smart cities, where various systems and devices are interconnected and constantly generating large amounts of data. Edge computing, which enables the processing and analysis of data closer to the source, has emerged as a promising solution to handle the data deluge in smart cities. This research aims to develop a data analytics framework that utilizes machine learning techniques for edge computing-based IoT systems in smart cities. The proposed framework will be designed to handle the unique challenges of IoT data, such as heterogeneity, volume, velocity, and variety. The framework will also enable real-time analytics and decision-making at the edge, which is critical for many smart city applications, such as traffic management, energy management, and public safety. The research will involve the evaluation of various machine learning algorithms for edge computing-based IoT systems and the development of a prototype system to demonstrate the effectiveness of the proposed framework.