Dynamic Resource Allocation in Edge-Enabled IoT Systems for Smart Cities

Abstract

As urban environments increasingly adopt smart technologies, integrating Internet of Things (IoT) systems with edge computing has become a crucial strategy for enhancing city management and services. This paper addresses the challenge of dynamic resource allocation within edge-enabled IoT systems in smart cities. We propose a novel framework that optimizes resource distribution based on real-time data, user demands, and network conditions, thereby improving latency, energy efficiency, and overall system performance. Our approach utilizes advanced algorithms, including machine learning and heuristic methods, to facilitate adaptive resource allocation across various applications such as traffic management, environmental monitoring, and public safety. Simulation results demonstrate significant improvements in resource utilization and response times compared to traditional cloud-centric models. The findings underscore the potential of dynamic resource allocation in creating resilient and efficient smart city infrastructures, paving the way for future research and practical implementations in urban environments.