On Intercept Probability Minimization under Sparse Random Linear Network Coding

This paper considers a network where a node wishes to transmit a source message to a legitimate receiver in the presence of an eavesdropper. The transmitter secures its transmissions employing a sparse implementation of Random Linear Network Coding (RLNC). A tight approximation to the probability of the eavesdropper recovering the source message is provided. The proposed approximation applies to both the cases where transmissions occur without feedback or where the reliability of the feedback channel is impaired by an eavesdropper jamming the feedback channel. An optimization framework for minimizing the intercept probability by optimizing the sparsity of the RLNC is also presented. Results validate the proposed approximation and quantify the gain provided by our optimization over solutions where non-sparse RLNC is used.Comment: To appear on IEEE Transactions on Vehicular Technolog

High-Speed Data Dissemination over Device-to-Device Millimeter-Wave Networks for Highway Vehicular Communication

Gigabit-per-second connectivity among vehicles is expected to be a key enabling technology for sensor information sharing, in turn, resulting in safer Intelligent Transportation Systems (ITSs). Recently proposed millimeter-wave (mmWave) systems appear to be the only solution capable of meeting the data rate demand imposed by future ITS services. In this poster, we assess the performance of a mmWave device-to-device (D2D) vehicular network by investigating the impact of system and communication parameters on end-users.Comment: To appear in IEEE VNC 2017, Torino, I

Feasibility Study of OFDM-MFSK Modulation Scheme for Smart Metering Technology

The Orthogonal Frequency Division Multiplexing based M-ary Frequency Shift Keying (OFDM-MFSK) is a noncoherent modulation scheme which merges MFSK with the OFDM waveform. It is designed to improve the receiver sensitivity in the hard environments where channel estimation is very difficult to perform. In this paper, the OFDM-MFSK is suggested for the smart metering technology and its performance is measured and compared with the ordinary OFDM-BPSK. Our results show that, depending on the MFSK size value (M), the Packet Error Rate (PER) has dramatically improved for OFDM-MFSK. Additionally, the adaptive OFDM-MFSK, which selects the best M value that gives the minimum PER and higher throughput for each Smart Meter (SM), has better coverage than OFDM-BPSK. Although its throughput and capacity are lower than OFDMBPSK, the connected SMs per sector are higher. Based on the smart metering technology requirements which imply the need for high coverage and low amount of data exchanged between the network and the SMs, The OFDM-MFSK can be efficiently used in this technology.Comment: 6 pages, 11 figures, ISGT Europe 201

Applying Deep Learning Techniques to the Analysis of Android APKs

Malware targeting mobile devices is a pervasive problem in modern life and as such tools to detect and classify malware are of great value. This paper seeks to demonstrate the effectiveness of Deep Learning Techniques, specifically Convolutional Neural Networks, in detecting and classifying malware targeting the Android operating system. Unlike many current detection techniques, which require the use of relatively rigid features to aid in detection, deep neural networks are capable of automatically learning flexible features which may be more resilient to obfuscation. We present a parsing for extracting sequences of API calls which can be used to describe a hypothetical execution of a given application. We then show how to use this sequence of API calls to successfully classify Android malware using a Convolutional Neural Network

Modeling and Design of Millimeter-Wave Networks for Highway Vehicular Communication

Connected and autonomous vehicles will play a pivotal role in future Intelligent Transportation Systems (ITSs) and smart cities, in general. High-speed and low-latency wireless communication links will allow municipalities to warn vehicles against safety hazards, as well as support cloud-driving solutions to drastically reduce traffic jams and air pollution. To achieve these goals, vehicles need to be equipped with a wide range of sensors generating and exchanging high rate data streams. Recently, millimeter wave (mmWave) techniques have been introduced as a means of fulfilling such high data rate requirements. In this paper, we model a highway communication network and characterize its fundamental link budget metrics. In particular, we specifically consider a network where vehicles are served by mmWave Base Stations (BSs) deployed alongside the road. To evaluate our highway network, we develop a new theoretical model that accounts for a typical scenario where heavy vehicles (such as buses and lorries) in slow lanes obstruct Line-of-Sight (LOS) paths of vehicles in fast lanes and, hence, act as blockages. Using tools from stochastic geometry, we derive approximations for the Signal-to-Interference-plus-Noise Ratio (SINR) outage probability, as well as the probability that a user achieves a target communication rate (rate coverage probability). Our analysis provides new design insights for mmWave highway communication networks. In considered highway scenarios, we show that reducing the horizontal beamwidth from $90^\circ$ to $30^\circ$ determines a minimal reduction in the SINR outage probability (namely, $4 \cdot 10^{-2}$ at maximum). Also, unlike bi-dimensional mmWave cellular networks, for small BS densities (namely, one BS every $500$ m) it is still possible to achieve an SINR outage probability smaller than $0.2$.Comment: Accepted for publication in IEEE Transactions on Vehicular Technology -- Connected Vehicles Serie

Agile Calibration Process of Full-Stack Simulation Frameworks for V2X Communications

Computer simulations and real-world car trials are essential to investigate the performance of Vehicle-to-Everything (V2X) networks. However, simulations are imperfect models of the physical reality and can be trusted only when they indicate agreement with the real-world. On the other hand, trials lack reproducibility and are subject to uncertainties and errors. In this paper, we will illustrate a case study where the interrelationship between trials, simulation, and the reality-of-interest is presented. Results are then compared in a holistic fashion. Our study will describe the procedure followed to macroscopically calibrate a full-stack network simulator to conduct high-fidelity full-stack computer simulations.Comment: To appear in IEEE VNC 2017, Torino, I

Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.Comment: Proc. of IET Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms, to appea

A Link Quality Model for Generalised Frequency Division Multiplexing

5G systems aim to achieve extremely high data rates, low end-to-end latency and ultra-low power consumption. Recently, there has been considerable interest in the design of 5G physical layer waveforms. One important candidate is Generalised Frequency Division Multiplexing (GFDM). In order to evaluate its performance and features, system-level studies should be undertaken in a range of scenarios. These studies, however, require highly complex computations if they are performed using bit-level simulators. In this paper, the Mutual Information (MI) based link quality model (PHY abstraction), which has been regularly used to implement system-level studies for Orthogonal Frequency Division Multiplexing (OFDM), is applied to GFDM. The performance of the GFDM waveform using this model and the bit-level simulation performance is measured using different channel types. Moreover, a system-level study for a GFDM based LTE-A system in a realistic scenario, using both a bit-level simulator and this abstraction model, has been studied and compared. The results reveal the accuracy of this model using realistic channel data. Based on these results, the PHY abstraction technique can be applied to evaluate the performance of GFDM based systems in an effective manner with low complexity. The maximum difference in the Packet Error Rate (PER) and throughput results in the abstraction case compared to bit-level simulation does not exceed 4% whilst offering a simulation time saving reduction of around 62,000 times.Comment: 5 pages, 8 figures, accepted in VTC- spring 201