Lightweight cryptographic URLLC for 5G-V2X

Background. Ultra-Reliable Low Latency Communication (URLLC) as a service offered by fifth and sixth generation (5G/6G) wireless systems is a technological response to the needs of various mission-critical applications that require reliable data transmission with low latency. These applications also include Intelligent Transportation Systems services, which, among other things, provide connectivity and autonomous vehicle control. The combination of high reliability and low latency requirements in URLLC usage scenarios creates a security problem for URLLC data transmission that cannot be solved using conventional complex cryptographic methods based on a secret key. The article discusses in detail the approach to using physical layer security mechanisms (physical layer security - PLS) as a powerful alternative to classical cryptographic security methods for URLLC, and also proposes the application of the wire-tap channel concept in URLLC with an analysis of the efficiency that can be achieved for physical layer security. Objective. The aim of the article is to provide an overview of information security solutions in URLLC usage scenarios, as well as to propose a constructive method for information protection for reliable data transmission with low latency without the use of cryptographic mechanisms based on a secret key. Methods. Theoretical research in the field of the branch channel concept was used to create solutions that allow data protection with information-theoretic stability in URLLC usage scenarios for providing IoT, connected car and autonomous driving services. Results. The article examines in detail the data security issues in ultra-reliable low latency communication (URLLC) as a service offered by fifth and sixth generation (5G/6G) wireless systems. It is determined that URLLC is a technological response to the needs of various critical applications that require reliable signal transmission with low latency, and among these applications are Intelligent Transportation Systems services, which, among other things, provide connectivity and autonomous vehicle control. It is shown that the combination of high reliability and low latency requirements in URLLC scenarios creates a security problem for URLLC data transmission that cannot be solved using conventional complex cryptographic methods based on a secret key. The feasibility of using physical layer security mechanisms (PLS) as a powerful alternative to classical cryptographic security methods for URLLC is substantiated. The approach to applying the concept of a wire-tap channel in URLLC is considered in detail, as well as the results that can be achieved for physical layer security, and the influence of code parameters for probabilistic cryptographic transformations in accordance with the concept of a wire-tap channel on PLS URLLC. Estimates of the effectiveness of PLS URLLC for finite block length codes are provided. Conclusions. An effective way to ensure data security for ultra-reliable low-latency physical layer link (PLS URLLC) of fifth-generation 5G wireless systems in the field of connected cars and vehicles of 4-5 levels of automation can be approaches based on the concept of a tapped channel ("wire-tap channel").

Long Term Evolution (LTE) network offers a high throughput with low latency that makes it the best choice for Evolved Multimedia Broadcast Multicast Service (E-MBMS). E-MBMS transmits data to all users simultaneously using the same channel and same Modulation and Coding Scheme (MCS) level. Each user experiences different channel condition, so an MCS level should be selected properly to satisfy all users. Typically, MCS level is selected on the basis of users' worst channel condition which results in wasting network resources. Since the E-MBMS performance directly depends on the selected MCS level, an efficient Adaptive Modulation and Coding (AMC) scheme is essentially needed. This paper introduces a new mechanism that uses the Standard Deviation (SD) of MCS level of E-MBMS user group operated on single cell mode to efficiently select its optimal MCS level. Simulation results show that the proposed mechanism increase the spectrum efficiency, throughput, and lower Packet Loss Ratio compared to the Worst User Channel Condition mechanism and Fixed Predefined MCS mechanism.

In the future, mixed traffic flow will include two types of vehicles: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to disrupt the traditional transportation system. This new solution shares real-time data with each other and the roadside units (RSU) for network management. Reinforcement learning (RL) is a promising approach for traffic signal management in complex urban areas by leveraging information gathered from CAVs. In particular, coordinating signal management at many intersections is a critical challenge in multi-agent reinforcement learning (MARL). According to this vision, we propose an approach that combines an actor–critic network–based multi-agent deep deterministic policy gradient (MADDPG) model and a rerouting technique (RT) to increase traffic performance in vehicular networks. This algorithm overcomes the inherent non-stationary of Q-learning and the high variance of policy gradient (PG) algorithms. Based on centralized learning with decentralized execution, the MADDPG model employs one actor and one critic for each agent. The actor network uses local information to execute actions, while the critic network is trained with extra information, including the states and actions of other agents. Through a centralized learning process, agents can coordinate with each other, diminishing the influence of an unstable environment. Unlike previous studies, we not only manage traffic light systems but also consider the effect of platooning vehicles on increasing throughput. Experimental results show that our model outperforms other models in terms of traffic performance in different scenarios.

Ultra-reliable low-latency communication (URLLC) is specified in 5G New Radio to serve the applications and services with the strict requirements of reliability and latency. The URLLC operation is in licensed spectrum then extended to unlicensed spectrum to support new use cases in the industrial scenario. This requires new features in physical layer to make URLLC work with the strict requirements in both licensed and unlicensed spectrum. This paper focuses on the design of physical uplink shared channel (PUSCH) repetitions for URLLC. In PUSCH repetition, the nominal PUSCH repetitions might be segmented into the smaller actual repetitions due to uplink/downlink (UL/DL) directions in time division duplex (TDD) configuration or slot boundary. This segmentation causes a degradation of the URLLC performance due to a smaller number of valid symbols for PUSCH repetitions, a drop of the repetition in an orphan symbol and an increase of listen before talk (LBT) overhead. To enhance the URLLC performance, two schemes are proposed. The first scheme deals with segmentation due to UL/DL directions by dynamically switching the chosen semi-static DL symbols to UL symbols. The second scheme deals with orphan symbols by transmitting signal in these symbols in order to maintain a continuous PUSCH transmission and avoid an additional LBT. The results show an improvement of URLLC performance in licensed and unlicensed spectrum by applying these two schemes.

With the prosperity of emerging applications, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6^{th}$ </tex-math></inline-formula> Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

A Smartphone VANET Based Forward Collision Detection System

Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.

A new concept such as smart city was introduced in the last years where the Intelligent Transportation system (ITS ) plays a critical role to provide road safety and manage Vehicular Ad Hoc Networks (VANETs) traffic. Nevertheless, VANETs have significant constraints like nodes high mobility, intermittent connectivity, variable network density and heterogeneity. However, the different types of traffic, the different Quality of Service requirements, the need to exchange mobile data, multi-services and data diversity leads mainly to load and time constraints in this specific and stringent type of networks. The main characteristic of this kind of networks is the very changing topology that poses supplementary constraints and makes achieving QoS constraints a very challenging task. In VANET network the vehicle generated traffic will be transferred to the data center from road side unit to the base station by using Long Term Evolution (LTE) in an urban area. Despite LTE has a larger system capacity and it provides a higher transmission speed, the network performance is affected by the implemented scheduling algorithm. In this context, we study the efficiency of LTE scheduler algorithms such as Proportional Fairness, Round Robin, Priority Set Scheduler, Maximum Throughput Scheduler and Throughput to Average Scheduler and Blind Equal Throughput mainly at the road side unit using Network Simulator 3(NS3) to determinate the most suitable scheduler for VANET traffic. Results demonstrate that the round robin algorithm is more effective for volumetric VANET traffic in terms of throughput, delay, packet loss rate and fairness.

In urban vehicular network, existing routing protocols using beacon message scheme and P2P (peer to peer) density discovery scheme can hardly make a good performance as well as keep a high feasibility. This paper proposes an IGOR (improved geography and overlay routing) in urban vehicular environment. In IGOR, RSUs (road side units) are introduced to support the road density discovery. A passive neighbor discovery scheme is used by OBUs (on board units) instead of the beacon message scheme. The performance of IGOR is evaluated against the benchmark routing protocol of LOUVRE (landmark overlays for urban vehicular routing environment). Results show that IGOR has a higher accuracy in density discovery as well as a lower cost of the routing establishment and maintenance.

Along with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

Live streaming services inside the Internet-of-Vehicles (IoV) are becoming more significant in vehicle entertainment systems as a result of the fast-paced growth of the automotive industry and communications technologies. At the same time, with the rapid development of in-vehicle video, the development of broadcasting business has been driven. The 3GPP standardization group has suggested the evolved Multimedia Broadcast Multicast Service (eMBMS), which by multicasting video services to vehicle users, can significantly enhance the utilization of spectrum resources and improve signal quality. Real-time video is sent by eMBMS over synchronized spectral resources of nearby roadside units (RSUs) using a single frequency networks (SFN). However, the video transmission rate depends on the vehicle users with the lowest channel state information, to overcome these limitations, the vehicle users can be divided into groups in the SFN. It is incredibly difficult to maximize the user quality of service (QoS) of a vehicle due to dynamic nature of IoV’s radio resources and channels. To overcome this obstacle, we employ a model SFN video transmission strategy that, while grouping vehicles, jointly optimizes group-oriented bit rate selection and resource allocation to decrease latency and bit rate switch in IoV. The Markov decision process (MDP), which takes into account the time-varying features of wireless channels, is used to describe the joint optimization problem. Then after, the abovementioned MDP is handled by using soft actor-critic (SAC) algorithm. The proposed approach may substantially enhance video quality while reducing latency and bit rate switch, according to extensive simulation findings based on actual data. It also works well in terms of learning efficiency and stability.KeywordsInternet-of-vehicleseMBMSSFNQoS

Global misbehavior detection in Cooperative Intelligent Transport Systems (C–ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle’s On–Board Units (OBUs) and by Road–Side Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML)-based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.

As urbanization grows and the cost of vehicle production decreases, urban traffic has become a major problem of modern life. Developing intelligent vehicles alongside standardizing inter-vehicle communications promises that this technology will be a part of future life. In this research, we propose a method in which clustering is used to gather vehicles' movements information in a vehicle ad-hoc network. This method is based on extending green wave using road-side units as a fixed agent and on board units (OBU) in vehicles as a mobile agent. This information is then transmitted to traffic lights for decision making. This algorithm evaluated using Monte Carlo simulation. The simulation results show this method has a positive effect on reducing the average waiting time and overall stop of the vehicles behind the traffic lights.

This paper proposes a roadside unit (RSU)-coordinated synchronous multi-channel medium access control (MAC) scheme for vehicular ad hoc networks (VANETs). The proposed scheme allows the on board unit (OBU) to reserve service channels (SCHs) on the control channel (CCH) during almost the whole synchronous interval and supports simultaneous transmissions on different SCHs. It enhances the performance of VANETs and decreases the CCH congestion. Moreover, we use RSU to record the rendezvous information and broadcast it to the OBUs. This method avoids the multi-channel hidden terminal problem. An analytical model is developed to evaluate the aggregate throughput on SCHs. This model considers the following factors: non-safety message transmission probability in each synchronous interval, average size of the non-safety message, and the number of OBUs. Furthermore, the requirement of obtaining the maximum throughput is computed. Simulation results are provided to validate the analytical model and to demonstrate the improvement in throughput. The results indicate that the proposed scheme can provide higher aggregate throughput than that of the vehicular enhanced multi-channel MAC and IEEE 1609.4, and better performance in CCH congestion control especially under high network load conditions.