Cloud and Edge Computing Integration in Smart City IoT Solutions

Abstract

The rise of smart city initiatives has created a growing need for efficient and scalable Internet of Things (IoT) solutions that can handle vast amounts of data quickly and securely. Traditional cloud computing often struggles to meet the real-time demands of smart city applications, primarily due to latency issues and primary concerns. This paper explores how we can overcome these challenges by combining cloud and edge computing into a hybrid approach that utilizes both strengths. Edge computing allows for faster processing of data right where it's generated, which reduces delays and enhances data security. Meanwhile, cloud computing excels at handling complex analytics and can scale to accommodate resource-intensive tasks. Our proposed architecture intelligently allocates processing tasks based on specific data needs: tasks that require quick responses are handled at the edge. At the same time, more complex analyses are processed in the cloud. We conducted simulations in various smart city applications to validate this approach, including traffic management, environmental monitoring, and emergency services. The results showed significant improvements in response times, data privacy, and overall operational efficiency. These findings indicate that integrating cloud and edge computing offers a promising and sustainable solution for IoT frameworks in smart cities, striking a balance between quick responsiveness and secure, scalable data management.