Software-defined Internet Of Vehicles Research Articles

SecGreen: Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV

Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> has an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as– physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> is compared with the existing schemes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stable &amp; Scalable Link Optimization (SSLO), and Secure &amp; Energy-Efficient Blockchain-enabled (SEEB)</i> respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> scheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> scheme in comparison to these existing competitive schemes in the literature.

How to Mitigate DDoS Intelligently in SD-IoV: A Moving Target Defense Approach

Software defined Internet of Vehicles (SD-IoV) is an emerging paradigm for accomplishing Industrial Internet of Things (IIoT). Unfortunately, SD-IoV still faces security challenges. Traditional solutions respond after attacks happening, which is low-effective. To cope with this problem, moving target defense (MTD) was proposed to modify network configurations dynamically. However, current MTD for IIoT has several drawbacks: 1) it cannot handle highly dynamic environments; 2) MTD strategy lacks intelligence because it needs attack–defense models; 3) they are difficult to trace sources. In this article, we propose an intelligent MTD scheme to defend against distributed denial-of-service in SD-IoV. Firstly, we model the configuration mutation of roadside units as a Markov decision process (MDP), and adopt deep reinforcement learning to solve the optimal configuration. Next, we evaluate the trust of vehicles after shuffling, which can distinguish spy vehicles. Finally, extensive simulation results confirm the effectiveness of our solution compared with representative methods.

Optimizing Gradient Methods for IoT Applications

Solving linear programming (LP) and nonlinear programming (NLP) problems is momentous because of their wide applications in real-life problems. There is no unified way to find the global optimum for NLPs. But on the other hand, the simplex algorithm, as the dominating methodology for LPs for several decades moves only on the boundary (vertices) and ignores the vast majority of the feasible region in the process of searching. In this article, we study two gradient-based methodologies that explore the whole feasible region, which guarantee faster convergence rates for both LP and NLP optimization problems including IoT problems such as Software-Defined Internet of Vehicles (SDIoV) and vehicular ad hoc networks (VANETs). The gradient-simplex algorithm (GSA) for LPs, which moves inside the feasible region in the gradient direction at first to reduce the search space and then explores the reduced boundary to find an optimal solution. The evolutionary-gradient algorithm (EGA), on the other hand, is for NLPs and uses an evolutionary population to estimate gradients by evolving to find better solutions in steps. Based on extensive simulations, the obtained numerical results show that both approaches provide efficient solutions and outperform the state-of-the-art methods on optimization problems with large feasible spaces. Comparative results of applying the GSA on SDIoV and VANETs with different sizes are included.

Guest Editorial Software Defined Internet of Vehicles

Internet of Vehicles (IoV) is a large interactive network composed of information such as vehicle location, speed and route. Vehicles can collect their own environment and state information through GPS, RFID, sensors, camera image processing, and other devices. They can transmit their various information to the central processing unit through Internet technology. These large amounts of vehicle information can be analyzed and processed through computer technology to calculate the optimal route for different vehicles, and report road conditions in time and schedule signal light cycles. Internet of Vehicles (IoV) is a large-scale system network for wireless communication and information exchange between vehicles and people, vehicles and roads, vehicles and the Internet, which is based on intra-vehicle network, inter-vehicle network and vehicle mobile Internet, in accordance with the agreed communication protocols and data interaction standards. The system realizes the integrated network of intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control by “filtering and cleaning” massive data and processing data on the platform. Internet of Vehicles (IoV) system utilizes advanced IoT technology, cloud computing, and big data to make the system fully aware of roads and traffic. It enables all vehicles to collect information through their own environment and state, and upload all kinds of information to the Internet big data platform. The central processing unit collects, analyzes, and processes a large amount of uploaded information. The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety.

Software Defined Internet of Vehicles for Automation and Orchestration

We are introducing an innovative networking approach to the Internet of Vehicles (IoV) for their automation and orchestration by redesigning the wireless edge framework. To this end, we propose to expand the Software Defined Networking (SDN) functionality from the edge network to the cars, i.e. equipping our vehicles with mobile base stations having SDN capabilities. By adopting such a novel approach, most of the impending IoV automation and orchestration challenges are ameliorated, which has been extremely difficult and non-trivial to tackle by using standard existing approaches. The three primary challenges identified in this context are a) the scalability of the network of connected autonomous vehicles, b) the desirable security of the IoV data networking, and c) the flexibility of quality of experience (QoE) perceived by the vehicles. To tackle the aforementioned three challenges, we design a policy-driven framework for a secure and efficient IoV networking paradigm and then investigate its performance via analytic modeling. More importantly, from the IoV data networking perspective, we design an intent-based flow offloading scheme to facilitate enhanced and adjustable QoE. Furthermore, we develop a rigorous analysis to quantify the efficiency of the proposed SDN-capable IoV data networking by modeling TCP connections over WiFi. Using mathematics as a tool for reasoning the three key challenges, our feasibility analysis is validated with extensive simulations. Finally, we provide new insights by deriving the stability conditions for the data flow dynamics of the proposed approach.

Blockchain and Deep Reinforcement Learning Empowered Spatial Crowdsourcing in Software-Defined Internet of Vehicles

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.

Fault-Tolerance Mechanisms for Software-Defined Internet of Vehicles

Due to the rapid development of the Internet of Things, cloud computing, big data, and software-defined networks technologies, vehicular ad hoc networks have evolved into software-defined Internet of Vehicles (IoV). In software-defined IoV, messages transmitted by vehicles are transferred using wireless communication technology, so attackers can easily attack specific vehicles and Road-Side Units (RSUs) within the wireless communication range. When any vehicle or RSU is under attack or control, the entire network may collapse and produce incorrect computing results, which in the worst case, can lead to car accidents. Therefore, building a highly reliable and fault-tolerant software-defined IoV is of high importance. In this paper, we visit the consensus problem in software-defined IoV. Software-defined IoV is built with infrastructures, that is, RSUs. RSUs have a backhaul capacity and a powerful computing capacity. Thus, in software-defined IoV, we will focus on how to use RSUs to assist vehicles in reaching a consensus.

Dynamic Controller Assignment in Software Defined Internet of Vehicles Through Multi-Agent Deep Reinforcement Learning

In this article, we introduce a novel dynamic controller assignment algorithm targeting connected vehicle services and applications, also known as Internet of Vehicles (IoV). The proposed approach considers a hierarchically distributed control plane, decoupled from the data plane, and uses vehicle location and control traffic load to perform controller assignment dynamically. We model the dynamic controller assignment problem as a multi-agent Markov game and solve it with cooperative multi-agent deep reinforcement learning. Simulation results using real-world vehicle mobility traces show that the proposed approach outperforms existing ones by reducing control delay as well as packet loss.

Adaptive Traffic Engineering Based on Active Network Measurement Towards Software Defined Internet of Vehicles

With the rapid development of urbanization, enormous amounts of vehicular services have been emerging and challenge both the architectures and protocols of the Internet of Vehicles. The high-speed mobility features of nodes in the vehicular networks changes the network topology frequently, resulting in low routing efficiency, and higher packet loss. In this article, we utilize software-defined networking (SDN) technology to decouple the network control plane from the data forwarding plane, and divide the vehicular networks into three functional layers: data, control, application layers. Based on the proposed network architecture, we propose an adaptive traffic engineering (TE) mechanism to guarantee the V2V continuous traffic in vehicular networks with high-speed mobile vehicles or dynamic network topology. In particular, the proposed TE is based on a proposed active network measurement mechanism under the assistance of the centralized management ability of the SDN technique. The proposed active network measurement approach is a greedy approach where the next hop determination for the measurement packet takes multiple link reliability factors (e.g., the delay, the length, the packet error rate, the neighbors, etc.) into account. Then, we utilize the artificial bee colony (ABC) algorithm to optimize the TE mechanism that can be deployed and executed in the SDN controller. By the proposed TE mechanism, multiple candidate end-to-end paths can be concurrently measured, and the optimal data forwarding path can be adaptively switched. Simulation results demonstrate that our approach performs better than some recent research outcomes, especially in the aspect of performing reliable data forwarding (almost 5% better than the compared objects).

Distributed Learning for Vehicle Routing Decision in Software Defined Internet of Vehicles

With the increasing number of vehicles, the traffic congestion is becoming more and more serious. In order to alleviate such a problem, this article considers transmission and inference delay of cloud centralized computing in the software defined Internet of Vehicles (SDIoV), and builds a new SDIoV architecture based on edge intelligence, for supporting real-time vehicle routing decision through distributed multi-agent reinforcement learning model. Then, a software defined device collaboration optimization method is designed to improve the efficiency of distributed training. Combined with multi-agent reinforcement learning, a distributed-learning-based vehicle routing decision algorithm (DLRD) is proposed to adaptively adjust vehicle routing online. The performed simulations show that the DLRD can successfully realize real-time routing decision for vehicles and alleviate traffic congestion with the dynamic changes of the road environment.

Autonomous Vehicle Source Enumeration Exploiting Non-Cooperative UAV in Software Defined Internet of Vehicles

The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, we construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.

Position Prediction for Routing in Software Defined Internet of Vehicles

By the prediction of future location for a vehicle in Internet of Vehicles (IoV), data forwarding schemes can be further improved. Major parameters for vehicle position prediction includes traffic ...

Quality of Service Aware Routing Protocol in Software-Defined Internet of Vehicles

Software-defined Internet of Vehicles (SDIoV) has emerged as a promising field of study as it could overcome the shortcomings of traditional vehicular networks, such as offering efficient data transmission and traffic shaping in different vehicular scenarios to satisfy all the requirements of applications on the fly. Although routing solutions are lightly addressed for SDIoV, there are many limitations of routing protocols unaddressed in such environment. More precisely, shortest path routing algorithms are mostly focused in the state of the arts. This paper presents quality of service aware routing algorithm that forwards packets toward the most reliable and connected path to the destination. Particularly, candidate routes should satisfy metrics, such as signal to interference and noise ratio (SINR) constraint and have the highest probability of connectivity. To address these issues, we have formulated a discrete optimization problem to favor the best route among candidate paths and proposed the modified laying chicken algorithm (LCA) that results better results than the traditional approaches. We have mathematically analyzed the probability of connectivity along with the SINR metric. Moreover, a multiscore function based on traffic density and greediness factor is proposed to make intelligent decision at the intersections. Simulation results are used to validate the superiority of the proposed routing approach over the existing solutions.

An MAC layer aware pseudonym (MAP) scheme for the software defined Internet of vehicles

This paper proposes a cross-layer design to enhance the location privacy under a coordinated medium access control (MAC) protocol for the Internet of Vehicles (IoV). The channel and pseudonym resources are both essential for transmission efficiency and privacy preservation in the IoV. Nevertheless, the MAC protocol and pseudonym scheme are usually studied separately, in which a new MAC layer semantic linking attack could be carried out by analyzing the vehicles' transmission patterns even if they change pseudonyms simultaneously. This paper presents a hierarchical architecture named as the software defined Internet of Vehicles (SDIV). Facilitated by the architecture, a MAC layer aware pseudonym (MAP) scheme is proposed to resist the new attack. In the MAP, RSU clouds coordinate vehicles to change their transmission slots and pseudonyms simultaneously in the mix-zones by measuring the privacy level quantitatively. Security analysis and extensive simulations are conducted to show that the scheme provides reliable safety message broadcasting, improves the location privacy and network throughput in the IoV.

Incentive edge caching in software‐defined internet of vehicles: A Stackelberg game approach

SummaryIn this paper, we investigate an incentive edge caching mechanism for an internet of vehicles (IoV) system based on the paradigm of software‐defined networking (SDN). We start by proposing a distributed SDN‐based IoV architecture. Then, based on this architecture, we focus on the economic side of caching by considering competitive cache‐enablers market composed of one content provider (CP) and multiple mobile network operators (MNOs). Each MNO manages a set of cache‐enabled small base stations (SBS). The CP incites the MNOs to store its popular contents in cache‐enabled SBSs with highest access probability to enhance the satisfaction of its users. By leasing their cache‐enabled SBSs, the MNOs aim to make more monetary profit. We formulate the interaction between the CP and the MNOs, using a Stackelberg game, where the CP acts first as the leader by announcing the popular content quantity that it which to cache and fixing the caching popularity threshold, a minimum access probability under it a content cannot be cached. Then, MNOs act subsequently as followers responding by the content quantity they accept to cache and the corresponding caching price. A noncooperative subgame is formulated to model the competition between the followers on the CP's limited content quantity. We analyze the leader and the follower's optimization problems, and we prove the Stackelberg equilibrium (SE). Simulation results show that our game‐based incentive caching model achieves optimal utilities and outperforms other incentive caching mechanisms with monopoly cache‐enablers whilst enhancing 30% of the user's satisfaction and reducing the caching cost.

Service-Oriented Dynamic Connection Management for Software-Defined Internet of Vehicles

Internet of vehicles (IoV) is an emerging paradigm for accommodating the requirements of future intelligent transportation systems (ITSs) with the overwhelming trend of equipping vehicles with versatile sensors and communications modules, and facilitating drivers and passengers with a variety of innovative ITS applications. However, the implementation of IoV still faces many challenges, such as flexible and efficient connections, quality of service guarantee, and multiple concurrent support requests. To this end, in this paper we introduce the software-defined IoV (SD-IoV), which is able to tackle the above-mentioned issues by adopting the software-defined networking framework. We first present the architecture of SD-IoV and develop a centralized vehicular connection management approach. Then, we aim to allocate dedicated communications resources and underlying vehicular nodes to satisfy each service. We formulate the dynamic vehicular connection as an overlay vehicular network creation (OVNC) problem. A comprehensive utility function is also designed to serve as the optimization objective of OVNC. Finally, we solve the OVNC problem by developing a graph-based genetic algorithm and a heuristic algorithm, respectively. Extensive simulation results are provided to demonstrate the effectiveness of our proposed solution of dynamic vehicular connection management.

Improved Rule Installation for Real-Time Query Service in Software-Defined Internet of Vehicles

Internet of Vehicles (IoV) has gained considerable attention from industry and academia due to the development of communication technology and smart city. However, a proprietary and closed way of operating hardware in network equipment slows down the progress of new service deployment and extension in IoV. Moreover, the tightly coupled control and data planes in traditional networks significantly increase the complexity and cost of network management. By proposing a novel architecture, i.e., software-defined IoV (SDIV), we adopt a software-defined network (SDN) architecture to address these problems by leveraging its separation of the control plane from the data one and a uniform way to configure heterogeneous switches. However, IoV characteristics introduce some great challenges in rule installation due to the limited size of flow tables at OpenFlow-enabled switches that are the main SDN component. It is necessary to build compact flow tables for IoV scalability. Accordingly, we develop a novel rule installation mechanism to reduce the number of rules for real-time query services in SDIV. We separate the wired data plane from the wireless one and use multicast addresses in the latter. We introduce a destination-driven model in the wired data plane to reduce the number of rules at switches. Experiments with a real data trace show that the developed approach significantly reduces the number of rules without degrading the performance of data transmissions for real-time query services in IoV.