Realistic Vehicular Scenario Research Articles

A Novel Deep-Learning Model for Remote Driver Monitoring in SDN-Based Internet of Autonomous Vehicles Using 5G Technologies

The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT).

Secure and Efficient Video Transmission in VANET

Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the "Quality of Experience (QoE)" and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP.Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.

An Application-Driven Framework for Intelligent Transportation Systems Using 5G Network Slicing

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

Secure and Efficient Video Transmission in VANET

Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the Quality of Experience (QoE) and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP. Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.

Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing.

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.

Millimetre wave band time domain channel sounder

This study describes a time domain channel sounder based on binary sequences. The channel sounder system consists of several off-the-shelf laboratory instruments and is controlled by a PC. The frequency band of interest is the unlicensed millimetre wave band, 57–64 GHz. A crucial feature of the proposed system is its fast measurement speed enabling measurements of realistic vehicular scenarios. The main part of the study is dedicated to the description and analysis of the system behaviour when nonlinear components, such as mixers, low noise amplifiers or power amplifiers are used. The contributions of this study are the description of how to mitigate these effects utilising several different excitation signals and the evaluation and comparison of the channel sounder performance in terms of the spurious-free dynamic range parameter.

Design and Performance of Secure Geocast for Vehicular Communication

The characteristics of vehicular communication environments and their networking and application requirements have led to the development of unique networking protocols. They enable vehicle-to-vehicle and vehicle-to-infrastructure communication based on the IEEE 802.11 technology, ad hoc principles, and wireless multihop techniques using geographical positions. These protocols, which are commonly referred to as Geocast, greatly support the vehicular communication and applications but necessitate a tailored security solution that provides the required security level with reasonable processing and protocol overhead, as well as reasonably priced onboard and road-side unit equipment. In this paper, we present the design of a security solution for Geocast, which is based on cryptographic protection, plausibility checks using secure neighbor discovery and mobility-related checks, trustworthy neighborhood assessment, and rate limitation. We analyze the achieved security level of the proposed scheme and assess its overhead and performance. Furthermore, we develop a software-based prototype implementation of a secure vehicular communication system. We find that the proposed security measures could result in a network performance bottleneck in realistic vehicular scenarios. Finally, we analyze the tradeoff between security overhead and protocol performance and determine the minimal processing overhead needed for acceptable performance.