Location Verification Systems Research Articles

Location Verification for Emerging Wireless Vehicular Networks

The work reported here utilizes the best aspects of information theory and deep-learning concepts so as to provide, for the first time, a solution for a real-world location verification system (LVS) in the context of vehicular networks. it is well established that global positioning system coordinates supplied by vehicles will be a vital component of such emerging networks. This supplied location information, if erroneous and not verified, can seriously degrade the overall system performance and lead to significant safety issues. A number of location verification protocols and systems have been developed to address this important problem but all have operational constraints and performance limitations due to their requirement for ideal static channel conditions and assumed threat models. In this article, we remove such limitations by designing a neural-network-based LVS (NN-LVS) that can accommodate a priori unknown channel conditions and unknown threat models. Under most channel conditions, the NN-LVS shows a performance improvement of 50%, or more, relative to other LVSs. We also derive a new information-theoretic bound on the total error for an LVS and show how this new bound allows for a useful tradeoff in learning-time versus verification-performance for the NN-LVS. We demonstrate an improved performance for the NN-LVS within the context of vehicular networks using time of arrival measurements of the vehicles’ transmitted signals measured at multiple verifying base stations. The work reported here, we believe, paves the way to the actual deployment in real-world conditions of LVSs for emerging vehicular networks.

Location Verification Systems Based on Received Signal Strength With Unknown Transmit Power

In the context of location verification systems (LVSs), this letter proves that the knowledge of a legitimate user’s transmit power has no effect on the optimal performance of a received signal strength (RSS)-based LVS. Specifically, we prove that the detection performance of a generalized likelihood ratio test (GLRT), where the unknown transmit power is estimated, is identical to that of a differential likelihood ratio test (D-LRT). Our analysis also proves the asymptotic optimality of D-LRT for an RSS-based LVS with unknown transmit power. These results are important for real-world deployments of LVSs, since D-LRT incurs a significantly lower implementation cost relative to GLRT.

Location Verification Systems for VANETs in Rician Fading Channels

In this work we propose and examine Location Verification Systems (LVSs) for Vehicular Ad Hoc Networks (VANETs) in the realistic setting of Rician fading channels. In our LVSs, a single authorized Base Station (BS) equipped with multiple antennas aims to detect a malicious vehicle that is spoofing its claimed location. We first determine the optimal attack strategy of the malicious vehicle, which in turn allows us to analyze the optimal LVS performance as a function of the Rician $K$-factor of the channel between the BS and a legitimate vehicle. Our analysis also allows us to formally prove that the LVS performance limit is independent of the properties of the channel between the BS and the malicious vehicle, provided the malicious vehicle's antenna number is above a specified value. We also investigate how tracking information on a vehicle quantitatively improves the detection performance of an LVS, showing how optimal performance is obtained under the assumption of the tracking length being randomly selected. The work presented here can be readily extended to multiple BS scenarios, and therefore forms the foundation for all optimal location authentication schemes within the context of Rician fading channels. Our study closes important gaps in the current understanding of LVS performance within the context of VANETs, and will be of practical value to certificate revocation schemes within IEEE 1609.2.

Location Verification Systems Under Spatially Correlated Shadowing

The verification of the location information utilized in wireless communication networks is a subject of growing importance. In this work we formally analyze, for the first time, the performance of a wireless Location Verification System (LVS) under the realistic setting of spatially correlated shadowing. Our analysis illustrates that anticipated levels of correlated shadowing can lead to a dramatic performance improvement of a Received Signal Strength (RSS)-based LVS. We also analyze the performance of an LVS that utilizes Differential Received Signal Strength (DRSS), formally proving the rather counter-intuitive result that a DRSS-based LVS has identical performance to that of an RSS-based LVS, for all levels of correlated shadowing. Even more surprisingly, the identical performance of RSS and DRSS-based LVSs is found to hold even when the adversary does not optimize his true location. Only in the case where the adversary does not optimize all variables under her control, do we find the performance of an RSS-based LVS to be better than a DRSS-based LVS. The results reported here are important for a wide range of emerging wireless communication applications whose proper functioning depends on the authenticity of the location information reported by a transceiver.