IoT Regression Techniques In Smart Parking Systems: Survey

Abstract

Smart cities are one of the main areas where IoT showed great success, collecting and processing enormous amounts of data to facilitate different applications. Smart parking is one of the evolving smart cities applications. The rapid increase in the population also results in a high number of vehicles that may lead to traffic congestion in urban cities. This traffic congestion is increasing the problem of urban mobility. Urban mobility may have an adverse impact on the quality of life as well as the economy. This problem can be mitigated with the efficient management of the parking systems. This work aims to review the IoT-based regression techniques used in smart parking systems to overcome the problem of urban mobility. IoT-based regression techniques are used to predict parking space in the parking area so that the drivers can get parking space on time and the problem of urban mobility can be mitigated. It was identified that regression techniques efficiently predicted parking space availability. This study provides a comprehensive review of regression techniques used in IoT smart parking applications. The system architecture was also presented to get a better understanding of this work.