A computational architecture for smart cities capable of responding to emergency events

Abstract

Ever since the first industrial revolution, there has been a trend of urbanization in the cities and it was always a challenge to any government to manage social welfare with such a high population density. The rapidly evolving development of technologies like the electronic computer and the internet allowed people to generate large amounts of data never seen before. In this scenario, Big Data techniques are being largely developed to make it possible to gather statistical information and build machine learning models that can be used to forecast events and improve decision-making processes. The popularity and accessibility of technological tools, like open-source software, open data, and personal computers, enables independent projects to provide insights into a variety of scenarios. The work developed describes a lean architecture framework for smart cities that aims to be efficient, inexpensive, and robust for systems highly dependant on data processing. This system allows for fast responses to emergency events, such as pandemics and catastrophes by simplifying deployment, data management and task execution. A prototype of the system has been implemented and tested.