Privacy-Aware Edge Intelligent Parking Recommendation Using Intuitionistic Fuzzy Sets

Intelligent parking recommendations are essential for enhancing parking space utilization and alleviating traffic congestion. Current parking recommendation systems are mostly based on collecting a large amount of raw parking-related data. However, this often imposes a significant communication and computing burden on resource-constrained edge vehicles and roadside facilities, resulting in high communication overhead, latency, and particularly privacy risks. In this article, a lightweight privacy-aware edge intelligent parking recommendation scheme (FuzzyTop) is proposed. Raw parking data is converted into fuzzy information on the edge sides using intuitionistic fuzzy sets (IFSs), and then a fuzzy multiattribute ranking algorithm is devised to determine the top $k$ optimal parking lots for drivers. We evaluated our scheme using real-world data sets collected from Hangzhou and Shanghai in China. The experimental results show that the FuzzyTop algorithms outperform the state-of-the-art benchmarks in terms of data transmission, accuracy, and real-time performance.