Achieving efficient and privacy-preserving worker selection with arbitrary spatial ranges for vehicular crowdsensing

Authors

Yantao Yu, Tongji University, China
Xiaoping Xue, Tongji University, China
Jinxiao Ma, Tongji University, China
Songnian Zhang, Xidian University, China
Yunguo Guan, Eastern Michigan University
Rongxing Lu, University of New Brunswick, Canada

Document Type

Article

Publication Date

2024

Department/School

Information Security and Applied Computing

Publication Title

IEEE Transactions on Vehicular Technology

Comments

Y. Guan is a faculty member in EMU's School of Information Security and Applied Computing.

Link to Published Version

DOI: 10.1109/TVT.2024.3394909

Recommended Citation

Yu, Y., Xue, X., Ma, J., Zhang, S., Guan, Y., & Lu, R. (2024). Achieving efficient and privacy-preserving worker selection with arbitrary spatial ranges for vehicular crowdsensing. IEEE Transactions on Vehicular Technology, 73(9), 13765–13776. https://doi.org/10.1109/TVT.2024.3394909