Location information claimed by devices will play an ever-increasing role in future wireless networks such as 5G, the Internet of Things (IoT), and Intelligent Transportation Systems (ITS). Against this background, the verification of such claimed location information will be an issue of growing importance. A formal information-theoretic Location Verification System (LVS) can address this issue to some extent, but such a system usually operates within the limits of idealistic assumptions on a priori information on the proportion of genuine user-vehicles in the field. In this work we address this critical limitation by the use of a Deep Neural Network (DNN) showing how such a DNN based LVS is capable of efficiently functioning even when the proportion of genuine user-vehicles is completely unknown a-priori. We demonstrate the improved performance of this new form of LVS based on Time of Arrival (ToA) measurements from multiple verifying base stations within the context of vehicular networks, quantifying how our DNN-based LVS outperforms the stand-alone information-theoretic LVS in a range of anticipated real-world conditions.
View on arXiv