Ido Nevat

Geo-Spatial Location Spoofing Detection for Internet of Things

We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called enhanced location spoofing detection using audibility (ELSA), which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the users device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit audibility information (or outage information) to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the devices location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

Signal strength based wireless Location Verification under spatially correlated shadowing

Given the growing role of location information in emerging wireless networks, the authentication of such information is becoming increasingly important. Perhaps the most obvious example of this is in network-based Intelligent Transport Systems (ITS), where authentication of location information is of critical importance to the safety and security of the system users. In this work, we investigate for the first time the performance limits of a Location Verification System (LVS) in the realistic setting of correlated log-normal fading channels. Utilizing the wireless signal strengths measured by authorized base stations as the input location information metrics, robust theoretical analysis and detailed simulations are used in order to determine the impact of key parameter settings on the LVS performance. Specifically, we show how the performance of an LVS depends on the correlation of the shadowing, and illustrate how such correlation can in fact lead to significant location-authentication performance improvement in some circumstances. The impact on performance of utilizing differential signal strengths, rather than raw received signal strengths, at the LVS is also analyzed. In a wider context, the work reported on here provides new insights into the performance of location authentication in channel settings that are anticipated for a wide range of emerging wireless networks.

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

In this paper, we address the challenging problem of multiple source localization in wireless sensor networks (WSN). We develop an efficient statistical algorithm, based on the novel application of sequential Monte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wireless channels. We also derive the posterior Cramér-Rao bound (PCRB) of the source location estimate. The PCRB is used to analyze the accuracy of the proposed SMC sampler algorithm and the impact that quantization has on the accuracy of location estimates of the sources. Extensive experiments show the benefits of the proposed scheme in terms of the accuracy of the estimation method that is required for model selection (i.e., the number of sources) and the estimation of the source characteristics compared to the classical importance sampling method.

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 his 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.

Timing information in wireless communications and optimal location verification frameworks

The verification of location information in wireless networks is a relatively new area of research, but one of growing importance. In this work we explore two formal theoretical frameworks for an optimal location verification system in which physical-layer timing information is the main observational input. In our first framework, we derive an optimal decision-rule using the system input/output mutual information as the optimization metric. In the second framework, a more traditional Bayesian approach is adopted in which the misclassification error is used as the decision-rules optimization metric. A verification-performance comparison between time-of-arrival (ToA) information and the more easily determined received signal strength (RSS) information is given. Our key finding is that for ToA accuracies attainable in next generation wireless networks, significant improvement in location verification can be expected relative to current RSS-based techniques. Our results are important for a wide range of emerging wireless networks and services, but especially for emerging Intelligent Transport Systems (ITS), where the authentication of location information is of critical importance to the safety and security of system users.

Location Spoofing Detection for VANETs by a Single Base Station in Rician Fading Channels

In this work we examine the performance of a Location Spoofing Detection System (LSDS) for vehicular networks in the realistic setting of Rician fading channels. In the LSDS, an authorized Base Station (BS) equipped with multiple antennas utilizes channel observations to identify a malicious vehicle, also equipped with multiple antennas, that is spoofing its location. After deriving the optimal transmit power and the optimal directional beamformer of a potentially malicious vehicle, robust theoretical analysis and detailed simulations are conducted in order to determine the impact of key system parameters on the LSDS performance. Our analysis shows how LSDS performance increases as the Rician K-factor of the channel between the BS and legitimate vehicles increases, or as the number of antennas at the BS or legitimate vehicle increases. We also obtain the counter-intuitive result that the malicious vehicles optimal number of antennas conditioned on its optimal directional beamformer is equal to the legitimate vehicles number of antennas. The results we provide here are important for the verification of location information reported in IEEE 1609.2 safety messages.

Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks

We develop a new and efficient algorithm to solve the problem of joint channel and Doppler offset estimation in time-varying cooperative wireless relay networks. We first formulate the problem as a Bayesian dynamic nonlinear state space model, then develop an algorithm, which is based on particle adaptive marginal Markov chain Monte Carlo, method to jointly estimate the time-varying channels and static Doppler offsets. We perform detailed complexity analysis of the proposed algorithm and show that it is very efficient and requires moderate computational complexity. In addition, we develop a new version of the recursive marginal Cramér-Rao lower bound and derive expressions for the achievable mean-square error. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms and performs close to the Cramér-Rao lower bound.

Privacy-aware incentive mechanism for mobile crowd sensing

Mobile crowd sensing is an emerging sensing paradigm where sensing applications buy sensor data from mobile smartphone users (workers) instead of deploying their own sensor networks to estimate some statistics of a spatial event. In many spatial monitoring applications, the crowdsourcer needs to incentivize smartphone users to contribute sensing data so that the collected dataset has good spatial coverage. To further incentivize privacy-concerned workers to contribute, we propose a Stackelberg incentive framework that allows workers to specify their location privacy requirements while also increasing the spatial coverage of the collected dataset. We then derive a unique Stackelberg equilibrium which demonstrates the stability of our approach. Our simulation results show that our approach is significantly better in terms of data utility than the non-location-aware and uniform-reward approaches.

Skew-t copula for dependence modelling of impulsive (α-stable) interference

Impulsive interference is a strong limitation in ultra wide band systems or ad hoc networks. However, many work rely on the assumption of independent interference samples which is in many situations an unrealistic assumption. We propose to model the dependence structure using natural extensions to existing interference models based on parameter copula models. We focus on a particular flexible class of models based on the skewed-t copula family. They allow one to capture interesting dependence features based on extremal concordance such as multivariate generalizations of joint extreme correlation known as tail dependence. In the skew-t copula family this can arise in both homogeneous and heterogeneous forms in the extreme quadrants of the multivariate distribution. Importantly, by considering the skew-t copula it is also amenable to efficient scalability to high dimensions. In a second step, we study the impact of these dependence in the receivers performance when they are designed assuming i.i.d. signals.

How to Utilize Sensor Network Data to Efficiently Perform Model Calibration and Spatial Field Reconstruction

This chapter provides a tutorial overview of some modern applications of the statistical modeling that can be developed based upon spatial wireless sensor network data. We then develop a range of new results relating to two important problems that arise in spatial field reconstructions from wireless sensor networks. The first new result allows one to accurately and efficiently obtain a spatial field reconstruction which is optimal in the sense that it is the Spatial Best Linear Unbiased Estimator for the field reconstruction. This estimator is obtained under three different system model configurations that represent different types of heterogeneous and homogeneous wireless sensor networks. The second novelty presented in this chapter relates to development of a framework that allows one to incorporate multiple sensed modalities from related spatial processes into the spatial field reconstruction. This is of practical significance for instance, if there are d spatial physical processes that are all being monitored by a wireless sensor network and it is believed that there is a relationship between the variability in the target spatial process to be reconstructed and the other spatial processes being monitored. In such settings it should be beneficial to incorporate these other spatial modalities into the estimation and spatial reconstruction of the target process. In this chapter we develop a spatial covariance regression framework to provide such estimation functionality. In addition, we develop a highly efficient estimation procedure for the model parameters via an Expectation Maximization algorithm. Results of the estimation and spatial field reconstructions are provided for two different real-world applications related to modeling the spatial relationships between coastal wind speeds and ocean height bathymetry measurements based on sensor network observations.