Universidade Federal do Ceará
Mestrado e Doutorado em Ciências da Computação

Título: A Microservices- And Artificial Intelligence-Based Software Architecture for Building Flexible Smart City Platforms

Data: 11/03/2025
Horário: 09h
Local: Remoto

Resumo:

A smart city integrates data from various subdomains to offer intelligent services, improving city resource management and citizens’ daily lives. Technologies such as ICT, IoT, big data, and AI enable this data management, which is supported by software platforms. However, in addition to the usual challenges of software platforms, developing a smart city platform faces issues such as resource heterogeneity, flexibility, AI efficiency, fast data processing, and low latency. This work proposes addressing these issues with a microservice-oriented, data-centric platform that leverages AI and a three-layer architecture (edge, fog, cloud). This approach improves flexibility, scalability, and latency management, ensuring a responsive and efficient solution for smart cities. Semantic annotation mechanisms in the fog layer resolve data heterogeneity, enabling data exchanges using semantic values. This work’s main contributions include identifying essential functionalities for smart city platforms, analyzing existing solutions, specifying a reference architecture, implementing a prototype, and conducting performance evaluations. Initially, we conducted a Systematic Literature Review (SLR) to understand the state of the art, identifying the current enabling technologies, essential software platform requirements, and relevant open issues. Based on this, we introduce UFCity, a software architecture designed to meet such demands. We analyzed a UFCity-based prototype both qualitatively (use case scenarios) and quantitatively (Experimental Design 33). In the use case scenarios, we observed a reduction in the number of message exchanges and network bandwidth usage due to distributed data processing across solution layers. Furthermore, semantic mechanisms and IoT middleware efficiently handled resource heterogeneity. In quantitative analysis, we found consistent throughput in fog computing nodes even with message overload. Thus, this work demonstrates that UFCity meets the essential requirements of a smart city platform through use case scenarios and exhibits high execution performance, establishing itself as an advantageous solution compared to those proposed in the literature.

Banca examinadora:

• Prof. Dr. Angelo Roncalli Alencar Brayner (MDCC/UFC) – Orientador
• Prof.ª Dr.ª Rossana Maria de Castro Andrade (MDCC/UFC)
• Prof. Dr. Francisco José da Silva e Silva (UFMA)
• Prof. Dr. Markus Endler (PUC-Rio)