A Systematic Mapping Study on Smart Cities Modeling Approaches

TL;DR

This systematic mapping study reviews existing research on smart city modeling approaches, highlighting prevalent methods, research trends, and future directions to better understand and develop smart city frameworks.

Contribution

It provides a comprehensive overview of smart city modeling approaches, identifying key research trends, common methodologies, and gaps for future exploration.

Findings

Smart governance is the most investigated city dimension.

Business, architectural, and ontological modeling are the most used approaches.

Most existing technologies are not yet proven in operational environments.

Abstract

The Smart City concept was introduced to define an idealized city characterized by automation and connection. It then evolved rapidly by including further aspects, such as economy, environment. Since then, many publications have explored various aspects of Smart Cities across different application domains and research communities, acknowledging the interdisciplinary nature of this subject. In particular, our interest focuses on how smart cities are designed and modeled, as a whole or as regards with their subsystems, when dealing with the accomplishment of the research goals in this complex and heterogeneous domain. To this aim, we performed a systematic mapping study on smart cities modeling approaches identifying the relevant contributions (i) to get an overview of existing research approaches, (ii) to identify whether there are any publication trends, and (iii) to identify possible future research directions. We followed the guidelines for conducting systematic mapping studies by Petersen et al. to analyze smart cities modeling publications. Our analysis revealed the following main findings: (i) smart governance is the most investigated and modeled smart city dimension; (ii) the most used modeling approaches are business, architectural, and ontological modeling approaches, spanning multiple application fields; (iii) the great majority of existing technologies for modeling smart cities are not yet proven in operational environments; (iv) diverse research communities publish their results in a multitude of different venues which further motivates the presented literature study. Researchers can use our results for better understanding the state-of-the-art in modeling smart cities, and as a foundation for further analysis of specific approaches about smart cities modeling. Lastly, we also discuss the impact of our analysis for the Model-Driven Engineering community.