Blockchain-Based Software-Defined Vehicular Networks for Intelligent Transportation System Beyond 5G

The growth of technical revolution towards 5G Next generation networks is expected to meet various communication requirements of future Intelligent Transportation Systems (ITS). Motivated by the consumer needs for variety of ITS applications, bandwidth, high speed and ubiquity, researches are currently exploring different network architectures and techniques, which could be employed in Next generation ITS. To provide flexible network management, control and high resource utilization in Vehicular Ad-hoc Networks (VANETs) on large scale, a new hierarchical 5G Next generation VANET architecture is proposed. The key idea of this holistic architecture is to integrate the centralization and flexibility of Software Defined Networking (SDN) and Cloud-RAN (CRAN), with 5G communication technologies, to effectively allocate resources with a global view. Moreover, a fog computing framework (comprising of zones and clusters) has been proposed at the edge, to avoid frequent handovers between vehicles and RSUs. The transmission delay, throughput and control overhead on controller are analyzed and compared with other architectures. Simulation results indicate reduced transmission delay and minimized control overhead on controllers. Moreover, the throughput of proposed system is also improved.

Intelligent Transportation Systems (ITS) are increasingly shifting toward data-driven and artificial intelligence-based frameworks that integrate sensing, prediction, and control to enhance mobility efficiency, safety, and adaptability. This dissertation contributes to the development of next-generation ITS through three complementary studies focusing on flexible traffic monitoring, real-time traffic forecasting, and stability-aware vehicle control. The first study develops a multi-drone framework for wide-view corridor traffic analysis and origin-destination (OD) estimation. By coordinating multiple unmanned aerial vehicles, as demonstrated with two drones in this study, the framework enables continuous vehicle tracking across extended corridors. A deep learning-based pipeline combining vehicle detection and tracking automatically reconstructs vehicle trajectories and matches them across drone views. Field experiments conducted at a freeway weaving section achieved over 91% accuracy in OD flow estimation compared with ground-truth data, demonstrating the framework’s effectiveness for scalable and high-resolution traffic monitoring. The second study proposes a day-specific spatial-temporal graph convolutional network (Day-STGCN) for real-time traffic forecasting. Building upon the classical STGCN architecture, the model integrates rolling historical time-dependent features with day-type categorization to capture both short-term variations and long-term recurring patterns. Using real-world sensor data, the model significantly improves prediction accuracy over baseline graph neural networks, providing robust day-aware forecasting that reflects realistic daily traffic dynamics. The third study investigates cooperative adaptive cruise control (CACC) and its effects on traffic string stability under varying market penetration rates of connected and automated vehicles (CAVs). Analytical derivations establish explicit stability conditions linking controller parameters, time headway, and control intervals in both homogeneous and heterogeneous traffic flows. The results reveal that appropriately designed CACC controllers enhance string stability even in mixed environments with different types of vehicles. Collectively, these studies present an integrated framework that advances ITS through data-driven sensing, learning, and control, offering theoretical and practical foundations for more intelligent, responsive, and stable transportation systems.

Low density of vehicles in rural areas and during the periods when the vehicle traffic is low in cities (such as at night) makes the classical ad-hoc network algorithms impractical for Vehicular Ad Hoc Networks. End-to-end connectivity requirement of these algorithms causes this deflection. Connections get broken when a suitable network element isn't available for data transfer. This problem which occurs in the low dense Vehicular Networks can be solved by using delay tolerant approaches in routing algorithms. By using the store-carry-forward method, data is carried by network elements until a suitable network element has been found. By this way, network connectivity is provided in a delayed manner between the vehicles which are momentarily not connected. In this paper, the impact of delayed transfer of data to an end-to-end transfer and the impact of data lifetime to the network performance are examined. Considering the speed of the vehicles and the desired throughput, lifetime of data is determined according to the network connectivity. Results contributes to the fulfilment of the Quality of Service (QoS) requirements in Vehicular Network applications.

Vehicular communication networking is one of the most important building blocks of Intelligent Transportation System (ITS). The vehicular communication network is a wireless communication system enabling vehicles to communicate with each other as well as with roadside base stations. Mobility management of vehicles which move at high speeds and occasionally make a long journey is an interesting research area of vehicular communication networks. Recently, The Proxy Mobile IPv6 (PMIPv6) protocol is proposed for network-based mobility management to reduce the overhead of mobile nodes. PMIPv6 shifts the burden of the mobility management from mobile nodes to network agents to decrease the overhead and latency for the mobility management. In this paper, we derive the scenario of deploying PMIPv6 in vehicular communication networks and propose a new LMA handover mechanism for realizing the scenario. By carrying out the ns-2 based simulations, we verify the operability of the proposed mechanism.

SummaryIn smart cities, a large number of vehicles are connected into an intelligent transportation system and share information via the vehicular communication network (VCN). Accurate, fine‐grained, and comprehensive traffic measurements are very crucial for the controller's decision making in software‐defined networking (SDN) in VCN. Fine‐grained traffic measurements can accurately portray network behaviors for VCN. However, this will increase a large amount of measurement overhead. Therefore, how to effectively obtain accurate and fine‐grained traffic is a huge challenge for VCN. To the end, this paper proposes a novel accurate and SDN‐based fine‐grained traffic measurement approach to obtain comprehensive traffic in VCN. Firstly, based on SDN architecture, we exploit the pull‐based sampling mechanism to quickly obtain coarse‐grained traffic measurement values. Secondly, based on the matrix completion theory, we use both interpolation and optimization methods to obtain fine‐grained traffic measurements. Thirdly, the optimization model and detailed algorithm are proposed to attain accurate traffic. Finally, we conduct a larger number of simulations to validate the measurement approach proposed in this paper. Simulation results show that our approach exhibits better performance and is promising.

Location tracking of mobile entity becomes a research challenge for applications in vehicular networks, mobile sensor networks and mobile adhoc networks. The GPS based tracking in vehicular networks has limitations in certain scenarios such as inside tunnels, tall buildings, dense forests, etc. Tracking of fast-paced mobile entity (say mobile node) finds applications in vehicular networks such as chasing, hit-and-run accidents, etc. Connected dominating set(CDS) provides a virtual backbone in adhoc network. This paper uses connected dominating set(CDS) based virtual backbone in vehicular adhoc networks for tracking of fast-paced mobile node's location. We show that using a simple heuristics that the location updates for mobile node can be reduced to the tune of 40%.

SummaryThe recent breakthroughs in the automobile industries and telecommunication technologies along with the exceptional multimodal mobility services brought focus on intelligent transportation system (ITS), of which vehicular ad hoc networks (VANETs) gain much more attention. The distinctive features of software‐defined networking (SDN) leverages the vehicular networks by its state of the centralized art having a comprehensive view of the network. Its potential to bring the flexibility, programmability and other extensive advancements to vehicular networks has set the stage for a novel networking paradigm termed as software‐defined vehicular networks (SDVNs). Many researchers have demonstrated the SDN‐based VANETs with the various configuration of the SDN components in VANET architecture. However, a compilation of the work on the SDN‐based VANET system as a whole, incorporating its architecture, use‐cases, and opportunities, is still inadequate. We start with the summary of the recent studies that exist on the SDVNs, followed by the comprehensive explanation of its components. Next, we present the taxonomy of SDVN based on the architecture modes, protocols, access technologies, and opportunities with trending technologies. Finally, we highlight the challenges, open research issues, and future research directions.

Intelligent Transportation Systems (ITS) are gaining momentum among researchers. ITS encompasses several technologies, including wireless communications, sensor networks, data and voice communication, real-time driving assistant systems, etc. These states of the art technologies are expected to pave the way for a plethora of vehicular network applications. In fact, recently we have witnessed a growing interest in Vehicular Networks from both the research community and industry. Several potential applications of Vehicular Networks are envisioned such as road safety and security, traffic monitoring and driving comfort, just to mention a few. It is critical that the existence of convenience or driving comfort services do not negatively affect the performance of safety services. In essence, the dissemination of safety services or the discovery of convenience applications requires the communication among service providers and service requesters through constrained bandwidth resources. Therefore, service discovery techniques for vehicular networks must efficiently use the available common resources. In this thesis, we focus on the design of bandwidth-efficient and scalable service discovery protocols for Vehicular Networks. Three types of service discovery architectures are introduced: infrastructure-less, infrastructure-based, and hybrid architectures. Our proposed algorithms are network layer based where service discovery messages are integrated into the routing messages for a lightweight discovery. Moreover, our protocols use the channel diversity for efficient service discovery. We describe our algorithms and discuss their implementation. Finally, we present the main results of the extensive set of simulation experiments that have been used in order to evaluate their performance.

Smart vehicles is one of the innovation that can be used to increase the effectiveness of traffic in smart city as urban area and in the highway. In its development, smart vehicles not only require mobile communication services to ensure safety but also multimedia services for the passengers. These services must be integrated on intelligent transportation system. 5G vehicular network technology provides data communication services for intelligent transportation system via macrocells and smallcells to support smart vehicles which can drive at a maximum speed of 500Km/hour with multimedia services for passengers with data rates reaching the Gbps level. This paper will review 5G network architecture and emerging technologies that enabling vehicular network for smart vehicles in smart city.

Vehicular Ad-hoc Networks (VANET) have emerged as a viable solution for the network related problems in Intelligent Transportation Systems (ITSs). However, with its current architecture, especially with the unavailability of the global network information, VANET has not been able to fulfill some of the necessities for efficient information dissemination and most critically, still lacks flexibility and programmability. Recently, Software Defined Networking (SDN) has been introduced into the vehicular domain in order to overcome the shortcomings in VANET. SDN is capable of handling the dynamic nature of networks with intelligent applications while lowering operational costs through simplified hardware and software. Yet, the architectural limitations of the presented Software Defined Vehicular Networks (SDVN), especially the centralization of control plane and its placement, have imposed doubts on its usability especially in the midst of stringent delay requirement of VANET applications.

As the automotive and telecommunication industries advance, more vehicles are becoming connected, leading to the realization of intelligent transportation systems (ITS). Vehicular ad-hoc network (VANET) supports various ITS services, including safety, convenience, and infotainment services for drivers and passengers. Generally, such services are realized through data sharing among vehicles and nearby infrastructures or vehicles over multi-hop data routing mechanisms. Vehicular data routing faces many challenges caused by vehicle dynamicity, intermittent connectivity, and diverse application requirements. Consequently, the software-defined networking (SDN) paradigm offers unique features such as programmability and flexibility to enhance vehicular network performance and management and meet the quality of services (QoS) requirements of various VANET services. Recently, VANET routing protocols have been improved using the multilevel knowledge and an up-to-date global view of traffic conditions offered by SDN technology. The primary objective of this study is to furnish comprehensive information regarding the current SDN-based VANET routing protocols, encompassing intricate details of their underlying mechanisms, forwarding algorithms, and architectural considerations. Each protocol will be thoroughly examined individually, elucidating its strengths, weaknesses, and proposed enhancements. Also, the software-defined vehicular network (SDVN) architectures are presented according to their operation modes and controlling degree. Then, the potential of SDN-based VANET is explored from the aspect of routing and the design requirements of routing protocols in SDVNs. SDVN routing algorithms are uniquely classified according to various criteria. In addition, a complete comparative analysis will be achieved to analyze the protocols regarding performance, optimization, and simulation results. Finally, the challenges and upcoming research directions for developing such protocols are widely stated here. By presenting such insights, this paper provides a comprehensive overview and inspires researchers to enhance existing protocols and explore novel solutions, thereby paving the way for innovation in this field.

Software-Defined Networking (SDN) is a developing architecture that provides scalability, flexibility, and efficient network management. However, optimal controller placement faces many problems, which affect the performance of the overall network. To resolve the Multi-controller SDN (MC-SDN) that is deployed in the SDN environment, we propose an approach that uses a hybrid metaheuristic algorithm that improves network performance. Initially, the proposed SDN network is constructed based on graph theory, which improves the connectivity and flexibility between switches and controllers. After that, the controller selection is performed by selecting an optimal controller from multiple controllers based on controller features using the firefly optimization algorithm (FA), which improves the network performance. Finally, multi-controller placement is performed to reduce the communication latency between the switch to controllers. Here, multiple controllers are placed by considering location and distance using a hybrid metaheuristic algorithm, which includes a harmonic search algorithm and particle swarm optimization algorithm (HSA-PSO), in which the PSO algorithm is proposed to automatically update the harmonic search parameters. The simulation of multi-controller placement is carried out by the CloudsimSDN network simulator, and the simulation results demonstrate the proposed advantages in terms of propagation latency, Round Trip Time (RTT), matrix of Time Session (TS), delay, reliability, and throughput.

The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.

Nowadays, the Intelligent Transport Systems (ITS) attract many attentions. ITS applications would indeed increase the road safety and the transport efficiency, limit the impact of the vehicles on the environment, improve the overall productivity... However, several important open issues have to be solved before. Among them, the software architecture represents a key issue. Indeed, most of the ITS applications will rely on a distributed frameworks embedded in the vehicles. These applications often require robustness, quality of services or real time management, while the vehicular networks present important constraints in terms of dynamic, variable density, communication reliability, communication duration...

Recent advances in information and communications cloud-based services hold the potential to overcome the scalability and complex maintenance limitations of traditional networks. Software Defined Networking (SDN) surfaced as a promising paradigm to mitigate such limitations while offering flexible networks management. Particularly, SDN separates the control plane from the data plane to achieve abstraction of lower-level functionality, hence, allowing more efficient network management and utilization. However, SDN suffers from various performance and scalability problems leading to significant research efforts on maximizing the scalability of the control plane. This paper aims at reviewing different SDN controller scalability, topology-based and mechanism-based approaches, as well as discussing and analyzing how they attempt to solve the scalability challenge. Furthermore, this paper elaborates on the promising research trends and challenges. Our insights are also discussed to stimulate further research efforts addressing the control plane scalability in SDN.