# Artificial Intelligence and Location Verification in Vehicular Networks

## TL;DR

This paper introduces a neural network-based location verification system for vehicular networks that outperforms traditional methods, especially when prior information about user authenticity is unknown and under challenging signal conditions.

## Contribution

The work presents a novel NN-based LVS that functions effectively without prior knowledge of genuine user proportions, improving verification accuracy in vehicular IoT environments.

## Key findings

- NN-LVS outperforms traditional LVS in various real-world scenarios.
- Effective under Non-Line-of-Site (NLoS) signal bias.
- Maintains high performance even with unknown user authenticity proportions.

## Abstract

Location information claimed by devices will play an ever-increasing role in future wireless networks such as 5G, the Internet of Things (IoT). 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 users in the field. In this work we address this critical limitation by using a Neural Network (NN) showing how such a NN based LVS is capable of efficiently functioning even when the proportion of genuine users is completely unknown a-priori. We demonstrate the improved performance of this new form of LVS based on Time of Arrival measurements from multiple verifying base stations within the context of vehicular networks, quantifying how our NN-LVS outperforms the stand-alone information-theoretic LVS in a range of anticipated real-world conditions. We also show the efficient performance for the NN-LVS when the users' signals have added Non-Line-of-Site (NLoS) bias in them. This new LVS can be applied to a range of location-centric applications within the domain of the IoT.