Software Defined Vehicular Networks Research Articles

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

Load Balancing Algorithm for Migrating Switches in Software-defined Vehicular Networks

In Software-Defined Networks (SDN), the divergence of the control interface from the data plane provides a unique platform to develop a programmable and flexible network. A single controller, due to heavy load traffic triggered by different intelligent devices can not handle due to it’s restricted capability. To manage this, it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness. The flow of data through the multiple controllers also varies, resulting in an unequal distribution of load between different controllers. One major drawback of the multiple controllers is their constant configuration of the mapping of the switch-controller, quickly allowing unequal distribution of load between controllers. To overcome this drawback, Software-Defined Vehicular Networking (SDVN) has evolved as a configurable and scalable network, that has quickly achieved attraction in wireless communications from research groups, businesses, and industries administration. In this paper, we have proposed a load balancing algorithm based on latency for multiple SDN controllers. It acknowledges the evolving characteristics of real-time latency <i>vs.</i> controller loads. By choosing the required latency and resolving multiple overloads simultaneously, our proposed algorithm solves the load-balancing problems with multiple overloaded controllers in the SDN control plane. In addition to the migration, our algorithm has improved 25% latency as compared to the existing algorithms.

Intelligent Approach for Traffic Orchestration in SDVN Based on CMPR

The vehicle ad hoc network that has emerged in recent years was originally a branch of the mobile ad hoc network. With the drafting and gradual establishment of standards such as IEEE802.11p and IEEE1609, the vehicle ad hoc network has gradually become independent of the mobile ad hoc network. The Internet of Vehicles (Vehicular Ad Hoc Network, VANET) is a vehicle-mounted network that comprises vehicles and roadside basic units. This multi-hop hybrid wireless network is based on a vehicle-mounted self-organizing network. As compared to other wireless networks, such as mobile ad hoc networks, wireless sensor networks, wireless mesh networks, etc., the Internet of Vehicles offers benefits such as a large network scale, limited network topology, and predictability of node movement. The paper elaborates on the Traffic Orchestration (TO) problems in the Software-Defined Vehicular Networks (SDVN). A succinct examination of the Software-defined networks (SDN) is provided along with the growing relevance of TO in SDVN. Considering the technology features of SDN, a modified TO method is proposed, which makes it possible to reduce time complexity in terms of a group of path creation while simultaneously reducing the time needed for path reconfiguration. A criterion for path choosing is proposed and justified, which makes it possible to optimize the load of transport network channels. Summing up, this paper justifies using multipath routing for TO.

MobiPlace: Mobility-Aware Controller Placement in Software-Defined Vehicular Networks

In this paper, we propose a mobility-aware scheme, named MobiPlace, to address the controller placement problem (CPP) at the Road Side Units (RSUs) in Software-Defined Vehicular Networks (SDVNs). MobiPlace places local controllers at the selected RSUs to reduce the operational delay experienced in traditional SDVN architecture, where controllers are placed at the cloud. Additionally, we consider the effect of dynamic road traffic and propose dynamic adjustment of the placement of the controller with minimal changes. In contrast to the existing literature, we infuse traffic monitoring and traffic prediction strategies to ensure accurate delivery of control messages and data packets. We formulate an Integer Linear Program (ILP) and propose a solution approach consisting of three modules — mobility management , controller placement , and controller selection . The mobility management module uses Markov predictor to predict vehicle movement and reduce controller synchronization overhead when a vehicle moves to a different controller's coverage area. The controller placement module applies a simulated annealing-based algorithm to select potential RSUs that serve as local controllers. The controller selection module determines the preferable controller location for processing the service requests. Simulation results depict that MobiPlace reduces the average flow setup delay by $13.85 \%$ compared to the existing state-of-the-art.

TBDDoSA-MD: Trust-Based DDoS Misbehave Detection Approach in Software-defined Vehicular Network (SDVN)

Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in that network will no longer meet the service requests from other vehicles. Therefore, in this paper, we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles. Trust values are calculated based on direct trust and recommendations (indirect trust). These trust values help to decide whether a vehicle is legitimate or malicious. We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages. The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator, which uses OMNeT++ and Simulation of Urban Mobility (SUMO). We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework (TBF) scheme using the following performance parameters such as detection accuracy, packet delivery ratio, detection time, and energy consumption. Simulation results show that the proposed work has a high detection accuracy of more than 90% while keeping the detection time as low as 30 s.

Software-defined vehicular network (SDVN): A survey on architecture and routing

Abstract With the advances in automotive industry and telecommunication technologies, more and more vehicles are connected which paves the pathway to future intelligent transportation system (ITS). The safety of both the passengers and pedestrians has been a major concern in ITS. Vehicular ad hoc network (VANET) is a promising technology that can provide a wide range of services, such as safety, convenience, and infotainment for both the passengers and drivers. However, due to the dynamic nature of the vehicular environment, VANET has to deal with several challenges such as intermittent connectivity, provisioning of quality of service (QoS), and heterogeneity of applications. Moreover, the deployment of new services/protocols on a large-scale is a daunting task due to the inflexible architecture of VANET. The distinctive features of software-defined vehicular network (SDVN) such as programmability and flexibility leverages the vehicular network to satisfy the performance and management requirements of VANET. In this paper, we first present the taxonomy of SDVN architecture based on its modes of operation. Then we survey the state-of-the-art SDVN routing protocols and classify them based on different criteria. Finally, we highlight the challenges of current SDVNs.

Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ETX$ </tex-math></inline-formula> ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.

A weight-based conditional privacy-preserving authentication scheme in software-defined vehicular network

The rapid development of vehicular ad hoc networks (VANETs) has brought significant improvement to traffic safety and efficiency. However, owing to limitations associated with VANETs’ own unchanging model and traditional network structure, there are still many challenging concerns such as poor flexibility and controllability to deal with. To solve these inherent problems effectively, we propose a weight-based conditional anonymous authentication scheme by introducing the newly emerging software-defined networking (SDN) framework. Firstly, by making use of the global planning and dynamic management features of SDN, vehicles are classified into different priorities using weighted values to reduce communications redundancy, and control the participation of malicious vehicles. Then, an efficient conditional privacy-preserving scheme was developed to secure communications among vehicles. A two-step tracing approach has been designed to exclude and punish vehicles whose weights drop below the threshold. Extensive analyses indicate that our conditional privacy-preserving scheme is secure and has lower computation costs than conventional state-of-the-art authentication schemes.

Diversified Technologies in Internet of Vehicles Under Intelligent Edge Computing

To investigate the diversified technologies in Internet of Vehicles (IoV) under intelligent edge computing, artificial intelligence, intelligent edge computing, and IoV are combined. Also, it proposes an IoV model for intelligent edge computing task offloading and migration under the SDVN (Software Defined Vehicular Networks) architecture, that is, the JDE-VCO (Joint Delay and Energy-Vehicle Computational task Offloading) optimization. And the simulation is performed. The results show that in the analysis of the impact of different offloading strategies on the IoV, it is found that the JDE-VCO algorithm is superior to other schemes in terms of transmission delay and total offloading energy consumption. In the analysis of the impact of the task unloading of the IoV, the JDE-VCO algorithm is less than RTO (Random Tasks Offloading) and UTO (Uniform Tasks Offloading) algorithm schemes in terms of the number of tasks per unit time, and the average task completion time for the same amount of uploaded data. In the analysis of the packet loss ratio and transmission delay, it can be found that the packet loss ratio and transmission delay of the JDE-VCO algorithm are less than the RTO and UTO algorithms. Moreover, the packet loss ratio of the JDE-VCO algorithm is about 0.1, and the transmission delay is stable at 0.2s, which has obvious advantages. Therefore, through research, the IoV model of task offloading and migration built by intelligent edge computing can significantly improve the load sharing rate, offloading efficiency, packet loss ratio, and transmission delay when the IoV is processing tasks and uploading data. It provides experimental basis for the improvement of the IoV system.

Enhancing the Performance of Flow Classification in SDN-Based Intelligent Vehicular Networks

Intelligent vehicular networks converged with software-defined networking provides several flow-based surveillance services to mobile applications on vehicular nodes. But, as the scale of such networks grows exponentially, a substantial delay in processing tremendous flows emerges. The delay can be reduced by accelerating the packet classification methods, which are nowadays exploited in software-defined vehicular networks. Fast packet classification lets firewalls to inspect each incoming packet at wire speed. One of the well-known packet classification methods is the KD-tree algorithm. This paper presents an enhanced version of this algorithm that uses the geometric space to display different fields and increases search speed by recursive decomposition of the search space. Also, the enhanced KD-tree is integrated with a leaf-pushing technique, which enhances the performance of KD-tree search during classification. The proposed algorithm is implemented using a bloom filter data structure and a hash table. Experimental results show that the proposed leaf-pushed KD-tree algorithm improves packet classification speed up to 24 times in comparison with the conventional KD-tree. Moreover, the proposed algorithm can significantly reduce the classification time in comparison with state-of-the-art tree-based algorithms.

Energy-Efficient End-to-End Security for Software-Defined Vehicular Networks

One of the most promising application areas of the industrial Internet of Things (IIoT) is vehicular ad hoc networks (VANETs). VANETs are largely used by intelligent transportation systems to provide smart and safe road transport. To reduce the network burden, software-defined networks (SDNs) act as a remote controller. Motivated by the need for greener IIoT solutions, this article proposes an energy-efficient end-to-end security solution for software-defined vehicular networks (SDVNs). Besides, SDN's flexible network management, network performance, and energy-efficient end-to-end security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: 1) in roadside unit (RSU)-based group authentication scheme, each vehicle in the RSU range receives a group ID-key pair for secure communication; and 2) in private collaborative intrusion detection system (p-CIDS), the SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 and MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.

Stochastic performance modeling and analysis of multi service provisioning with software defined vehicular networks

Tremendous growth of traffic in Vehicular Ad hoc NETworks (VANET) in recent days leads into enormous delay in data transfer. To overcome this, we use Software Defined Vehicular Networks (SDVN) as a promising solution with reliable multi-hop cooperative data dissemination (MHCDD) for data traffic forwarding in the network of end to end connectivity. In SDN based system, data forwarding in a road side unit is through cooperatively data dissemination. The intermittent connectivity in VANET dynamically changes because of the vehicular mobility, thereby causing SDN and RSU controller to link/connection failures. However, in multi-hop data traffic forwarding from source to destination node through RSU and SDN switches in which controllers control the data traffic for Virtual Network Customization (VNC). In this paper, we introduce Software defined-VANETs (SD-VANETs) to improve the network resource utilization by providing the network connectivity for dynamic network topology. Here, we analyze the network model using M/M/1 and M/M/m queueing models, which are proposed to effectively minimize the delay and enhance the throughput in multi-server queueing system of the SDVN. To achieve this, we propose a novel solution with enhancement of the SDVN system reliability in terms of service rate which is based on service level agreement.

Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks

With recent development in vehicular communication technologies, much attention has been paid to data dissemination in vehicular networks. In particular, the infrastructure-to-vehicle (I2V) communication is one of the primary technologies to provide a variety of information services. To enhance the bandwidth efficiency of I2V communication, this work considers in a software-defined vehicular networks (SDVN), aiming at exploiting synergistic effects of network coding and vehicular caching. First, we consider a data service scenario in which roadside unites (RSUs) are connected with the controller, which exercises scheduling decisions based on service requests received from vehicles. On this basis, we formulate a cooperative coding and caching scheduling problem with the objective of maximizing the bandwidth efficiency of I2V communication. Then, we propose a binary particle swarm optimization (BPSO)-based coding scheduling (BPSO_CS) algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation. The results conclusively demonstrate the superiority of the proposed solution.

Towards Dynamic Controller Placement in Software Defined Vehicular Networks.

The emerging SDVN (Software Defined Vehicular Network) paradigm promises to bring flexibility and efficient resource utilization to vehicular networks, enabling the emergence of novel Intelligent Transportation Services. However, as it was initially designed with wired network in mind, applying the SDN paradigm to a vehicular context faces new challenges related to the peculiar characteristics of this network (high node mobility and node density, and the presence of wireless links). In this paper, we focus on one of the critical architectural elements of SDVN, namely, the SDN Controller Placement, and promote the use of dynamic placement methods that take into account the dynamicity of vehicular networks’ topology. We also describe the different approaches towards a dynamic controller placement and also propose an ILP (Integer Linear Programming) based dynamic placement method that adaptively readjusts the number and placement of controllers according to road traffic fluctuations. The proposed method is evaluated using a realistic traffic trace from Luxembourg City. Simulation results show that our approach outperforms the static approach as proposed in the literature.

Routing Schemes in Software-Defined Vehicular Networks: Design, Open Issues and Challenges

Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature to well compute the flow tables. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed.

Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration.

Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software- Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SDN OpenFlow controller. From SDVN, new vehicular architectures are introduced, for instance SD Vehicular Cloud (SDVC). In SDVC, vehicles are SDN devices that host virtualization technology for enabling deployment of cloud-based vehicular applications. In addition, the migration of Virtual Machines (VM) over SDVC challenges the performance of cloud-based vehicular applications due the highly mobility of vehicles. However, the current literature that discusses VM migration in SDVC is very limited. In this paper, we first analyze the evolution of computation and networking technologies of SDVC with a focus on its architecture within the cloud-based vehicular environment. Then, we discuss the potential cloud-based vehicular applications assisted by the SDVC along with its ability to manage several VM migration scenarios. Lastly, we provide a detailed comparison of existing frameworks in SDVC that integrate the VM migration approach and different emulators or simulators network used to evaluate VM frameworks’ use cases.

Soft-VAN: Mobility-Aware Task Offloading in Software-Defined Vehicular Network

In this paper, we propose a mobility-aware task offloading scheme, named as Soft-VAN, with an aim to minimize task computation delay in a software-defined vehicular network. The proposed scheme consists of two phases - fog node selection and task offloading. In the first phase, we formulate an integer linear program (ILP), and solve the problem to get optimal number of fog nodes required for a given network. In the task offloading phase, we formulate an optimization problem to minimize overall delay in task computation, while considering associated constraints. As finding optimal solution to the problem is NP-hard, we propose a greedy heuristic approach in two phases - task offloading and computed task downloading - to solve it in polynomial time. The greedy solution for offloading takes into account network delay, flow-rule capacity, and link utilization. On the other hand, the solution for computed task downloading considers vehicle's mobility in addition to the parameters associated with the offloading decisions. Experimental results show that the proposed scheme, Soft-VAN, is capable of enhancing the performance approximately by 30%, 45%, and 50% in terms of delay compared to state-of-the-art schemes - Detour, DAGP, and SD2O, respectively.

Cross-Layer and SDN Based Routing Scheme for P2P Communication in Vehicular Ad-Hoc Networks

Conventional routing protocols proposed for Vehicular Ad-hoc Network (VANET) are usually inefficient and vulnerable for multi-hop data forwarding due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) technologies, link stability can be better improved through the availability of global network information. Thus, in this paper, we present a novel software-defined network (SDN) based routing scheme for P2P connection under urban inter-vehicle networks that can find a global optimal route between source and destination. This is a cross-layer routing protocol in VANETs, which utilizes metrics not only considering the position and velocity of vehicles, but also channel allocation and link duration when selecting the relay vehicles. Consequently, it starts a route discovery process which can improve the network performance in terms of end-to-end delay and low overhead. Furthermore, packet loss is largely minimized by the relatively stable paths. With the help of realistic simulation, we show that the proposed routing framework performs better than other three latest SDVN and conventional VANET protocols in routing overhead, average end-to-end delay, packet drop ratio, and average throughput. Therefore, our routing scheme is more suitable for 5G-enabled vehicular ad-hoc networks in future.

SDN-based real-time urban traffic analysis in VANET environment

Accurate and real-time traffic flow prediction plays a central role for efficient traffic management. Software Defined Networking (SDN) is one of the key concerns in networking that has caught much attention in recent years. Extensive advancement in software-based configurable equipment has made ready for a new networking paradigm called software-defined vehicular networks (SDVNs). In this paper, we propose a data-driven approach for implementing an artificially intelligent model for vehicular traffic behavior prediction. We combine the flexibility, scalability, and adaptability leveraged by the SDVN architecture along with the machine learning algorithms to model the traffic flow efficiently. First, we introduce an ingenious approach to find congestion sensitive spots in the VANET by means of clustering algorithm and then predicting the future traffic densities for each spot by recurrent neural networks (RNNs). Neural networks (NNs) have been extensively used to model short-term traffic prediction in the past years. We construct a long short-term memory neural network (LSTM-NN) architecture which overcomes the issue of back-propagated error decay through memory blocks for spatiotemporal traffic prediction with high temporal dependency. Due to such learning ability, LSTM can capture the stochastic characteristics and non-linear nature of the traffic flow. The performance evaluation of the proposed approach shows that it has the potential to predict real-time traffic trends accurately with Mean Squared Error (MSE) of 3.0e−3 in the scaled metrics, which is equivalent to 97% accuracy in traffic density prediction.

A blockchain‐based framework to secure vehicular social networks

AbstractVehicular social network is emerging as a new promising concept, combining two types of network paradigms, namely, vehicular networks and social networks. In order to manage efficiently the security and the control of the network, this paper proposes a new framework based on the emerging concepts of software‐defined vehicular network (SDVN) and blockchain. Using the SDVN makes the network more programmable, virtualized, and partitionable. However, on the other hand, it also creates a well‐known vulnerability of a single point of failure. Hence, we propose to introduce the blockchain paradigm that will enable the certification of transactions and ensure data anonymity in a fully distributed manner. To this end, three levels of controllers are needed: a principal controller (PC), roadside units (RSUs), and a local controller. In order to dynamically select miners, a distributed miners connected dominating set algorithm (DM‐CDS) has been proposed. The DM‐CDS is a single‐phase distributed algorithm that supports a dynamic topology based on a trust model and some other network parameters, such as the connectivity degree, the average link quality indicator, and the rank. The performance of the proposed DM‐CDS is evaluated throughout multiple scenarios using different parameters, such as trust metric, node density, node mobility, and radio range. The obtained results highlight the importance of such proposed architecture, especially in terms of number of required miners. For instance, when the density of nodes increases, the number of selected miners increases similarly to when the network length increases. The node mobility impacts also on the stability of the selected miners, in terms of withdrawing and joining, showing a variation between 0% and 10%. The trust metric has also an important impact on the selection of miners, as only nodes with a higher trust level are selected to endorse the roles of miners.