Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
Feature recognition, Finding parking spaces, Intelligent transportation systems, WiFi CSI
With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot.
Includes bibliographical references (pages 68-73)
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Zhang, Yunfan, "An Approach to Finding Parking Space Using the CSI-based WiFi Technology" (2018). Electronic Theses and Dissertations. 2440.