Optimal MEC Deployment in Smart Cities Using Clustering Algorithms.

Abstract

With the increasing complexity of urban mobility and the rapid growth of connected devices, there is a growing need for more intelligent and adaptive transportation systems. This study explores the integration of three core technologies — Fifth Generation (5G) networks, Multi- access Edge Computing (MEC), and Artificial Intelligence (AI) — to optimize the allocation of computing resources in smart cities. Using real vehicle movement data from San Francisco, clustering techniques such as Agglomerative Clustering and visualization tools like Dendro- grams are applied to identify optimal MEC server deployment locations. This approach aims to ensure efficient resource distribution, reduce latency, and enhance user experience within intelligent transportation systems.