This thesis investigates different approaches to solving the problem of regular long queue times in student restaurants. The problem's important features were defined, and the most important feature is the characteristic of the chaotic occupancy pattern of the queue, even in relatively small periods. It was concluded that due to the nature of the subjective experience during wait times, it is better to categorize queue lengths, rather than to see them as a continuous value. Queue length was divided into three categories; short, medium and long. With the reached conclusions, the approach of capturing WiFi signals to detect crowds was chosen. As each restaurant user carries a personal smartphone, the number of people in the restaurant can be obtained by capturing WiFi packets and counting unique MAC addresses. By placing the capture device that detects WiFi signals near the restaurant queue, and filtering the detected devices by WiFi signal strength (ie by distance), only people waiting in the queue are counted. Developing and testing the chosen approach, it was concluded that it provides adequate accuracy for the needs of this thesis. By establishing a server and a web interface, the user can check the current state of the queue in the restaurant. By going to the restaurant only during short queues, the user's waiting time in line is significantly reduced.