Fog-Assisted Cooperative Protocol for Traffic Message Transmission in Vehicular Networks

Traffic information exchange between vehicles and city-wide traffic command center will enable various traffic management applications in future smart cities. These applications require a secure and reliable communication framework that ensures real-time data exchange. In this paper, we propose a Fog-Assisted Cooperative Protocol (FACP) that efficiently transmits uplink and downlink traffic messages with the help of fog Road Side Units (RSUs). FACP divides the road into clusters and computes cluster head vehicles to facilitate transmission between vehicles and traffic command center or fog RSUs. Using a combination of IEEE 802.11p and C-V2X wireless technologies, FACP minimizes the time required by a vehicle to retrieve traffic information. Furthermore, FACP also utilizes cooperative transmissions to improve the reliability of traffic messages. Simulations results show that FACP improves the reception rate and end-to-end delay of traffic messages

VANET based advanced road traffic management systems

Masters Research - Master of Philosophy (MPhil)With the increasing number of vehicles on the road, the demand for advanced traffic control systems is on the rise. In future, Intelligent Transportation System (ITS) is envisaged to be a key component of the road traffic management system. To maximize the efficiency of the traffic control system, various ITS applications could be used along with the existing control methods by integrating communications, computing and electronic technologies. A new wireless networking standard known as the IEEE 802.11p has been exclusively developed for the VANET (Vehicular Ad-hoc Network) based next generation transportation systems. The IEEE 802.11p standard can support both V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) communications mode. For a VANET based ITS, the V2I mode can be used for exchanging signals between traffic control entities and vehicles. The aim of this research is to utilize a V2I communication architecture to accommodate and integrate two novel applications of an advanced ITS, namely the IRTSS (Intelligent Road Traffic Signalling System) and the PRTMS (Predictive Road Traffic Signalling System). A novel IRTSS has been proposed and implemented using the V2I based VANET architecture to control traffic flow both at isolated and coordinated road intersections. Furthermore, a basic Predictive Road Traffic Management System (PRTMS) has been developed and integrated with the VANET architecture to achieve multi-junction traffic flow control. An OPNET based integrated simulation model has been developed that jointly examines the performance of the proposed road traffic management system and the communications network. A realistic road traffic flow has been embedded in the simulation model that complies with the international road traffic control standards proposed by Vienna Convention on Road Signs and Signals, AsutRoads and United States Department of Transportation (U.S. DoT). Both proposed ITS applications are based on the VANET architecture which could be implemented for a city size road network. The proposed vehicle detection system is relatively advanced compared to the existing sensor based detection systems. The thesis presents a unique co-simulation model that incorporates both road infrastructure, controlled vehicle mobility and a model of a VANET network. Simulation results are analysed to characterise the VANET based IRTSS and PRTMS systems for a wide area traffic control system. The results indicate that the proposed architecture can efficiently detect and control traffic flows in a large road network with minimum hardware/software resources compared to the existing vehicle detection methods

A novel vehicular mobility modelling technique for developing ITS applications

This paper presents a novel technique to extract important road parametric values from a validated microscopic mobility model based on VanetMobiSim. The model simulates a 100 km² urban road scenario taken from the area of Newcastle, Australia and measures various route parameters at different vehicle densities on the road. The mobility traces obtained from the model were analyzed in MATLAB to derive the road informatics such as route based speed, journey time, average waiting time and normalized traffic density at a particular intersection in an open loop manner. It is expected that the informatics will contribute to the development of various Vehicular Ad-hoc Network (VANET) applications such as motor or vehicular tailpipe emission modelling, minimizing traffic congestions including fuel consumption and intersection management

Traffic flow model for vehicular network

Intelligent Traffic Management (ITM) system promises to reduce the traffic congestion, intelligent intersection management as well as the smart dissemination of the informatics to control the traffic flow. Development of any Intelligent Transportation System (ITS) applications requires realistic road traffic model to analysis the performance. Service rate or service time is one of the most important components of any vehicular traffic model to analyze its properties. Traffic flow model which is based on any existing queuing methods have different properties to analyze such delay, queuing length, saturation flow (for fixed cycle of signaling period) etc. Depending on the on road traffic capacity and the intensity of the vehicle the service rate will determine and also the mentioned properties. In this paper, the model involves the analysis of the traffic parameters of an intersection with MM1 queuing system to optimize the service rate that is needed to develop the long term evaluation (LTE) based intersection management system (IMS)

Design and characterization of single photon APD detector for QKD application

Modeling and design of a single photon detector and its various characteristics are presented. It is a type of avalanche photo diode (APD) designed to suit the requirements of a Quantum Key Distribution (QKD) detection system. The device is modeled to operate in a gated mode at liquid nitrogen temperature for minimum noise and maximum gain. Different types of APDs are compared for best performance. The APD is part of an optical communication link, which is a private channel to transmit the key signal. The encrypted message is sent via a public channel. The optical link operates at a wavelength of 1.55μm. The design is based on InGaAs with a quantum efficiency of more than 75% and a multiplication factor of 1000. The calculated dark current is below 10-12 A with an overall signal to noise ratio better than 18 dB. The device sensitivity is better than -40 dBm, which is more than an order of magnitude higher than the dark current, corresponding to a detection sensitivity of two photons in pico-second pulses

Quintuple band antenna design using stacked series array for millimeter wave

Millimeter wave applications require efficient array antenna designs to fully utilize the spectrum. A quintuple band antenna is proposed for millimeter wave applications. Dual-layer substrate technology is utilized to achieve multiple resonances at higher frequencies ranging from 26GHz-40GHz. Twenty-five antenna array configurations were simulated and analyzed to develop the model. The resonances achieved correspond to the number of radiating patches. The operating frequencies of this design correspond with single band 27.64 GHz, dual band 24.18, 28.77 GHz, triple band resonances 23.18, 25.63, 35.79 GHz, quadruple band resonances 24.58, 26.77, 29.33, 34.43 GHz and quintuple band resonances 23.79, 25.37, 28.29, 31.69, 33.53 GHz. The proposed quintuple band antenna can be used for millimeter wave applications in all bands

Inter-cluster synchronization scheme for femtocell network

Femtocell is for indoor coverage, and cannot use a GPS antenna for time synchronization. High-precision crystal oscillators can solve the timing problem, but they are often too expensive for consumer grade devices. Since fBS and macrocell Base Station (mBS) network operate on the same frequency under a licensed spectrum fBS network can interfere with the macrocell network. In addition, fBSs can also interfere with each other if multiple units are in close proximity. In some cases, conflicting base stations may simultaneously raise transmit power in order to improve signal quality, thereby creating some interference. Furthermore, in a flat fBS structured network using IEEE 1588 synchronization algorithm and fBS-fBS synchronization scheme creates offset and frequency error which results inaccurate synchronization. However, in this paper an inter-cluster synchronization scheme is proposed in order to reduce offset and frequency error (skew) to achieve precise synchronization in fBS neighbor nodes. The analytical result shows that the proposed scheme is able to reduce the offset and skew significantly through inter-cluster synchronization ultimately increases synchronization accuracy. Therefore, achieved synchronization accuracy is 90% higher than the existing MS-assisted schemes

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted

Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications