FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

• Published in: IEEE transactions on computational social systems Vol. 9; no. 1; pp. 197 - 206
• Main Authors: Kong, Xiangjie, Gao, Haoran, Shen, Guojiang, Duan, Gaohui, Das, Sajal K.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-02-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collaborative edge computing (CEC); Collaborative work; Collision avoidance; Computational modeling; cooperative positioning (CP); Data models; Edge computing; Federated learning; Global navigation satellite system; Global Positioning System; Intelligent vehicles; Internet of Things; Internet of Vehicles; Navigation satellites; Privacy; Sensors; Transfer learning; Vehicle-to-infrastructure; Vehicles
• ISSN: 2329-924X; 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2021.3062053

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed.