Multiple Roadside Units Research Articles

Efficient content caching for 5G assisted vehicular networks

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

A novel coalition game based distributed cooperative content caching in mobile edge networks

In mobile edge networks with mobile vehicles, road-side units (RSUs) can be used as edge nodes to cache some popular contents for moving vehicles in advance, and therefore improving the transmission efficiency and quality of user experience during vehicle-to-infrastructure (V2I) content communication. Transferring large files to a fast-moving car is a challenging task because of the limited communication time between the vehicle and the RSU. In this paper, we propose a cooperative content placement model for RSUs, in which multiple RSUs can jointly complete the cache and transmission of large files, so that the vehicle users can obtain the target content in the process of high-speed movement. To form cooperative coalitions between RSUs and place as much content as possible, a novel coalition game based distributed cooperative content caching scheme, namely CGD3C, is proposed, in which an RSU cooperative clustering algorithm and a resource balancing based coalition formation algorithm is proposed to merge and split the coalitions, so as to obtain a stable coalition structure. Finally, a cooperative content caching determination algorithm is proposed to determine the content placement with objective of maximizing the current interests. Our simulation results show that our proposed CGD3C can achieve stable and effective content delivery for high-speed moving vehicles, form an optimal RSU coalition structure independently, as well as improve the profit by 15%, the caching resource utilization by 24%, and the request hit rate by 12% on average in comparison with existing schemes.

Signal-Vehicle Coordination Control Modeling and Roadside Unit Deployment Evaluation under V2I Communication Environment

Signal-vehicle coordinated control holds substantial promise for enhancing urban transportation efficiency. However, its development faces notable challenges: (1) most existing studies have been conducted based on the assumption of perfect communication conditions. This assumption overlooks the significant impact of vehicle-to-infrastructure (V2I) communication quality on control performance, which leads to poor applicability in practice. (2) The evaluation of roadside unit (RSU) deployment for optimizing signal-vehicle control has not been well studied. Hence, the modeling of signal-vehicle coordination control and RSU deployment evaluation under V2I environment are studied in this paper. First, we introduce a communication model that characterizes the imperfections in communication between RSUs and connected vehicles (CVs). Second, we propose a model for signal-vehicle coordination control within this connected environment. This model integrates strategies from both signal control optimization and the speed optimization of CV platoons. Finally, to assess the impact of the RSU deployment parameters on the performance of signal-vehicle coordination control, we introduce a systematic evaluation method. The reduction in vehicle delays is introduced as the evaluation indicator for control performance. Six other indicators—the number of vehicles in the RSU communication domain, connectivity probability between the CV and RSU, number of vehicles whose speeds are successfully optimized, number of speed adjustments, green extension time, and overlap rate of the communication domains of multiple RSUs—are introduced as the observation indicators. The simulation experiments verify the effectiveness of the proposed model in implementing signal-vehicle coordination control under imperfect communication and environments in low-traffic, medium-traffic, and high-traffic scenarios. Furthermore, these experiments show the quantitative impact of RSU deployment parameters (communication distance, command transmission cycle, installation position, and number of RSUs) on control performance.

MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networks.

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules.

Coalition Formation Game Based Information-Energy Collaboration in Vehicle Edge Computing Networks

In vehicle edge computing network (VECN), a key issue for the roadside unit (RSU) is how to handle computationally intensive tasks timely and use energy efficiently. When an RSU cannot finish the task on time, computation offloading will be an effective approach. However, when offloading tasks most RSUs neglect the energy supply and do not consider computation requirement and energy supply jointly. In this paper, we consider a VECN where there are multiple RSUs. When an RSU has a computational task with latency constraint, it will offload the task to the nearby RSUs and/or cloud servers, and purchase electricity from the neighbors and/or power grid if it is also short of power. To conserve energy and reduce cost, not only the collaboration between the VECN and the cloud/grid, but also the collaboration of RSUs within the VECN is investigated. Specifically, we first formulate the information-energy collaboration problem, and study how the strategies of computation offloading and energy sharing interact with each other, where the RSU's individual revenue is considered. Next, since the problem is a mixed nonlinear programming problem that is hard to be solved, we reformulate it into a double-level coalition formation game, based on which an algorithm with low complexity is proposed. Finally, we prove theoretically that through finite iterations, the game-based algorithm can converge to a stable Nash equilibrium which is the solution to the original problem. Simulation results show that our scheme is superior to the existing ones in reducing edge network expenses while improving individual revenue.

IDADET: Iterative Double-Sided Auction-Based Data-Energy Transaction Ecosystem in Internet of Vehicles

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Deep reinforcement learning based adaptive threshold multi-tasks offloading approach in MEC

Offloading tasks and data is an important use case for Mobile Edge Computing (MEC) that benefits a wide variety of mobile applications on different platforms, including autonomous vehicles and smartphones. A moving vehicle may pass through multiple Roadside Units (RSUs) and the MEC servers located within them as it travels. On-board applications generate structured tasks and can offload them to observable servers. However, since the mobile node cannot determine the information of the global server, choosing the best server becomes a key issue. In this paper, we address the problem of the offloading threshold in traditional Optimal Stopping Theory (OST) based decision models that cannot adapt to the environment. We use Deep Q-Network (DQN) to provide the model with the optimal offloading threshold for adaptive environments and propose a DQN-based Adaptive Threshold Multi-task Offloading approach (DQN-ATMOO). This method uses the principle of OST to solve the offloading order decision problem, divides structured tasks into sub-tasks, and executes offloading in sequence, with the goal of maximizing the probability of offloading to the best server and minimizing the total offloading delay. We experimentally compare and evaluate the proposed model using simulated and real datasets. The results show that the proposed model can be effectively implemented on mobile nodes and significantly reduces the total expected processing time.

Temporal Data Scheduling in Internet of Vehicles Using an Improved Decomposition-Based Multi-Objective Evolutionary Algorithm

Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service quality and service ratio are two conflict goals because of the limited bandwidth and the vehicle mobility in Internet of Vehicles. In order to optimize these two conflict objectives simultaneously, we present an improved decomposition based multi-objective evolutionary algorithm for the temporal data scheduling (I-MOEA/D-TDS) in Internet of Vehicles. Based on the MOEA/D framework, we integrate a self-adaptive weight vector adjustment method based on chain segmentation to improve the performances of temporal data scheduling. For verifying the availability of presented algorithm, under the hybrid Vehicle-to-Infrastructure / Vehicle-to-Vehicle (V2I/V2V) communications and multiple Roadside Units (RSUs) scenario, we compare the proposed algorithm with several related algorithms under the effects of data valid periods, service workloads, maximum tolerated delay, and traffic workloads. Experimental results suggest that the presented algorithm can achieve better data service quality and service ratio.

Optimizing Vehicles’ Tracking Accuracies While Considering Information Aging

Connected and autonomous vehicles are expected to revolutionize transportation systems in the near future. Nevertheless, full autonomy still requires further development that will only be achieved through a high level of cooperation and coordination among vehicles. For the safe execution of several autonomous functions, vehicles will need to frequently communicate their current location to the Road Side Units (RSUs). However, due to information aging, it is expected that the delays from the time a message is generated until the location of the vehicle is updated will induce a tracking error. Thus, there is a tradeoff between the frequency of location updates and tracking error. Further, in order to reduce the queue-induced delays present in the system, the number of messages can be reduced by employing an event-triggered communication scheme. In this work, a family of optimization problems is proposed, utilizing the event-triggered algorithm, in order to determine the optimal triggering threshold. Both single and multiple RSU cases are considered in several simulation scenarios, demonstrating that the proposed optimization framework derives the optimal tracking accuracy. In the case of multiple RSUs, results are further optimized by assigning roads (and in turn vehicles) to RSUs if coverage areas of multiple RSUs overlap.

An RSU-crossed dependent task offloading scheme for vehicular edge computing based on deep reinforcement learning

Various interdependent and computationally intensive on-vehicle tasks have posed great pressure on the computing power of vehicles. Vehicular edge computing (VEC) is considered to be a promising paradigm to solve this problem. However, due to the high mobility, vehicles will pass through multiple road-side units (RSUs) during task computing. How to coordinate the offloading decision of RSUs is a challenge. In this study, we propose a dependent task offloading scheme by considering vehicle mobility, service availability, and task priority. Meanwhile, to coordinate the offloading decisions among the RSUs, a Markov decision process (MDP) is carefully designed, in which the action of each RSU is divided into three steps to decide whether, where, and how each task is offloaded separately. Then, an advanced DDPG-based deep reinforcement learning (DRL) algorithm is adopted to solve this problem. Simulation results show that the proposed scheme has better performance in reducing task processing latency and consumption.

Energy-Efficient Cooperative Offloading for Edge Computing-Enabled Vehicular Networks

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.

A Traceable Self-Generated Pseudonym Scheme in VANETs

The generation of the pseudonym in vehicular ad hoc networks (VANETs) usually requires the participation of a trusted center (TA), and it needs multiple interactions with the TA. To address these problems, we propose a novel traceable self-generated pseudonym based on the non-interactive zero-knowledge proof (NIZK) and consortium blockchain in VANETs. In our scheme, a self-generated pseudonyms method is introduced to eliminate excessive communication burden, and a consortium blockchain is composed of multiple roadside units (RSUs) to replace the TA. RSUs in the consortium blockchain verify the effectiveness of OBU’s self-generated pseudonyms through the consensus mechanism. Multiple trace authorities (TRAs) are designed to jointly tracking of the real identity of the OBU, which effectively prevents the abuse of supervision by the only supervisor.

QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.

Improve Radar Sensing Performance of Multiple Roadside Units Cooperation via Space Registration

Roadside units (RSUs) can help vehicles sense the traffic environment, so as to improve traffic safety. Since the sensing capability of single RSU is limited, we propose a multiple RSUs cooperative radar sensing network (RSU-CRSN) with signal-level fusion technique. Spatial registration is an essential prerequisite and foundation for RSU-CRSN with signal-level fusion. In this paper, we present an adjustable beam enabled spatial registration algorithm (AB-SRA) that makes the sensing area of each RSU coincide by adjusting the sensing beam width of RSU. To adjust the width of sensing beam flexibly, a beamwidth adjustable beamforming algorithm (BABA) is proposed in this paper. Simulation results show that the performance of AB-SRA is close to perfect spatial registration.

MEC-Based Cooperative Multimedia Caching Mechanism for the Internet of Vehicles

Multimedia applications are expected to widely deploy over vehicular networks. In order to meet the low-latency and high-speed transmission requirements of multimedia applications, edge caching is introduced to reduce the network traffic and the transmission delay. Due to the limited storage of the edge cache server, an efficient approach for the content management plays a decisive role for the edge cache performance. This paper proposes a vehicle-to-infrastructure-based cooperative caching mechanism for Internet of Vehicles to improve the edge cache utilization. The system model is established with the goal of maximizing the cooperative caching hit rate. To jointly consider the collaborations between macrobase stations (MBS) and multiple roadside units (RSU), we propose a reinforcement learning algorithm to adaptively control the cache management. According to the content popularity and the network status, the proposed algorithm can dynamically adjust cached content across relevant MBSs and RSUs. The simulation results show that the proposed cooperative caching mechanism significantly improve the cache utilization and the quality of services.

An Authentication and Key Agreement Scheme Based on Roadside Unit Cache for VANET

Vehicular Ad Hoc Network (VANET) is a wireless Mobile Ad Hoc Network that is used for communication between vehicles, vehicles and fixed access points, and vehicles and pedestrians. However, because of the use of open wireless channels, VANET is more vulnerable. Therefore, VANET security is critical for safe driving and user privacy protection. Authentication and key agreement are crucial for ensuring security. Numerous authentication schemes have been proposed between vehicles and roadside units (RSUs). Many solutions are authentication and key negotiation between the vehicle and a single RSU. The vehicle passing through a region needs to complete authentication and key agreement with multiple RSUs separately, which brings a great burden to the vehicle. In order to simplify the authentication process of vehicles and multiple RSUs and improve the efficiency of authentication and key agreement, an efficient authentication and key agreement scheme based on RSU cache is proposed when the vehicle moves from one RSU to another RSU region. In the proposed scheme, RSUs are divided into regions, and each region has a RSU cluster head. When the vehicle enters a certain region and authenticates with a RSU successfully, the RSU submits the authentication information to the RSU cluster head. The RSU cluster head shares the authentication information with other RSUs in the region using the shared key. Other RSUs record the authentication information in the cache. When the vehicle communicates with other RSUs, the authentication is not necessary; the session key can be negotiated by simply exchanging information. After using the cache, the calculation and communication cost of the authentication and key negotiation between the vehicle and other RSU can be significantly saved, the calculation cost is reduced by 37%, and the traffic is reduced by 35%. The random oracle model is used to prove the security of the scheme. The results revealed that the authentication overhead of the proposed scheme is considerably lower than those of other schemes. Compared with the related schemes, the computational cost of the proposed scheme is reduced by 34% on average; the communication cost is close to other related schemes. Moreover, the security analysis shows that the proposed scheme provides better security compared to other related schemes.

Energy-Efficient Resource Allocation for 6G Backscatter-Enabled NOMA IoV Networks

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework.

Joint Vehicle Tracking and RSU Selection for V2I Communications With Extended Kalman Filter

We develop joint vehicle tracking and road side unit (RSU) selection algorithms suitable for vehicle-to-infrastructure (V2I) communications. We first design an analytical framework for evaluating vehicle tracking systems based on the extended Kalman filter. A simple, yet effective, metric that quantifies the vehicle tracking performance is derived in terms of the angular derivative of a dominant spatial frequency. Second, an RSU selection algorithm is proposed to select a proper RSU that enhances the vehicle tracking performance. A joint vehicle tracking algorithm is also developed to maximize the tracking performance by considering sounding samples at multiple RSUs while minimizing the amount of sample exchange. The numerical results verify that the proposed vehicle tracking algorithms give better performance than conventional signal-to-noise ratio-based tracking systems.

Intelligent Task Offloading in Fog Computing Based Vehicular Networks

Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system. These vehicles have a large number of applications that require wireless connectivity by using cellular vehicle-to-everything (C-V2X). The infrastructure of C-V2X comprises multiple roadside units (RSUs) that provide direct connectivity with the on-road vehicles. Vehicular traffic applications are mainly categorized into three major groups such as emergency response traffic, traffic management and infotainment traffic. Vehicles have limited processing capabilities and are unable to process all tasks simultaneously. To process these offloaded tasks in a short time, fog servers are placed near the RSUs. However, it is sometimes not possible for the fog computing server to process all offloaded tasks. In this work, a utility function for the RSU to process these offloaded tasks is designed. In addition, a knapsack-based task scheduling algorithm is proposed to optimally process the offloaded tasks. The results show that the proposed scheme helps fog nodes to optimally scrutinize the high-priority offloaded tasks for task execution resulting in more than 98% of emergency tasks beingprocessed by fog computing nodes.

RaptorQ codes aided secure data delivery strategy for vehicular networks

Abstract This paper deals with security issues in the presence of an eavesdropper for a vehicular scenario. The proposed secure data delivery scheme implements Fountain Codes, namely RaptorQ (RQ) codes, at the application layer (AL) to increase communication security against eavesdroppers’ attacks. For RQ coded transmission scheme, the receiver has to collect a sufficient number of coded packets to reconstruct the original source data. Secure delivery can be achieved if the legitimate user obtains enough RQ coded packets before the eavesdropper does. To satisfy this condition, it is proposed to use a road side unit (RSU) cooperation method when the eavesdropper has better channel conditions than the legitimate user to scatter the coded packets from multiple RSUs. The aim is to reduce the probability that the eavesdropper receives the sufficient number of coded packets and recover the source data before the legitimate user. An optimisation framework which jointly selects the RQ code rate at the AL and Modulation and Coding Scheme (MCS) at the physical (PHY) layer to ensure the secure data transmission by allowing the user to decode the file with a certain probability of decoding success while minimising the intercept probability at the eavesdropper is presented. To evaluate the proposed system, a realistic end-to-end system level simulator is developed. Simulation results show that the proposed scheme can provide secure and efficient data transmission over vehicular networks by significantly reducing the intercept probability at the eavesdropper.