Smart Parking System Based on Dynamic and Optimal Resource Allocation

Smart parking uses the Internet of Things (IoT) to collect and control data to efficiently manage parking spaces. Indeed, Smart Parking Systems (SPS) in smart cities can reduce traffic congestion, reduce the time for searching available parking spots, reduce the CO2 emissions, and offer other benefits, but their efficient resource (parking spot) allocation remains a challenge. In this article, we present our allocation approach using genetic algorithms (GA) with a multi-criteria objective function. We managed to optimize the four criteria of distance, parking space utilization, user preferences and travel time, while considering a set of parkings in a smart city. As a conceptual support of our approach, we propose a meta-model of concepts for smart parking management in IoT context; we also propose a four-layer architecture of SPS in a smart city. Our goal is to maximize parking space utilization, minimize travel time and distance, and satisfy driver preferences. We conducted a set of simulations to highlight significant improvements in the overall optimization of parking spots allocation. The results showed that the GA algorithm satisfied driver’s preferences while minimizing distance and travel time to get a parking spot and also, maximizing parking spots utilization. Furthermore, comparisons of the results obtained with those derived from the FCFS algorithm showed the effectiveness of the GA algorithm.