DASTData: um modelo baseado em Fog e Cloud Computing para rastreabilidade e armazenamento distribuído de dados IoT em Smart Cities

Abstract

In this work, a solution for the distributed storage of data in Smart Cities is pre sented. Through an Edge-Fog-Cloud architecture that partitions the data through the Sharding technique, a hierarchical model is proposed that manipulates IoT data generated by Smart Cities. The problem is related to the approaches used to promote an integrated environment. Related works tend to use cloud-focused approaches that generate high latency rates, and those that use Fog Computing only use the layer as middleware, not exploring greater possibilities for use. In this context, this work presents the DASTData model that aims to enable lower latency rates, more data security, fault tolerance, high availability and concurrent queries to promote a better experience in data management and availability in smart cities. In addition, our contribution to the literature, unlike related works, is related to the proposition of an architecture focused on enabling the traceability of users who have a mobile behavior in the city, providing the ability to analyze patterns and occurrences through the consolidation of data from one or more indi viduals. In the results obtained through the tests carried out in this work, it is observed that in queries a decentralized model such as DASTData is up to 73% more efficient than a centralized model.