4.7 Article

Location Verification for Future Wireless Vehicular Networks: Research Directions and Challenges

Journal

IEEE NETWORK
Volume 36, Issue 6, Pages 60-66

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.103.2100338

Keywords

Ions; Millimeter wave communication; Integrated circuits; Global navigation satellite system; Wireless networks; Neural networks; Rician channels

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This article discusses the issue of location verification in vehicular networks and provides solutions. Manipulations by malicious vehicles sending false location information can lead to poor operational outcomes and safety violations. With the help of mmWave technology and machine learning, simultaneous location reporting and verification can be achieved, providing solutions with reliability levels required by vehicular networks.
Vehicle location information obtained through the global navigation satellite system (GNSS) will play a pivotal role in emerging vehicular networks. This vital information is, however, susceptible to a host of unwanted manipulations, especially if a malicious entity is involved. The most obvious example of such manipulations is the forwarding by a malicious vehicle of false GNSS locations to other members of the network. Such events can lead to poor operational outcomes for the vehicular network, and in extreme cases even lead to catastrophic safety violations. Here, we highlight research efforts pursued in the past few years that have attempted to address this weakness in vehicular networks. We also discuss the importance of location verification in the wake of emerging wireless technologies, such as those being proposed for beyond 5G (B5G) wireless vehicular networks. In particular, we detail an opportunity to conduct location reporting and verification simultaneously with the aid of mmWave technology and discuss how emerging machine learning (ML) techniques will provide for location verification solutions where reliability levels will be commensurate with that required by the vehicular network paradigm. We close by discussing the potential enhancements for location verification within a future combined B5G-ML architecture.

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