Cooperative Vehicular Networks Research Articles

A Zero-Trust Approach for Cooperative Vehicular Networks: A Motorway Use Case

A Zero-Trust Approach for Cooperative Vehicular Networks: A Motorway Use Case

Securing cooperative vehicular networks amid obstructing vehicles and mixed fading channels

Smart cities (SCs) were founded on the basis of the Internet of Vehicles paradigm, aiming to enhance the quality of life. Despite not having a unique composition, the intelligent transportation services (ITS) is a key component in most of the SCs. The ITS integrates the new communication systems along with the traditional transportation systems, forming a universal network of static and vehicular entities communicating over wireless broadcast channels. In addition to its vulnerability to co-channel interference (CCI), eavesdropping, and fading, the vehicle-to-vehicle communication channels could be subjected to shadowed fading. That is due to the probabilistic obstruction by big vehicles. Hence, degrading the signal-to-noise ratio at the receiving vehicle severely. This work evaluates the secrecy performance of a practical cooperative vehicular relaying network in terms of its secrecy outage probability (SOP). Due to the probabilistic existence of a vehicular obstacle, three different communication scenarios are considered and a novel closed-form analytical expression for the SOP is obtained over mixed Nakagami-m fading and Nakagami-N-Gamma shadowed fading channels. The system comprises legitimate source and destination operating in full-duplex mode, thus imposing CCI on both the relay and the passive eavesdropper. The result demonstrate the impacts of the secrecy data rate threshold, the strength of shadowing, the density of the obstructing vehicles, and several parameters on the SOP. High levels of shadowing strength and density of obstructing vehicles significantly degrades the secrecy performance by increasing the SOP. Accordingly, adopting the mixed fading channel model is of great importance for practical network modeling.

Security-Aware Resource Allocation Scheme Based on DRL in Cloud–Edge–Terminal Cooperative Vehicular Network

Security-Aware Resource Allocation Scheme Based on DRL in Cloud–Edge–Terminal Cooperative Vehicular Network

Cooperative vehicular networks over Nakagami-[formula omitted] fading: Joint power control and spectrum scheduling

Facilitated by the rapid development of wireless communication technologies and the broad deployment of cellular networks, vehicular communications have evolved towards ultra-reliable low-latency communications (URLLC). In this paper, we consider a device-to-device (D2D)-enabled vehicular networks, where the uplink from roadside units (RSUs) to the base station (BS) shares spectrum with D2D-enabled vehicle-to-vehicle (V2V) links. The sum capacity of RSUs is maximized by jointly optimizing the transmit power and orthogonal spectrum bands scheduling, with the minimum data rate of RSUs and the reliability of V2V links guaranteed. Specifically, under Nakagami fading channels, the statistical channel state information (CSI) of vehicular communication links is utilized instead of the instantaneous CSI to adapt to the extra overheads resulting from frequent channel estimation. Moreover, user fairness is also considered, through maximizing the minimum ergodic capacity achieved by RSUs. To solve the formulated non-convex problem, we decomposed it into a power allocation subproblem that is analytically solved, and a spectrum bands scheduling subproblem that is tackled by resorting to the Hungarian method. The validity of the proposed methods is confirmed by numerical results.

Outage performance prediction of cooperative vehicle network based on sparrow search algorithm based on back‐propagation neural network

AbstractWith the support of the sixth‐generation mobile networks (6G) technology, the Internet‐of‐Vehicle (IoV) can realize the perception and monitoring of vehicle road information. However, due to the change of network topology and various environment, the reliable performance of the communication link is facing challenges. For the sake of improving communication quality, a cooperative vehicular network (CVN) system is established, which adopts cooperative communication and multiple input multiple output (MIMO) technology. According to the signal‐to‐noise ratio (SNR) threshold of relay vehicles, using hybrid decode‐amplify‐forward (HDAF) protocol and combining with antenna selection, the analytical expression of outage probability (OP) with Meijer‐G function is obtained. For predicting the OP accurately, the sparrow search algorithm based on back‐propagation neural network (SSA‐BPNN) is put forward. The simulation results show that the cascade order of the channels has a negative effect on the OP. Meanwhile, the prediction accuracy of SSA‐BPNN is 64.8% higher than that of BPNN, and 98.96% greater than that of general regression neural network, and the convergence rate is faster than ICS‐BPNN.

Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success rate of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a Contribution-based Dual-Clip Proximal Policy Optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all the subproblems are solved. Simulation results reveal that proposed algorithm can outperform the other algorithms in terms of the MOS and transmission success rate.

Aerial-Ground Cooperative Vehicular Networks for Emergency Integrated Localization and Communication

Aerial-Ground Cooperative Vehicular Networks for Emergency Integrated Localization and Communication

Joint perception data caching and computation offloading in MEC-enabled vehicular networks

The accelerated use of intelligent vehicles and the advancement of self-driving technologies have posed significant problems to the provision of real-time vehicular services, such as enormous amounts of computation, long transmission delay, and integration of sensor data. In this context, we propose to solve these problems to guarantee the Quality of Services (QoS) using computation offloading and perception data caching methods, where perception data means combinatorial sensor data, including sensor values and corresponding locations in an area. At first, we present a cooperative vehicular network framework with edge computing and sensor devices. Taking into account vehicle mobility, distributed resources, task properties, and user preferences, we jointly formulate a computation offloading strategy and a perception data caching strategy to minimize the average execution latency, which can be considered a Mixed-Integer Non-Linear Programming (MINLP) problem. We first propose a Genetic Algorithm (GA)-based scheme to solve our formulated problem. Then we propose a heuristic named Correlation-Monte Carlo Fast Search (CMCFS) method to obtain an effective strategy with low complexity. Simulation results reveal that both GA and CMCFS achieve less latency than other baseline schemes.

Cooperative vehicular networks: An optimal and machine learning approach

Intelligent Transport Systems (ITS) provide a promising technology to enhance road safety. The vehicular standard wireless access in vehicular environment (WAVE), also known as dedicated short-range communication (DSRC), can assist in reducing the number of deadly crashes. However, DSRC has a limited range. To enhance the network coverage, roadside units (RSUs) are placed along the road. However, the placement of RSUs at every instant increases the infrastructure cost. In this paper, we proposed the cooperative vehicular architecture, with network function virtualization (NFV) enabled RSU inside the mobile edge computing (MEC) unit. RSUs are only placed in the dense traffic region. We applied the Long short-term memory (LSTM) based machine-learning algorithm to predict the traffic flow based on the vehicle information table (VIT) maintained at the MEC unit. NFV is implemented at the top of RSU. Based on predicted traffic density it assists RSU to enhance its coverage range by exploiting the transmit power. Furthermore, MEC is also responsible for cooperative relay-based communication. Optimal stopping theory is modeled to select the best candidate relay node immediately. In this paper, we tested the proposed scheme in actual on-road vehicles and through simulations performed in network simulator NS-3.

Physical layer security of vehicular networks with cooperative jamming helpers

As an important part of the intelligent transportation system (ITS), vehicular networks can provide drivers and passengers with more comfortable and convenient services such as efficient traffic management and infotainment. However, the security threats on data exchanges over vehicular networks have become increasingly severe. Different from conventional cryptographic technologies, the application of physical layer security (PLS) to vehicular networks has been investigated to prevent the security of exchanging data from the eavesdropper and measure precisely the leaked information to the eavesdropper, due to its low complexity and communication overhead. In this work, we are concerned with the PLS of cooperative vehicular networks consisting of a source vehicle, a destination vehicle, an eavesdropping vehicle and a cooperative jamming vehicle. First, to improve the secrecy performance, the cooperative jamming helper emits jamming signals to degrade the eavesdropping channel without harming the legitimate channel. Then, based on the Rayleigh fading channel models and the traffic models, the closed-form expressions of the secrecy outage probability (SOP) and the average secrecy capacity (ASC) of the considered vehicular networks are derived, which deliver more implications of various system parameters on SOP and ASC performances and can be computed without simulations at a lower complexity. Second, a definition of the optimal jamming vehicle is introduced and then the cooperative jamming vehicle selection strategy is presented. The existence of the optimal jamming vehicle is measured in probability, which is explored analytically. Third, the optimal power allocation that maximizes the secrecy capacity is found analytically for the source vehicle and the cooperative jamming helper. Finally, simulations are also presented to demonstrate the validation of these analytical results and confirm the advantages of the cooperative jamming strategy and the optimal power allocation. From the numerical results, more observations on the effects of the main system parameters on secrecy performances are obtained, which provides some useful guides for practice.

Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles

AbstractThe internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.

Generalized Quadrature Spatial Modulation and its Application to Vehicular Networks With NOMA

Quadrature spatial modulation (QSM) is recently proposed to increase the spectral efficiency (SE) of SM, which extends the transmitted symbols into in-phase and quadrature domains. In this paper, we propose a generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain. A low-complexity detection scheme for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method. An upper bounded bit error rate is analyzed to discover the system performance of GQSM. Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. Computer simulation results verify the reliability of the proposed low-complexity detection as well as the theoretical analysis, and show that GQSM outperforms QSM in the entire SNR region. The superior BER performance of the proposed C-NOMA-GQSM scheme make it a promising modulation candidate for next generation vehicular networks.

Performance Analysis of Cellular-Relay Vehicle-to-Vehicle Communications

Vehicle-to-vehicle (V2V) communication-based cooperative vehicular networks are a key technology for future smarter transportation systems as they provide various applications for addressing road safety and traffic congestion. To alleviate the performance limitations of existing dedicated short-range communication (DSRC) protocols, cellular-assisted V2V communications have been proposed recently, wherein cellular base stations (BSs) relay the transmission from one vehicle to another. Such cellular-assisted communications have shown promise for more reliable V2V communications with a wider capacity and longer transmission distances. In this study, we theoretically analyze the performance of cellular-relay (CR) V2V communications, in which a vehicle first transmits a message to its nearest BS via uplink, and then, the BS forwards the message to a destination vehicle via downlink. We model the road segments through a Poisson line process and the positions of vehicles through a Poisson point process on the roads; subsequently, we derive a theoretical expression for the probability of a successful CR transmission and the mean local delay in the CR transmission. Furthermore, we propose a performance metric to evaluate the difference between the performances of CR and direct V2V communications, and it can be applied to automatic transmission mode selection (i.e., CR or direct) for V2V communications. We evaluate the analytical results using numerical examples and demonstrate the impacts of various system parameters on the performances of CR and direct V2V communications.

Optimizing Resource Allocation for 6G NOMA-Enabled Cooperative Vehicular Networks

In recent years, the concept of non-orthogonal multiple access (NOMA) has gathered much attention due to its potential to offer high spectral efficiency, present user fairness and grant free access to sixth generation (6G) vehicular networks. This paper proposes a new optimization framework for NOMA-enabled cooperative vehicular network. In particular, we jointly optimize the vehicle paring, channel assignment, and power allocation at source and relaying vehicles. The objective is to maximize the sum rate of the system subject to the power allocation, minimum rate, relay battery lifetime and successive interference cancelation constraints. To solve the joint optimization problem efficiently, we adopt duality theory followed by Karush-Kuhn-Tucker (KKT) conditions, where the dual variables are iteratively computed through sub-gradient method. Two less complex suboptimal schemes are also presented as the benchmark cooperative vehicular schemes. Simulation results compare the performance of the proposed joint optimization scheme compared to the other benchmark cooperative vehicular schemes.

Robust Cooperative Communication Optimization for Multi-UAV-Aided Vehicular Networks

Aerial-ground cooperative vehicular networks are envisioned as a novel paradigm in B5G/6G visions. In this letter, the challenge of optimizing the global energy-efficiency (EE) of multi-UAV-aided vehicular networks in the presence of uncertain air-to-ground (A2G) channels is addressed. Specifically, we propose a maximin paradigm to characterize the system, which aims to maximize its global EE meanwhile satisfying Quality-of-Service (QoS)-oriented data rate requirements in the worst-case situation. We theoretically derive a closed-form optimal solution for an embedded minimization subproblem under a parametric channel uncertainty set and thus develop a computationally tractable robust counterpart, which leads to a robust EE optimization design. Simulation results show that the proposed method significantly outperforms conventional EE schemes in terms of achieving higher global system performance and better robustness under random uncertain environments.

Secrecy Performance of Cooperative Cognitive AF Relaying Networks With Direct Links Over Mixed Rayleigh and Double-Rayleigh Fading Channels

This paper investigates the secrecy performance of an underlay cooperative cognitive relaying network, wherein a secondary source vehicle communicates with a fixed secondary destination terminal via a direct link and with the assistance of a secondary amplify-and-forward relay vehicle in the presence of a passive secondary eavesdropper vehicle, taking into consideration of interference at the primary user. We assume that the eavesdropper vehicle takes the advantages of both the relay link and direct link. We consider that vehicle-to-vehicle links are modeled as double-Rayleigh fading, while vehicle-to-fixed infrastructure links are modeled as Rayleigh fading. Such a scenario finds it relevancy in vehicle-to-vehicle communication and/or vehicle-to-infrastructure communication under spectrum sharing heterogeneous cooperative vehicular networks. For such a realistic scenario, in particular, we derive a tight lower bound expression of the secrecy outage probability under mixed Rayleigh and double-Rayleigh fading channels. We also present an effective secrecy diversity order analysis and show that the considered system can achieve a secrecy diversity order of 2 for infinitely large average channel gain values of the main links. Finally, we demonstrate the accuracy of our analytical findings via numerical and simulation results and show the impact of channel conditions, primary interference constraints, and direct links on the secrecy performance of the considered system.

Capacity Analysis for Cooperative Vehicular Networks with Different Fading Channels

The cooperative vehicular networks (CVNs) enable vehicles to obtain information of remote infrastructures through cooperative vehicles. Nevertheless, the tradeoff between the cooperative vehicular ratio and transmission capacity of CVN is a challenge. In this paper, an analytical framework is developed to study the impacts of cooperation, interference and channel fading on the system capacity. Moreover, based on the proposed analytical framework, a closed-form expression is derived to reveal the relationship between transmission capacity and other network parameters, such as outage probability, vehicular density and adjacent infrastructures distance. Numerical results show that an optimal cooperative vehicular ratio leads to the maximum capacity. The results can also provide a preferable guidance for the design of CVNs.

A Heterogeneous Network Modeling Method Based on Public Goods Game Theory to Explore Cooperative Behavior in VANETs.

Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only extracts part attributes of vehicles and does not distinguish the differences between strategy and attribute. In this paper, we focus on the heterogeneous network model based on the public goods game theory for VANET. Then we propose a Dynamic Altruism Public Goods Game (DAPGG) model consisting of rational nodes, altruistic nodes, and zealots to more realistically characterize the real VANET. Rational nodes only care about their own benefits, altruistic nodes comprehensively consider the payoffs in the neighborhood, while zealots insist on behaving cooperatively. Finally, we explore the impacts of these attributes on the evolution of cooperation under different network conditions. The simulation results show that only adding altruistic nodes can effectively improve the proportion of cooperators, but it may cause conflicts between individual benefits and neighborhood benefits. Altruistic nodes together with zealots can better improve the proportion of cooperators, even if the network conditions are not suitable for the spread of cooperative behavior.

Performance Analysis of Bidirectional AF Based Cooperative Vehicular Networks

This paper studies the precise performance of bidirectional amplify and forward (AF) based cooperative vehicular communication networks operating over cascaded Nakagami-m fading environments with arbitrary positive real numbers m, which have been proposed to be a realistically good description of the physical layer channel model of vehicular networks. Exact expressions are derived for four significant performances, namely moment generating function (MGF), higher order moments of signal to noise ratio (SNR), ergodic capacity and average symbol error probability (SEP), whose closed solutions have not been publicly reported. In order to obtain these accurate expressions, we turn our eyes to the MGF of inverse SNR instead of the usual probability density function (PDF) and cumulative distribution function (CDF), which are unknown and difficult to acquire, as well as fail in deriving closed form solutions. Simulation results corroborate the correctness and tightness of the derived expressions, sharp contrast to the approximations and asymptotic values at a sufficiently high SNR region in previous works.

Performance Analysis of Cooperative V2V and V2I Communications Under Correlated Fading

Cooperative vehicular networks will play a vital role in the coming years to implement various intelligent transportation related applications. Both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications will be needed to reliably disseminate information in a vehicular network. In this regard, a roadside unit (RSU) equipped with multiple antennas can improve the network capacity. While the traditional approaches assume antennas to experience independent fading, we consider a more practical uplink scenario where antennas at the RSU experience correlated fading. In particular, we evaluate the packet error probability for two renowned antenna correlation models, i.e., constant correlation (CC) and exponential correlation (EC). We also consider intermediate cooperative vehicles for reliable communication between the source vehicle and the RSU. Here, we derive closed-form expressions for packet error probability, which help to quantify the performance variations due to fading parameter, correlation coefficients, and the number of intermediate helper vehicles. To evaluate the optimal transmit power in this network scenario, we formulate a Stackelberg game, wherein, the source vehicle is treated as a buyer and the helper vehicles are the sellers. The optimal solutions for the asking price and the transmit power are devised which maximize the utility functions of helper vehicles and the source vehicle, respectively. We verify our mathematical derivations by extensive simulations in MATLAB.