We present a generic statistical characterization of the vehicle-to-vehicle (V-V) wireless channel by adopting a stochastic modeling approach. Our approach is based on the doubly underspread (DU) property of non-wide sense stationary uncorrelated scattering (non-WSSUS) wireless channels, with V-V channels pertaining to this category. DU channels exhibit explicit frequency and time intervals over which they are approximated as WSSUS. We call these intervals restricted time interval (RTI) and restricted bandwidth (RBW), and variations taking place inside them are characterized as small scale variations. Large scale variations take place outside RTI and RBW. In this paper, we focus on small scale variations, thus, our modeling finds its applicability within RTI and RBW. As practical V-V channels exhibit rapid

60GHz short-range wireless transmissions are susceptible to shadowing loss due to the inherent human activity obstructing the line-of-sight (LOS) path [4]- [8]. Shadowing loss is further influenced because of the utilization of directional antennas to overcome the increased path loss and effects of multipath propagation that are present at these frequencies [9]. It is wellknown that spatial shadowing variations in dB scale are modeled by a Gaussian distribution [10], [11]. However, there is not a rigorous statistical description in the published literature for the temporal variability of shadowing of LOS paths due to human activity. This seems surprising due to the significant impact human body shadowing has on 60GHz wireless links. Very recent advances based on measurements and simulations revealed that knowledge of human body shadowing is essential to estimate the performance [12] and

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(2017) Energy detection based spectrum sensing over two-wave and diffuse power fading channels. IEEE Transactions on Vehicular Technology, 66(1), pp. 868-874. (doi:10.1109868-874. (doi:10. /TVT.2016 This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it. Abstract-One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This article investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse than Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing as well as square law selection diversity reception are derived. These expressions are used to analyze the behavior of EDbased spectrum sensing over moderate, severe and extreme fading conditions, and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. The obtained results indicate that TWDP fading conditions can significantly degrade the sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable identifying the limits of ED-based spectrum sensing and quantifying the tradeoffs between detection performance and energy efficiency for cognitive radio systems deployed within confined environments such as in-vehicular wireless networks.

Abstract-The physical attributes of the dynamic vehicle-to-vehicle (V2V) propagation channel can be utilised for the generation of highly random and symmetric cryptographic keys. However, in physical-layer key agreement, non-reciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements which have to be addressed prior to the symmetric key generation which is inherently important in social IoT networks. This work parametrically models temporal variability attributes such as 3D scattering and scatterers' mobility and for the first time incorporates such features into the key generation process by combining non-reciprocity compensation with turbo codes. Preliminary results indicate a significant improvement in bit mismatch rate (BMR) and key generation rate (KGR) when compared with sample indexing techniques.

In frequency non-selective fading channels the multipath components can arrive at the mobile receiver via a three dimensional (3-D) scattering mechanism. That case occurs especially in urban environments, in which the tall buildings and other obstacles cause an arrival of multipath energy in the elevation plane, besides that arriving in the azimuth one. Another issue, which is a matter of investigation, is that the multipath energy may arrive at the mobile receiver in specific angular sectors. This is caused when a part of energy is blocked by the channel obstacles, or no multipath energy arrives from certain directions, due to lack of scattering objects in those directions, or directional antennas are employed. In this paper we propose a model which takes into account both 3-D multipath scattering and partial arrival of multipath energy. The proposed model assumes that the multipath components arrive at specific angular sectors in the azimuth receiver's plane, whereas in the elevation plane the angles of arrival are of continuous nature. Moreover a specular component with constant amplitude also exists. From the closed form autocorrelation function, the Doppler power spectral density (PSD) of the model is analytically derived. Afterwards the probability density function (PDF) of the envelope and phase are analytically calculated. What follows are the second order statistics, level crossing rate (LCR) and average duration of fades (ADF's), in closed form.

Abstract-Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. Firstly, we manifest the opportunity of behavioural profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilise a new dataset file for connected cars influenced by the Automatic Dependent Surveillance -Broadcast (ADS-B) surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real-time to predict anomalies using predictive modelling. Simulations show the applicability of a Bayesian estimation technique, namely Kalman Filter. With the analysis of future state predictions based on the previous behaviour, cyber-threats can be addressed with a vastly increased time-window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with predictive profiling and behavioural algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behaviour. When quicker action can be taken while connected cars encounter cyber-attacks, more effective engagement or interception of command and control will be achieved.