Search

The time lost looking for a free parking spot in a city impacts negatively not only on the mood of the drivers but also on the environment in terms of air quality and fuel consumption. The vehicle detection can be considered as the most important task in Smart Parking systems as it allows to automatically monitor the occupancy state of the parking spots in a city. In this paper, we implement and test a vehicle detection system based on a magnetometer sensor, which is part of a complete Smart Parking system under development at the University of Cagliari. After a preliminary analysis conducted to test the performance of the magnetometer, we conducted two specific experiments to investigate the suitability of the magnetometer as the mean to detect the presence of a vehicle in the parking spots. The first experiment, involving 15 different vehicles, has demonstrated that the magnetometer can be used to reliably detect the presence of a vehicle in a parking spot if it is placed under the front or rear axle of the vehicle. From the second experiment it resulted that, when considering 3 adjacent parking spots and only one magnetometer placed in the central spot, it is not possible to reliably detect the vehicles parked on the adjacent spots. Therefore, one magnetometer for each considered parking spot is needed.

Location has become an essential part of the next-generation Internet of Things systems. This paper proposes a multi-sensor-based 3D indoor localization approach. Compared with the existing 3D localization methods, this paper presents a wireless received signal strength (RSS)-profile-based floor-detection approach to enhance RSS-based floor detection. The profile-based floor detection is further integrated with the barometer data to gain more reliable estimations of the height and the barometer bias. Furthermore, the data from inertial sensors, magnetometers, and a barometer are integrated with the RSS data through an extend Kalman filter. The proposed multi-sensor integration algorithm provided more robust and smoother floor detection and 3D localization solutions than the existing methods.