FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles
Abstract
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.
Recommended Citation
X. Kong et al., "FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles," IEEE Transactions on Computational Social Systems, Institute of Electrical and Electronics Engineers (IEEE), Mar 2021.
The definitive version is available at https://doi.org/10.1109/TCSS.2021.3062053
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Publication Status
Early Access
Keywords and Phrases
Collaborative edge computing (CEC); Collaborative work; Computational modeling; cooperative positioning (CP); Data models; Edge computing; federated learning; Global Positioning System; Internet of Vehicles.; Sensors; Transfer learning
International Standard Serial Number (ISSN)
2329-924X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
15 Mar 2021
Comments
Published online: 15 Mar 2021
This work was supported in part by the National Natural Science Foundation of China under Grant 62072409 and Grant 62073295, in part by the Zhejiang Provincial Natural Science Foundation under Grant LR21F020003, and in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RF-B2020001. The work of Sajal K. Das was supported in part by the U.S. National Science Foundation (NSF) under Grant CNS-2008878, Grant SaTC-2030624, Grant CNS-1818942, and Grant OAC-1725755.