  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.
