Data pertaining to the availability of parking slots is crucial to the efficient operation of systems designed to monitor the state of parking spaces. Outdoor parking systems have been developed using wireless sensors, Internet of Things (IoT) technology, and cameras. Unfortunately, interference from electromagnetic fields complicates the tuning of parameters for detection algorithms and limits accuracy to only 90 percent. In this study, we investigated these problems by collecting data from magnetic sensors, light sensors, and LoRa wireless modules used in the detection transient events (car arrivals and departures) over a period of 13 months. This led to the design an adaptive occupancy detection system using a variety of sensors, which can be deployed with only minimal calibration.

The need to find parking contributes to road congestion and leads to unnecessary fuel consumption. Of all emerging parking systems, Internet-of-Things (IoT)-based systems have demonstrated the feasibility of real-time delivery of parking availability using magnetic sensors. However, existing magnetic-based methods are prone to false positives caused by electromagnetic fields emitted from surrounding electric facilities. In this study, we conducted a 3-month data collection in a parking area. We identified the need to introduce learning and collaboration into the design of our detection algorithm which recognizes learned patterns associated with car arrivals or departures, and to filter out unreliable events based on spatial and temporal features.