This work proposes an adaptive recommendation mechanism for smart parking that takes advantage of the popularity of smartphones and the rise of the Internet of Things. The proposal includes a centralized system to forecast available indoor parking spaces, and a low-cost mobile application to obtain data of actual and predicted parking occupancy. The described scheme uses data from both sources bidirectionally so that the centralized forecast system is fed with data obtained with the distributed system based on smartphones, and vice versa. The mobile application uses different wireless technologies to provide the forecast system with actual parking data and receive from the system useful recommendations about where to park. Thus, the proposal can be used by any driver to easily find available parking spaces in indoor facilities. The client software developed for smartphones is a lightweight Android application that supplies precise indoor positioning systems based on Quick Response codes or Near Field Communication tags, and semi-precise indoor positioning systems based on Bluetooth Low Energy beacons. The performance of the proposed approach has been evaluated by conducting computer simulations and real experimentation with a preliminary implementation. The results have shown the strengths of the proposal in the reduction of the time and energy costs to find available parking spaces.
Similarly, REF used Near Field Communication (NFC) tags for their indoor positioning system.
Cellular Automata-Based Application for Driver Assistance in Indoor Parking Areas †
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Engineering Computer Science Medicine