Services In Vehicular Networks Research Articles

Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing

Mobile edge computing has been a promising solution to enable real-time service in vehicular networks. However, due to high dynamics of mobile environment and heterogeneous features of vehicular services, traditional expert-based or learning-based strategies has to update handcrafted parameters or retrain learning model, which leads to intolerant overhead. Therefore, this paper investigates the problem of multi-task offloading (MTO), where there exist multiple offloading scenarios with varying parameters, such as task topology, resource requirement and transmission/computation capability. The objective is to design a unified solution to minimize task execution time under different MTO scenarios. Accordingly, we develop a Seq2seq-based Meta Reinforcement Learning algorithm for MTO (SMRL-MTO). Specifically, a bidirectional gated recurrent units integrated with attention mechanism is designed to determine offloading action by encoding sequential offloading actions and showing different preferences to different parts of input sequence. Particularly, a meta reinforcement learning framework is designed based on model-agnostic meta learning, which trains a meta policy offline and fast adapts to new MTO scenario within a few training steps. Finally, we conduct performance evaluation based on task generator DAGGEN and realistic vehicular traces, which shows that the SMRL-MTO reduces task execution time by 11.36% on average compared with greedy algorithm.

AC-SDVN: An Access Control Protocol for Video Multicast in Software Defined Vehicular Networks

The way to use limited bandwidth resources to achieve high-quality video services in vehicular networks is an important research topic. A large number of studies have shown that the fast-growing field of software defined networking (SDN) can provide solutions to the problems encountered by traditional IP networks when implementing video multicasting. However, there is no research addressing the security issues for this scenario. In this paper, we explore the application scenarios of video multicast in software defined vehicular networks (SDVN), and propose a secure and effective access control protocol to solve multicast security issues. This protocol realizes the authentication of multicast video requesting vehicles and RSUs. According to the authentication results, the SDN controller constructs multicast paths that only reach legitimate RSUs and vehicles, and only the vehicle passing the authentication can obtain video decryption keys. The protocol resists common attacks and satisfies the security requirements in vehicular networks. In addition, the scheme supports batch verification, which reduces the time cost of authentication, and adopts broadcast encryption technology to effectively reduce the communication load. Compared with related schemes, our protocol performs better in terms of computation and communication cost, packet loss rate, and time delay.

Intelligent Surface Empowered Sensing and Communication: A Novel Mutual Assistance Design

Integrated sensing and communication (ISAC) is a promising paradigm to provide both sensing and communication (S&C) services in vehicular networks. However, the power of echo signals reflected from vehicles may be too weak to be used for future precise positioning, due to the practically small radar cross section of vehicles with random reflection/scattering coefficient. To tackle this issue, we propose a novel mutual assistance scheme for intelligent surface-mounted vehicles, where S&C are innovatively designed to assist each other for achieving an efficient win-win integration, i.e., sensing-assisted phase shift design and communication-assisted high-precision sensing. Specifically, we first derive closed-form expressions of the expected echo power and achievable rate under uncertain angle information. Then, the communication rate is maximized while satisfying sensing requirements, which is proved to be a monotonic optimization problem on time allocation. Furthermore, we unveil the feasible condition of the problem and propose a polyblock-based optimal algorithm. Simulation results validate that the performance trade-off bound of S&C is significantly enlarged by the novel design exploiting mutual assistance in intelligent surface-aided vehicular networks.

Secure Edge Computing-Assisted Video Reporting Service in 5G-Enabled Vehicular Networks

Since traffic accidents occur frequently, a real-time video traffic reporting service is necessary in vehicular networks for a prompt response to accidents. Although the fifth-generation (5G) network provides a solution for real-time services in vehicular networks, the security and privacy of the services needs to be addressed first. The existing secure video reporting service schemes in 5G-enabled vehicular networks have significant computing, communication, and storage overheads, because of public key certificates, expensive bilinear pairing operations, and repeated video reporting to the cloud. To address these issues, we propose a secure edge computing-assisted video reporting service in 5G-enabled vehicular networks. In the proposed method, edge nodes complete the message verification and classification. Moreover, these nodes send the first received report message of the same accident to the designated official vehicles to realizes a local upload and download of video reports and to minimize the storage of repeated accident reports in the cloud. Security analysis indicates the proposed scheme is secure under the random oracle model and meets a series of vehicular networks requirements. In addition, performance evaluations show that the scheme achieves lower authentication overhead than existing signature schemes, and has lower total delay than other relevant video reporting service schemes.

Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks

Location-based services (LBSs) are widely used in vehicular networks. Privacy leakage from LBS is a key issue to be solved. However, the existing schemes fail to provide differentiated protection for users’ different locations, which may lead to the leakage of location information. In this paper, we propose a personalized location privacy protection scheme based on differential privacy to protect the privacy of location-based services in vehicular networks. Firstly, we propose a normalized decision matrix to describe the efficiency and the privacy effect of navigation recommendations. We then establish a utility model integrated with users’ privacy preferences to compute the effective driving route. Secondly, for different service request locations in the driving route, we define sensitivity distance as an index to quantify their privacy requirements. The privacy budget will be added to the service request location to generate a false location. Moreover, due to the limitation of road range in the driving route, if the privacy budget value allocated is small enough, the false location generated by the Plane Laplace will be deviated. As a result, the attacker can deduce users’ real request locations. Consequently, considering the factors of trajectory leakage, attack strategy and QoS, we establish a multi-objective optimization model to optimize the false location. Based on the real data set, we conduct a series of comparison simulations to evaluate the performance of the proposed scheme. The experimental results demonstrate that our scheme can satisfy users’ personalized services needs and provide an optimal solution to privacy and QoS.

UHF-RFID-Based Real-Time Vehicle Localization in GPS-Less Environments

The vehicle localization, which aims to identify a vehicle and then position the vehicle with a high precision, can be used to facilitate various applications and services in vehicular networks. Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet the accuracy requirements especially in certain specific scenarios, such as tunnels. At the same time, Ultrahigh frequency (UHF) radio frequency identification (RFID) has become an efficient booster for internet of things (IoT) due to the desirable advantages, such as low cost, battery-free, and unique identification. In this paper, based on the UHF-RFID, we propose a novel real-time vehicle localization scheme in GPS-Less Environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we adopt single antenna multi-frequency ranging scheme, in which the integer ambiguity problem is solved by the maximum-likelihood estimation (MLE)-based robust Chinese remainder theorem (CRT). With the reconstructed distances between the tags and the reader, the coordinates of the vehicle then can be calculated with the Levenberg-Marquardt (LM) algorithm. Furthermore, the computational complexities of the algorithms and the time consumption of the proposed scheme are analyzed. The experimental results demonstrate that the proposed scheme can track vehicle’s location with error lower than 27 cm at the probability of 90%.

UAV‐assisted data dissemination based on network coding in vehicular networks

Efficient and emergency data dissemination service in vehicular networks (VN) is very important in some situations, such as earthquakes, maritime rescue, and serious traffic accidents. Data loss frequently occurs in the data transition due to the unreliability of the wireless channel and there are no enough available UAVs providing data dissemination service for the large disaster areas. UAV with an adjustable active antenna can be used in light of the situation. However, data dissemination assisted by UAV with the adjustable active antenna needs corresponding effective data dissemination framework. A UAV-assisted data dissemination method based on network coding is proposed. First, the graph theory to model the state of the data loss of the vehicles is used; the data dissemination problem is transformed as the maximum clique problem of the graph. With the coverage of the directional antenna being limited, a parallel method to find the maximum clique based on the region division is proposed. Lastly, the method's effectiveness is demonstrated by the simulation; the results show that the solution proposed can accelerate the solving process of finding the maximum clique and reduce the number of UAV broadcasts. This manuscript designs a novel scheme for the UAV-assisted data dissemination in vehicular networks based on network coding. The graph theory is used to model the state of the data loss of the vehicles. With the coverage of the directional antenna being limited, then a parallel method is proposed to find the maximum clique of the graph based on the region division. The effectiveness of the method is demonstrated by the simulation.

Vehicle Pairing, Spectrum Assignment and Power Control for Non-Safety Critical Services in Heterogeneous Vehicular Networks

To accommodate the ever-increasing demands for non-safety critical services in vehicular networks, heterogeneous vehicular networks (HVNs) have been proposed and drawn considerable attention. It is known that the performance of HVNs suffers from a shortage of spectrum resource, and the relatively low utilization of licensed spectrum makes the spectrum shortage even worse. To cope with this, by introducing unlicensed spectrum into HVNs, we investigate the spectrum efficiency (SE)-oriented collaborative transmission. The SE for HVNs depends highly on the channel state information (CSI) of the effective communication links which are normally determined by the vehicle pairing. So, we first propose a joint social-physical domain vehicle pairing algorithm (JSPD-VPA), and then formulate an optimization problem to maximize the SE of vehicle-to-everything (V2X) communications while guaranteeing the quality of services (QoS) requirements of cellular users in licensed spectrum and users in unlicensed spectrum. Considering that the formulated problem is NP-Hard problem, to solve it, the formulated problem is decomposed into two subproblems, i.e., spectrum assignment subproblem and power control subproblem. An optimal hypergraph coloring algorithm is proposed to solve the spectrum assignment subproblem, and a low-complexity three-dimensional geometry approach is developed to solve the power control subproblem. Simulation results reveal that the proposed vehicle pairing, spectrum assignment and power control algorithm (VP-SA-PCA) can greatly enhance the SE and outperforms the baselines around 7%-37% within a certain range. Moreover, the superiority of the proposed JSPD-VPA has been validated.

Pr-CAI: Priority based-Context Aware Information scheduling for SDN-based vehicular network

The Intelligent Transport System (ITS) services recon upon reliable information dissemination among the heterogeneous vehicular network components along the roads. The information sharing among vehicles, pedestrian units and RoadSide Units (RSUs) is paramount in dynamic vehicular network to provide real-time data services in vehicular networks. Due to distinctive vehicular network characteristics, it is extremely challenging to provide efficient data services in heterogeneous traffic scenario. Firstly, the ITS services have different Quality of Service (QoS) constraints which varies in line with application specification. Secondly, there is a need of coordination among network resources in vehicular network. In this work, a Software Defined Network (SDN) based vehicular network model has been presented to provide reliable data services in heterogeneous traffic in urban environment. The Context Aware Information Scheduling (CAIS) problem has been formulated by analysing the characteristics and QoS requirements of ITS services. A Priority based Context Aware Information (Pr-CAI) scheduling algorithm has been proposed to ensure real time data service scheduling, which assigns priority to requests based on service deadline, request selection priority and traffic dynamics. The Pr-CAI has been simulated for different heterogeneous traffic scenarios to analyse its performance. The comprehensive simulation results prove the ascendancy of proposed algorithm with respect to the existing compared algorithms. The Pr-CAI scheduling algorithm ensures real time data service scheduling for different ITS services that maximizes the service ratio and provides fairness amongst users.

A novel framework for message dissemination with consideration of destination prediction in VFC

The rapid development of intelligent transportation systems (ITS) and the emergency of ever-growing vehicular applications pose significant challenges to the underlying communication system. Without the support of powerful communication, many vehicular services will just stay in conceptual phase and cannot be put into practice. Recently, vehicular fog computing (VFC) is introduced as a promising solution to provide low-latency services on roads, which utilizes enormous vehicles on roads as communication and computation resources and extends cloud computing service to an edge network. On the other hand, the publish/subscribe (pub/sub) paradigm provides a loosely coupled and scalable communication which can facilitate flexible and dynamic vehicular network services. Motivated by the merits of these two research fields, in this paper, we propose a novel joint design of pub/sub-communication model based on VFC architecture, which employs fog nodes as the data platform for messages aggregation. Specifically, we describe a method to predict the vehicle’s destination and construct a stable VFC on roads. Then, a message dissemination approach is designed based on our communication model. Finally, the experimental results confirm the efficiency of our proposed scheme in real-world urban scenarios.

A Novel Collision Supervision and Avoidance Algorithm for Scalable MAC of Vehicular Networks

In order to meet low-latency and ultrareliable requirements on safety services in vehicular networks, this paper proposes a novel Collision supervision and avoidance (CSA) algorithm for the contention based scalable media access control protocol. The twodimensional Markov chain model of adaptive backoff state transition criterion in CSA has been built, which could efficiently match the backoff states of nodes to the dynamic changes of vehicular networks. The scalable transmissions can be achieved through supervised trend and matching backoff mechanisms with three adaptive backoff modes. The packet transmit probabilities for the backoff modes have been derived with the theoretical result of the enhanced throughput. The simulation results show the remarkable scalability performance such as normalized throughput > 0.92, PDR > 86% and delay < 6.5ms even in the high-density and high-mobility environment.

Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks

The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

Reservation Service: Trusted Relay Selection for Edge Computing Services in Vehicular Networks

Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction. However, due to the various behaviors of vehicles, not all the vehicles are trustworthy and willing to join in the service request relay process. Therefore, how to exploit the cooperation between ECDs and vehicles to relay the service request by considering the dynamic traffic status and the behaviors of vehicles becomes a challenge. As an effort to address this problem, we propose a trusted relay selection scheme for edge services to facilitate the proposed reservation service framework. Specifically, we first design the request relay mechanism based on the dynamic traffic status to guarantee the efficiency of the relay process. Then, the reputation management mechanism is presented to constrain the behaviors of vehicles, where a vehicle with high reputation value can enjoy the price discount for computing service. Based on the designed request relay and reputation management mechanisms, a reputation-based auction approach is then proposed to select relay vehicles (RVs) to reduce the cost of the relay service. Simulation results show that the proposed reservation service framework can manage vehicles efficiently and lead to the lowest cost for the relay services compared with the conventional schemes.

Path Selection for Seamless Service Migration in Vehicular Edge Computing

Mobile-edge computing provisions computing and storage resources by deploying edge servers (ESs) at the edge of the network to support ultralow delay and high bandwidth services. To ensure QoS of latency-sensitive services in vehicular networks, service migration is required to migrate data of the ongoing services to the closest ES seamlessly when users move across different ESs. To achieve seamless service migration, path selection is proposed to obtain one or more paths (consisting of several switches and ESs) to transfer service data. We focus on the following problems about path selection: 1) where to implement path selection? 2) how to coordinate interests of mobile users (i.e., vehicles) and network providers since they have conflicting interests during path selection? and 3) how to ensure seamless service migration during the migration of vehicles? To address the above problems, this article investigates path selection for seamless service migration. We propose a path-selection algorithm to jointly optimize both interests of the network plane (i.e., the cost for network providers) and service plane (i.e., QoE of users). We first formulate it as a multiobjective optimization problem and further prove theoretically that the proposed algorithm can give a weakly Pareto-optimal solution . Moreover, to improve the scalability of the proposed algorithm, a distance-based filter strategy is designed to eliminate undesired switches in advance. We conduct experiments on two synthesized data sets and the results validate the effectiveness of the proposed algorithm.

Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks

With the development of vehicular network, location-based services (LBSs) provide increasing diversified services for drivers and passengers. When users enjoy the services, users' location needs to be constantly updated to service providers, which causes the location information to be speculated and attacked by attackers. However, existing schemes don't provide differentiated protection for users' different locations, which may lead to the leakage of location information. Therefore, we propose a location privacy protection method to satisfy users' personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users' personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location. Experimental results demonstrate that compared to existing methods, our scheme can meet the user's service requirements and achieve better service quality under the conditions of reasonable protection of their privacy.

AdPS: Adaptive Priority Scheduling for Data Services in Heterogeneous Vehicular Networks

Data service scheduling is paramount in realization of Intelligent Transportation System (ITS). ITS services recon upon real-time information sharing among vehicular network components. To provide real time information services in vehicular networks, numerous challenges are imposed due to unique characteristics of such networks. For efficient information dissemination, there is a prime requirement of an adaptive scheduling algorithm for approximate vehicular dynamics information. In this work, a Dedicated Short Range Communication (DSRC) based vehicular communication model has been used for efficient data services in heterogeneous traffic environment. A novel Adaptive Priority data Service scheduling (AdPS) algorithm has been proposed to provide real-time data services, which is based on fuzzy logic request deadline estimation and request prioritization. The prioritized requests has been stored in multilevel queues according to the urgency of requests to provide simultaneous access. The proposed algorithm has been simulated for different traffic scenarios for a real city environment and its performance has been analyzed at different traffic instances. The proposed protocol ensures the real time service scheduling which provides fairness among the users.

An anonymous authentication scheme for edge computing-based car–home connectivity services in vehicular networks

With the rapid development of Internet of Things technology, the interconnection between objects has become more and more popular, particularly in the fields of smart home and intelligent transport systems. Car–home connectivity services allow the user to monitor and control home appliances while driving a car on the road. Such services can be deployed on edge servers in vehicular networks for low latency and user privacy. However, privacy issue is a big challenge for this application since user’s sensitive information such as driving route, home location, even connection between car and home are vulnerable to be disclosed and abused. To address this issue, the paper proposes an anonymous authentication scheme based on efficient cryptographic building blocks for privacy-preserving car–home connectivity services. The scheme covers the IEEE standard WAVE stack and the higher layer facilitating anonymous authentication for security services. As a result, it allows the user driving in a car to interact with home appliances without sensitive information being leaked to any edge server or roadside unit. Meanwhile, a misbehaving user can be inspected against anonymity with his/her identity being retrieved by authority. The performance analysis shows that the proposed anonymous authentication scheme outperforms some other related works in vehicular network environment, with respect to communication overhead, computation overhead, signaling cost, serving ratio, and authentication delay.

A $Q$ -Learning-Based Proactive Caching Strategy for Non-Safety Related Services in Vehicular Networks

Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic ${Q}$ -learning solution together with vehicle movement predictions based on a long short-term memory network. The optimal caching strategy which minimizes the latency of caching services can be derived by our heuristic ${\varepsilon }_{{n}}$ -greedy training processes. Numerical results demonstrate that our proposed strategy can achieve better performance compared with several baselines under different prediction accuracies.

New Bargaining Game Model for Collaborative Vehicular Network Services

The wireless industry’s evolution from fourth generation (4G) to fifth generation (5G) will lead to extensive progress in new vehicular network environments, such as crowdsensing, cloud computing, and routing. Vehicular crowdsensing exploits the mobility of vehicles to provide location-based services, whereas vehicular cloud computing is a new hybrid technology that instantly uses vehicular network resources, such as computing, storage, and Internet for decision-making. In this study, novel crowdsensing, cloud computing, and routing algorithms are developed for a next-generation vehicular network, and they are combined into a hybrid scheme. To provide an effective solution toward an appropriate interactive vehicular network process, we focus on the paradigm of learning algorithms and game theory. Based on individual cooperation, our proposed scheme is designed as a triple-plane game model to adapt to the dynamics of a vehicular network system. The primary advantage of our game-based approach is to provide self-adaptability and responsiveness to current network environments. The performance of our hybrid scheme is evaluated and analyzed using simulation experiments in terms of the cloud service success ratio, normalized dissemination throughput, and crowdsensing success probability.

Cooperative Temporal Data Dissemination in SDN-Based Heterogeneous Vehicular Networks

Heterogeneous network resources are expected to cooperate with each other to support temporal data services in vehicular networks. However, it is challenging to implement an efficient data scheduling strategy due to the following factors: first, there are different time constraints on services, which are imposed by the application requirements of both temporal data quality and transmission delay; second, the heterogeneity of wireless interfaces further complicates the transmission task assignment in dynamic vehicular environments. Therefore, this paper proposes an software-defined network-based architecture to enable unified management on heterogeneous network resources. Then, we formulate the cooperative temporal data dissemination (CTDD) problem by considering the property of temporal data, the heterogeneity of wireless interfaces, and the delay constraints on service requests. Further, we prove the NP-hardness of the CTDD by constructing a polynomial-time reduction from a well know NP-hard problem, classical knapsack problem. On this basis, we design a heuristic algorithm called priority-based task assignment (PTA), which synthesizes dynamic task assignment, broadcast efficiency, and service deadline into priority design. Accordingly, PTA is able to adaptively distribute broadcast tasks of each request among multiple interfaces, so as to improve overall system performance. Last but not least, we build the simulation model and implement the proposed algorithm. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.