TCFP: A Novel Privacy-Aware Edge Vehicular Trajectory Compression Scheme Using Fuzzy Markovian Prediction

Yinglong Li, Zhiwei Huang, Tieming Chen, Xinchen Xu, Weiru Liu, Mingqi Lv

Abstract: Vehicular trajectory data can be widely used in applications such as traffic prediction and congestion control. However vehicular trajectory data is voluminous and requires significant storage and processing resources, which contradicts the resources-constraint vehicular networks. Existing compression methods suffer either low compression effects or privacy leakage. A privacy-aware Trajectory Compression scheme based on Fuzzy markovian Prediction (TCFP) is proposed in this paper, which consists of two steps of fuzzy compression. The first-step compression is achieved by converting the raw trajectory data into fuzzy information on the edge vehicle sides. Further compression is performed at edge RSUs through fuzzy multi-order Markovian prediction combined with new-devised fuzzy deviation filtering rules. Extensive experimental evaluation based on real-world data sets demonstrates the proposed TCFP scheme achieves desired QoS performance in terms of compression rate, compression time and information loss.