Smart Parking with Learning-aided User Activity Sensing Based on Edge Computing

Parking is an everyday ordinary activity in urban lives. In other words, it is the most common and essential requirement of all drivers in a car park to fast search a preferred parking spot. Recently most parking lots provide a sensing-based display system to notify available parking spots. However, such systems are still unable to tell drivers exact parking spots and make any recommendation to improve the traffic conditions and driver experiences. In this paper, a novel machine-learning-aided smart parking system based on Internet of Things, smart mobile devices and edge computing is proposed. This novel parking system aims at providing customized parking experience to users through highly accurate positioning and user activity sensing computed as the edge intelligence. The proposed system mainly focuses on increasing user experience for parking through the concatenated autoencoder design for improving performances of the user positioning mechanism.