A Traffic Model for Machine-Type Communications Using Spatial Point Processes

A source traffic model for machine-to-machine communications is presented in this paper. We consider a model in which devices operate in a regular mode until they are triggered into an alarm mode by an alarm event. The positions of devices and events are modeled by means of Poisson point processes, where the generated traffic by a given device depends on its position and event positions. We first consider the case where devices and events are static and devices generate traffic according to a Bernoulli process, where we derive the total rate from the devices at the base station. We then extend the model by defining a two-state Markov chain for each device, which allows for devices to stay in alarm mode for a geometrically distributed holding time. The temporal characteristics of this model are analyzed via the autocovariance function, where the effect of event density and mean holding time are shown.Comment: Accepted at the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) - Workshop WS-07 on "The Internet of Things (IoT), the Road Ahead: Applications, Challenges, and Solutions

Vehicle to vehicle (V2V) wireless communications

This work focuses on the vehicle-to-vehicle (V2V) communication, its current challenges, future perspective and possible improvement.V2V communication is characterized by the dynamic environment, high mobility, nonpredective scenario, propagation effects, and also communicating antenna's positions. This peculiarity of V2V wireless communication makes channel modelling and the vehicular propagation quite challenging. In this work, firstly we studied the present context of V2V communication also known as Vehicular Ad-hoc Netwok (VANET) including ongoing researches and studies particularly related to Dedicated Short Range Communication (DSRC), specifically designed for automotive uses with corresponding set of protocols and standards. Secondly, we focused on communication models and improvement of these models to make them more suitable, reliable and efficient for the V2V environment. As specifies the standard, OFDM is used in V2V communication, Adaptable OFDM transceiver was designed. Some parameters as performance analytics are used to compare the improvement with the actual situation. For the enhancement of physical layer of V2V communication, this work is focused in the study of MIMO channel instead of SISO. In the designed transceiver both SISO and MIMO were implemented and studied successfully

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

On the performance of machine-type communications networks under Markovian arrival sources

Abstract. This thesis evaluates the performance of reliability and latency in machine type communication networks, which composed of single transmitter and receiver in the presence of Rayleigh fading channel. The source’s traffic arrivals are modeled as Markovian processes namely Discrete-Time Markov process, Fluid Markov process, Discrete-Time Markov Modulated Poisson process and Continuous-Time Markov Modulated Poisson process, and delay/buffer overflow constraints are imposed. Our approach is based on the reliability and latency outage probability, where transmitter not knowing the channel condition, therefore the transmitter would be transmitting information over the fixed rate. The fixed rate transmission is modeled as a two-state Discrete-time Markov process, which identifies the reliability level of wireless transmission. Using effective bandwidth and effective capacity theories, we evaluate the trade-off between reliability-latency and identify QoS requirement. The impact of different source traffic originated from MTC devices under QoS constraints on the effective transmission rate are investigated

Detecting ADS-B Spoofing Attacks using Deep Neural Networks

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to examine each incoming message based on the PHY-layer features (e.g., IQ samples and phases) and flag suspicious messages. Our experimental results show that SODA detects ground-based spoofing attacks with a probability of 99.34%, while having a very small false alarm rate (i.e., 0.43%). It outperforms other machine learning techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It further identifies individual aircraft with an average F-score of 96.68% and an accuracy of 96.66%, with a significant improvement over the state-of-the-art detector.Comment: Accepted to IEEE CNS 201

Traffic classification and prediction, and fast uplink grant allocation for machine type communications via support vector machines and long short-term memory

Abstract. The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving machine type communication (MTC) applications. Therefore, 3GPP has introduced the need to use fast uplink grant (FUG) allocation. This thesis proposes a novel FUG allocation based on support vector machine (SVM) and long short-term memory (LSTM). First, MTC devices are prioritized using SVM classifier. Second, LSTM architecture is used to predict activation time of each device. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. Furthermore, a set of correction techniques is introduced to overcome the classification and prediction errors. The Coupled Markov Modulated Poisson Process (CMMPP) traffic model is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model based CMMPP is used to evaluate the proposed algorithm in a more dense network. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay when serving the target massive and critical MTC applications