Location information claimed by devices will play an ever-increasing role in future wireless networks such as wireless vehicular networks, 5G, and 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 proportions of genuine and malicious 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 proportions of genuine and malicious users are 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-Sight (NLoS) bias in them. This new LVS can be applied to a range of location-centric applications within the domain of the IoT.
|Conference||2019 IEEE Global Communications Conference|
|Abbreviated title||IEEE GLOBECOM 2019|
|Period||9/12/19 → 13/12/19|