Smart City Feasibility Study using IoT and Machine Learning

Complexity and resource constraints accompany urban growth. According to UN figures, cities currently use 75% of the global energy, with 70% of the greenhouse gas emissions being mostly generated from transportation and residence buildings. Furthermore, city residents are susceptible to the consequences of climate change. Therefore, a feasibility study for the possibility of implementing a smart city was conducted in this paper. The results show that the current situation of the cities is/renders them far from being smart, while the environmental aspect needs to be controlled by the use of IoT sensors. The utilized Hyperd algorithm gave highly accurate prediction results.