Collaborative Localization with Truth Discovery for Heterogeneous and Dynamic Vehicular Networks
Paper in proceeding, 2020
Collaborative localization over vehicular networks is challenging if quality varies among the collected multiple sources of information. These information sources are either from vehicle on-board sensors or remote sensing using vehicular communications. The variation in the quality of remote sensor information may cause estimation performance deterioration, even threatening the system security. In this paper, we propose a distributed localization framework with truth discovery for heterogeneous and dynamic vehicular networks. Firstly, it allows vehicles to learn which neighboring vehicles they should cooperate with. Secondly, it is resilient against the quality variation of the shared information between the connected vehicles.
5G mobile communication
wireless sensor networks
Remote sensing
quality of information
Kalman filters
distributed localization
truth discovery
Sensors
vehicular ad hoc networks
Vehicle-to-everything
vehicular networks
Signal to noise ratio
sensor placement
Vehicle dynamics
Author
Fuxi Wen
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Tommy Svensson
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
IEEE Vehicular Technology Conference
15502252 (ISSN)
Vol. 2020-May 9128766
91st IEEE Vehicular Technology Conference, VTC Spring 2020
Antwerp, Belgium,
Antwerp, Belgium,
Automotive 5G Integrated Security and Communications
Chalmers, 2018-01-01 -- 2019-12-31.
Areas of Advance
Information and Communication Technology
Transport
Subject Categories
Telecommunications
Communication Systems
Computer Science
DOI
10.1109/VTC2020-Spring48590.2020.9128766