2 research outputs found
Machine Learning and Location Verification in Vehicular Networks
Location information will play a very important role in emerging wireless
networks such as Intelligent Transportation Systems, 5G, and the Internet of
Things. However, wrong location information can result in poor network
outcomes. It is therefore critical to verify all location information before
further utilization in any network operation. In recent years, a number of
information-theoretic Location Verification Systems (LVSs) have been formulated
in attempts to optimally verify the location information supplied by network
users. Such LVSs, however, are somewhat limited since they rely on knowledge of
a number of channel parameters for their operation. To overcome such
limitations, in this work we introduce a Machine Learning based LVS (ML-LVS).
This new form of LVS can adapt itself to changing environments without knowing
the channel parameters. Here, for the first time, we use real-world data to
show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate
this improved performance within the context of vehicular networks using
Received Signal Strength (RSS) measurements at multiple verifying base
stations. We also demonstrate the validity of the ML-LVS even in scenarios
where a sophisticated adversary optimizes her attack location.Comment: 5 pages, 3 figure
Artificial Intelligence and Location Verification in Vehicular Networks
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.Comment: 6 Pages, 5 figure