Efficient automated car parking system based modified internet of spatial things in smart cities

Abstract

The technological advances of smart cities have been progressively increasing to improve the quality of life to humans, especially in urban mobility. Parking appears to be a major issue, with residents needing to find a suitable parking space among many parking areas, resulting in time and fuel waste as well as environmental pollution. We propose in this paper a new automated system model that integrates reinforcement learning (RL), Q-learning, and image processing algorithms based on modified Internet of Spatial Things (IoST) architecture to optimize automated parking in smart cities. For demonstrating the efficiency of the proposed model, iFogSim simulation is used to reduce network usage and latency. Moreover, it deploys heterogeneous devices in multi layers and different scenarios. The experimental results show that the suggested system for automated car parking in fog-based placement-IoST network is feasible and effective. it minimizes latency and the total network usage compared to the cloud-based placement of the implemented system.

Energy Aware Smart Car Parking System Based Internet of Spatial Things

“…Moreover, the IoST generates huge amounts of data over time. Hence, the load balancing algorithm is an essential aspect of IoST's development [9]. The term "spatial data" refers to information about actual physical objects on Earth that is displayed on a map in the form of geographic coordinates [10].…”