Addressing the covid-19 crisis by harnessing internet of things sensors and machine learning algorithms in data-driven smart sustainable cities

Abstract

This paper analyzes the outcomes of an exploratory review of the current research on data-driven smart sustainable cities. The data used for this study was obtained and replicated from previous research conducted by Capgemini, ICMA, KPMG, UNESCAP, UNHSP, SCC, The University of Adelaide, and The World Bank. We performed analyses and made estimates regarding Internet of Things sensors and machine learning algorithms. Data collected from 5,200 respondents are tested against the research model by using structural equation modeling.