Relay Vehicles Research Articles

A general task offloading and resources allocation strategy for multi-RSUs with load unbalance and priority awareness

Vehicular Edge Computing is a new computing paradigm that enables real-time response to vehicular applications and servers by performing data processing on edge computing devices near the vehicle. However, on the one hand, the random distribution and the mobility of vehicles may lead to load unbalance among different Roadside Units (RSUs), and some tasks may not be able to get timely response due to inadequate computing resources and communication resources in the high-load RSU areas. On the other hand, considering the different urgency of the tasks, the service quality of the system will be seriously affected if these tasks are not treated indistinguishably. To address the above challenges, this paper constructs a priority-aware task offloading and computing&communication resources allocation problem in a general scenario of unbalanced load among multi-RSUs, aiming at minimizing the average delay. In the problem, considering the absence of communication resources, the relay vehicle is used to offload the subtasks of splittable tasks to the RSUs that are in the neighbouring and low-load. Moreover, to take full advantage of computing resources, the task can be reasonably split into at most four parts and processed in parallel on a relay vehicle, a current RSU, a neighbouring RSU and a local vehicle. To solve the problem, a Split-Hop Offloading and Resources Allocation Strategy (SHORAS) based on an improved particle swarm optimisation algorithm is proposed, which uses a penalty function to incline resources towards high priority tasks. Simulation results show that SHORAS improves 24% in terms of the total system delay and effectively reduces the processing delay in the high-load areas compared to other strategies, while ensuring the delay requirements of high priority tasks.

UAV aided NOMA relaying with energy harvesting architecture: Performance analysis

In this work, we consider a dual hop Non Orthogonal Miltiple Access (NOMA) based communication network between Source S and two destination users UA , UB which is aided by an Unmanned Aerial Vehicle (UAV) relay R. The energy constrained UAV relay harvests energy from signal transmitted by the source through Power Splitting (PS) and further transmits the signal using Amplify and Forward (AF) protocol to the destination users through NOMA. For the proposed network, we derive analytical expressions for outage probability and ergodic capacity. The impact of parameters like PS ratio and UAV altitude is studied on the overall system performance. Further, the performance of the proposed system is also compared with Orthogonal Multiple Access (OMA) scheme. The results obtained show that the energy harvesting capacity of UAV is significantly affected under Linear and Non Linear EH models. The numerical analysis done is verified through Monte Carlo Simulations.

Kinerja Sistem Hybrid Radio Frequency Energy Harvesting Pada Sistem Komunikasi V2V

This article investigates the impact of hybrid Radio Frequency Energy Harvesting (RF-EH) techniques on cooperative Vehicle-to-Vehicle (V2V) communications systems. The energy harvesting (EH) process is carried out in the vehicle or vehicle transmitter as a source using the Time Switching (TS) protocol and in the vehicle or relay vehicle using the Power Splitting (PS) protocol. The transmitter or source vehicle obtains the RF signal for the EH process from the base station (BS), while the relay vehicle obtains the RF signal from the transmitter or source vehicle. System performance is presented with Outage Probability (OP) values ​​for different EH parameters, namely, time switching factor, α and power splitting factor â. From the simulation results, all parameters influence the OP value. The higher the value of the RF-EH parameters (α and â) results in a lower OP value. For noise power, N_0 = -100 dBm, the best OP value for α = 0.7 is 0.1214 and the best OP value for â = 0.8 is 0.1557.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks.

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network's route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

Improved Multiresolution Neural Network for Mobility-Aware Security and Content Caching for Internet of Vehicles

With the emerging communication and computation technologies, the Internet of Vehicles (IoV) has become a new paradigm that enables vehicles to communicate with Road Side Units (RSUs) as well as with other vehicles to collect or exchange information. However, the inherent characteristics of IoV, such as the high mobility of the vehicle and the limited storage capacity of the edge nodes, cause numerous difficulties in developing a caching scheme. Therefore, in this paper, a novel improved Multinomial Recurrent Neural Network (MRNN) classifier and 2CK-ECC algorithm are proposed to predict the vehicular mobility, security, and content caching for the IoV. Initially, the vehicles are checked for registration. Afterwards, vehicle login and vehicle authentication take place. Then, mobility prediction is carried out for the authenticated vehicles using the MRNN Classifier. After that, vehicle clustering is done via the novel Variance Balanced Iterative Reducing and Clustering Using Hierarchies (VBIRCH) technique. Next, the Relay Vehicles (RVs) are selected using the novel Harris Self Avoiding Hawks Optimization (HSAHO). Further, the data contents are divided into chunks and saved in the cache memory. From the cache memory, data is transferred to the RSU. Finally, a novel Caesar Combined Key-based Elliptic Curve Cryptography (2CK-ECC) algorithm is investigated for secure data transmission. The experimental outcomes demonstrate that the proposed technique outperforms the existing base line approaches.

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.

RSMA for Hybrid RIS-UAV-Aided Full-Duplex Communications With Finite Blocklength Codes Under Imperfect SIC

In this work, we consider a hybrid aerial full-duplex (FD) relaying consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of rate splitting multiple access (RSMA) in such networks and focus on joint optimization of RSMA parameters, 3D-coordinates of the UAV/RIS, and phase shift matrix at the RIS along with analyzing the outage probability, block error rate (BLER) and achievable weighted sum rate for finite blocklength (FBL) and infinite blocklength (IBL) codes under imperfect successive interference cancellation (SIC) at each user and residual-self interference (RSI) at the UAV. We first formulate the weighted sum rate maximization problem and adopt the block coordinate descent (BCD) method to deal with the non-convex nature of the problem. Thereafter, we propose a BCD-based algorithm that jointly optimizes these parameters using a heuristic approach for optimum power allocation, a Riemannian conjugate gradient-based algorithm to get the optimal phase shift at the RIS, and an iterative algorithm to obtain the optimal UAV/RIS position. It also distributes the common rate among the users optimally. Next, with obtained optimal parameters, we further analyze the performance of the network and derive the closed-form expressions of BLER, outage probability, and average weighted sum rate. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions, and demonstrate the superiority of RSMA over non-orthogonal multiple access (NOMA) and conventional orthogonal multiple access (OMA) schemes.

ABM-V: An Adaptive Backoff Mechanism for Mitigating Broadcast Storm in VANETs

In vehicular ad hoc networks (VANETs), the broadcast storm problem may disable road safety applications and cause congestion or traffic accidents. Existing schemes propose broadcast storm mitigation by reducing the number of relay vehicles. Concurrently, in improving reachability, these schemes stipulate that vehicles expected to transmit packets to more next-hop neighbors have higher relay priority. However, the potential redundancy caused by duplicate reception by neighbors is ignored, which exacerbates the contention and collisions in transmission. This paper proposes an adaptive backoff mechanism for mitigating broadcast storm in VANETs (ABM-V). Specifically, the receiver estimates the expected benefit and redundancy by combining the distribution of neighbors. Then, the receiver adaptively adjusts the backoff time by utilizing Dempster-Shafer evidence theory. Finally, the receiver with the shortest backoff time becomes the relay and rebroadcasts the packet to its neighbors. Simulation results illustrate that ABM-V significantly reduces the rebroadcast ratio and redundancy ratio compared with the existing typical schemes while maintaining stable reachability.

Bio-inspired Probabilistic Opportunistic Routing for Vehicular Delay-Tolerant Networks Using A Hybrid Swarm Approach

Vehicular Delay Tolerant Network (VDTN) routing has been improved using probabilistic routing to improve the coverage of intermittent vehicular networks. VDTNs are characterized by large transmission ranges of vehicles, rapid vehicular speeds, and restricted mobility movements, particularly in urban areas. As a result, the probabilistic Sore-Carry-and-Forward (SCF) relay vehicle selection can be inaccurate when considering remote vehicles, leading to high overheads. To address this issue, a new bio-inspired VDTN routing protocol is proposed to better estimate the SCF capabilities of remote vehicles so that the duplicated generated bundles’ copies are reduced without altering the bundles’ delivery ratio and average delivery delay. This solution sequentially employs the Ant Colony Optimizer (ACO) and Glowworm Swarm Optimization (GSO) to adaptively control the replication of bundle copies at each bundle forwarding stage based on predefined probabilistic forwarding parameters. Simulation results from a sparse urban mobility scenario show reduced bundle replication rates compared to several probabilistic VDTN routing protocols.

Group contention-based full-duplex unmanned aerial vehicle relay system

Unmanned aerial vehicle (UAV) relaying network systems have been widely studied to expand network coverage and improve frequency efficiency for wireless communication. This paper proposes a carrier sense multiple access with collision avoidance (CSMA/CA) based group contention (GC) full-duplex (FD) UAV relay system. The proposed FD UAV relay system guarantees the establishment of an FD pair. The UAV acts as a relay node and notifies the next-hop destination to all nodes with the acknowledge (ACK) packet. Only nodes that can establish the FD pair with the next-hop destination compete for channel access. No matter which node wins the channel contention, throughput and delay are improved because it can guarantee the FD pair establishment. Both analytical and simulation results verified that the proposed FD UAV relay system could achieve better throughput and delay than a conventional UAV relay system.

LOS Coverage Area in Vehicular Networks with Cox-Distributed Roadside Units and Relays

We develop an analytical framework to examine the line-of-sight (LOS) coverage area in vehicular networks with roadside units (RSU) and vehicle relays. In practical deployment scenarios, RSUs and vehicle relays are spatially correlated and we characterize this by employing Cox point processes to model the locations of RSUs and vehicle relays simultaneously. Leveraging the random blockage model, we model the LOS coverage area as Boolean models on these Cox point processes. The LOS coverage area is then evaluated by its area fraction. We show that relays can increase the area fraction of LOS coverage by nearly 50% even when RSUs and relays are spatially correlated. By presenting a stochastic geometry model for a vehicular network with RSUs and relays and then by providing a tool to capture its LOS coverage, our work assesses the viability of vehicle relays for modern vehicular networks exploiting LOS coverage.

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

SummaryFor the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

Deep Reinforcement Learning Based Joint Beam Allocation and Relay Selection in mmWave Vehicular Networks

Millimeter-wave (mmWave) can provide abundant spectrum resource in vehicular communication networks. Nevertheless, due to the high path-loss and blocking effects in mmWave propagation, and high mobility of vehicles, downlink services for vehicles would be seriously degraded. In this paper, we firstly propose a deep reinforcement learning-based joint beam allocation and relay selection (JoBARS) scheme to mitigate blocking effects and optimize the total transmission rate of the vehicular network, where the mmWave base station (mmBS) provides multi-user services. When downlinks are blocked, the mmBS can select appropriate idle vehicles as relay nodes to enhance service quality from a global perspective. We set the rate punishment restriction in JoBARS scheme to guarantee each vehicle can obtain high-quality service. Besides, a relaying incentive mechanism (RIM) is proposed to avoid vehicles being overly selected for relaying and ensure that relay vehicles have a higher chance of being served in the next round. We demonstrate that JoBARS scheme can effectively enhance the total transmission rate while alleviating transmission outages caused by severe propagation attenuation of mmWave signals. Compared with Greedy Selection scheme, the total rate and average connection probability of vehicles under JoBARS scheme are nearly 17% and 14% higher when blocking effects are severe.

Outage Probability Minimization in Secure NOMA Cognitive Radio Systems With UAV Relay: A Machine Learning Approach

This paper considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) cognitive radio (CR) system with an unmanned aerial vehicle relay (UR). In this system, a secondary transmitter (ST) uses licensed spectrum from the primary network to transmit signals to its secondary receivers (SRs) based on NOMA. The UR is used as a relay to forward the signals from the ST to the SRs. As a result, the system can achieve significant improvements in spectral efficiency and network capacity. However, such a MIMO NOMA CR system faces issues of interference and security, i.e., eavesdropping attacks, due to the shared spectrum use and the UR. Therefore, we aim to minimize the outage probability of the secondary network, subject to constraints on the outage probability of the primary network and the intercept probabilities of eavesdroppers. Then, we attempt to optimize the transmit power of the UR, the coordinates of the UR, and the power allocation factors for NOMA. We further derive closed-form expressions for the outage probabilities of the secondary and primary networks and the intercept probabilities at the eavesdroppers. We propose using a machine learning algorithm based on a constrained continuous genetic algorithm to solve the optimization problem.

Secrecy Performance for RIS-Based Integrated Satellite Vehicle Networks With a UAV Relay and MRC Eavesdropping

This work investigates the secrecy issues for the secrecy reconfigurable intelligent surface-based integrated satellite unmanned aerial vehicle (UAV) relay networks with multiple vehicle eavesdroppers. Particularly, the maximal ratio combining (MRC) eavesdropping is utilized to overhear the legitimate signal information. The UAV is applied to forward the legitimate signal to the destination user. Reconfigurable intelligent surface is applied to ensure the transmission. Relied on the proposed secrecy system model and MRC eavesdropping scheme, this paper gets the detailed investigations for the secrecy outage probability (SOP) through the whole signal-to-noise ratios (SNRs) to valuate the effects of important parameters on the SOP. Moreover, to achieve the further impacts of main parameters on the SOP at high SNRs, the approximate analysis for the SOP is also gotten together with the secrecy diversity order (SDO) and secrecy coding gain (SCG). At last, several typical Monte Carlo (MC) results are obtained to present the rightness of obtained theoretical analysis.

NOMA-enabled Cooperative V2V Communications with Fixed-Gain AF Relaying

By virtue of its improving bandwidth efficiency along with user fairness, non-orthogonal multiple access (NOMA) technique is considered a promising method for next-generation wireless communication systems. Since fading effect of wireless channels in vehicle-to-vehicle (V2V) communication systems are more severe than those in traditional systems, in this study, we employ the power-domain downlink NOMA technique in cooperative V2V communication systems to enhance data transmission capacity and network efficiency. In the proposed system, the base station communicates with two vehicular nodes, namely near and far users, through a relay vehicle employing the fixed-gain amplify-and-forward scheme. In real-life scenarios, the relay and the users move in high velocities; hence, the corresponding fading channels between these nodes are exposed as having double-Rayleigh fading characteristic in which the fading coefficient of a wireless channel is modeled as the product of two Rayleigh distributed random variables. To analyze the system performance, we first investigate the outage probability and derive its exact closed-form expressions for the near and far users. Then, we make the exact ergodic capacity analysis and obtain the closed-form solution for the near user. Furthermore, outage and ergodic performances of the NOMA-enabled system are compared to the simulation results of the traditional orthogonal multiple access approach. We also give analytic and numerical results to evaluate the performance of the proposed system and show the consistency of Monte-Carlo simulations with analytical derivations. It is observed that even with the small power allocation, both performances of the near user mostly outperform the far user.

Cluster-based combined hybrid relay vehicle selection approach for improving performance and reliability in vehicular ad hoc networks

Cluster-based combined hybrid relay vehicle selection approach for improving performance and reliability in vehicular ad hoc networks

Secrecy Outage Probability for Two-Way Integrated Satellite Unmanned Aerial Vehicle Relay Networks with Hardware Impairments

In this paper, we investigate the secrecy outage performance for the two-way integrated satellite unmanned aerial vehicle relay networks with hardware impairments. Particularly, the closed-form expression for the secrecy outage probability is obtained. Moreover, to get more information on the secrecy outage probability in a high signal-to-noise regime, the asymptotic analysis along with the secrecy diversity order and secrecy coding gain for the secrecy outage probability are also further obtained, which presents a fast method to evaluate the impact of system parameters and hardware impairments on the considered network. Finally, Monte Carlo simulation results are provided to show the efficiency of the theoretical analysis.