Resource Management for Blockchain-enabled Internet of Vehicles

Recent advances in wireless communication, sensing, computation and control technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Demanded by the requirements of information-centric and data-driven intelligent transportation systems (ITS), it is of great significance to explore new paradigms of IoV in supporting large-scale, real-time, and reliable information services. In this article, we propose a hierarchical system architecture, which aims at synthesizing the paradigms of software defined networking and fog computing in IoV and best exploiting their synergistic effects on information services. Specifically, a four-layer architecture is designed, comprising the application layer, the control layer, the virtualization layer, and the data layer, with objectives of enabling logically centralized control via the separation of the control plane and the data plane; facilitating adaptive resource allocation and QoS oriented services based on network functions virtualization and network slicing, and enhancing system scalability, responsiveness, and reliability by exploiting the networking, computation, communication, and storage capacities of fog-based services. On this basis, we further analyze newly arising challenges and discuss future research directions by presenting a cross-layer protocol stack. Finally, for the proof of concept, we implement the system prototype and give two case studies in real-world IoV environments. The results of field tests not only demonstrate the great potential of the new architecture, but also give insight into the development of future ITS.

The sensing and communication capabilities of the Internet of Vehicles (IoV) allow for the design of safer Intelligent Transportation Systems (ITS), with capabilities for reduced crashes and drivers' injuries. To this aim, entities in the IoV can autonomously exchange Warning Messages (WMs) on the road status to help drivers, passengers, and vehicles to take immediate actions and reduce road accidents. WMs authentication, timely propagation, and integrity is a fundamental requirement, as the injection of false WMs or their delay by a malicious user can lead to accidents or traffic congestion. The currently available WMs authentication and propagation schemes focus on the security properties, without however accounting for the scalability issues related to the large number of entities connected in the IoV. In this paper, we propose Secure Warning Message Authentication and Propagation (SWAP), an authentication protocol specifically designed for WM propagation in the Internet of Vehicles (IoV). We introduce the Warning Message Authority (WMA), a novel entity responsible for secure WMs handling. Thanks to the WMA, WMs are managed by dedicated resources that allow for the timely delivery of safety-critical information, avoiding delays due to network congestion at the Trusted Authority (TA). We prove the security of SWAP by using the Random Oracle Model (ROM) and via security analysis. Compared to state of the art authentication schemes, SWAP reduces the number of communication bits by 100 bits, and provides a ten-fold decrease of the computation time.

To enable intelligent transportation systems, the Internet of Vehicles (IoV) uses wireless communications between roadside units, onboard units), phones, and other gadgets within a vehicle to deliver a new convenient, comfortable, and safe driving experience. IoV is now more exposed and frequently targeted because of its greatly increased reliance on wireless communication, control, and computer technology, To put it another way, an adversary may target such an open channel to intercept, change, inject malicious messages, or erase loaded data. This could lead to replay, impersonation, middle attack, privileged insider, and other related attacks. An authentication protocol should strive to accomplish security, anonymity, and untraceability among other critical aspects. In this research, we suggest a brand-new key agreement and mutual authentication methodology for IoV powered Intelligent transportation system (ITS). We demonstrate that our suggested system is secure against several known threats in IoV- the enabled ITS environment. Furthermore, a thorough comparison of our proposed system to eight other competing ones reveals that it has the lowest communication and computational costs. In addition, it is more secure and provides more functionality characteristics.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber-physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

Intelligent Transportation System (ITS) and Internet of Vehicles (IoV) allows the vehicles to disseminate and exchange various messages among nearby vehicles in the network. It includes road safety, road accidents, location sharing, driver assistance, navigation, collision warning, traffic data, and toll payment. Moreover, the exchange of these messages is carried out in an insecure channel. Hence, it leads to several issues during transmission of these messages in resource constrained IoV networks. It includes reliable data dissemination, dynamic topology, mobility of the vehicles, vehicle user’s privacy, and lightweight authentication mechanism. The existing Vehicle to Vehicle (V2V) data dissemination techniques have limitations in security attacks such as side channel, device stolen, cloning attacks, high computational overhead, throughput, and high communication cost. In this article, we propose P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV: Privacy Preserving Lightweight Secure (V2V) Data Dissemination Scheme for IoV environment using lightweight cryptographic primitives such as SHA-256, concatenation and XoR operation. We evaluate P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV in terms of security and vehicle user’s privacy against several security attacks. We do an in-depth security analysis of the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV by using informal security analysis and provides security proofs. We also perform a comparative analysis of P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV with the existing state-of-the-art IoV environment using various performance parameters. The test-bed experimental result shows that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV outperforms the existing V2V data dissemination technique in terms of computational cost, communication overhead, and energy consumption.

The convergence of vehicular technology, artificial intelligence (AI), and distributed computing has catalyzed the emergence of the Internet of Vehicles (IoV) as a cornerstone of next-generation intelligent transportation systems (ITS). By enabling vehicle-to-everything (V2X) communication, IoV supports cooperative perception, real-time decision-making, and autonomous driving. However, the reliance on large-scale, data-driven intelligence in IoV exposes systems to critical challenges, including adversarial poisoning, privacy leakage, identity forgery, and the fragility of centralized learning architectures. Federated Learning (FL) has been proposed as a promising paradigm to alleviate some of these issues by enabling distributed model training without centralizing sensitive vehicular data. Nonetheless, conventional FL remains vulnerable to security and trust limitations, particularly in dynamic vehicular environments. This thesis addresses these challenges by designing secure, privacy-preserving, and scalable FL frameworks that leverage distributed ledger technologies and cutting-edge security mechanisms.The thesis advances knowledge through four interconnected contributions. First, two novel optimization-driven poisoning attack models are introduced: PA-PSOSA and PAPSOGA, which combine particle swarm optimization with simulated annealing and genetic algorithms, respectively. These models demonstrate that even a small poisoning budget can substantially degrade global model utility under black-box and clean-label constraints, highlighting the urgency of robust defenses in vehicular FL. Second, a permissioned blockchain-enabled FL (BCFL) framework is proposed, in which consortium edge nodes running Practical Byzantine Fault Tolerance (PBFT) consensus replace the central aggregator. With blockchain integration and data validation mechanisms, this design ensures identity authentication, verifiable audit trails, and improved resilience against poisoning and Sybil attacks, while maintaining high model accuracy under adversarial conditions. Third, the framework is further enhanced to achieve inference-resistance by integrating secure aggregation (SecAgg) and differential privacy (DP), and lightweight with off-chain commitments. This design significantly reduces ledger storage requirements, increases system throughput, and mitigates inference-based privacy risks. Finally, to overcome the scalability limitations of PBFT-based BCFL, a DAG-enabled FL (DFL) framework is developed. By leveraging parallel validation, utility-score-based tip selection, and reputation-weighted aggregation, this framework significantly improves scalability, reduces communication complexity, and enhances robustness in asynchronous vehicular environments.Together, these contributions articulate a coherent progression from exposing vulnerabilities in vehicular FL to constructing secure, privacy-preserving, and scalable frameworks tailored for IoV ecosystems. The findings demonstrate that interdisciplinary integration of optimization theory, cryptography, differential privacy, and distributed ledger technologies is indispensable for trustworthy vehicular intelligence. Beyond theoretical significance, the proposed frameworks offer practical designs for deployment in safety-critical IoV environments. Future research directions include the integration of zero-knowledge proofs (ZKP) for verifiable privacy, adaptive defenses against evolving adversarial strategies, and experimental validation in real-world vehicular testbeds. Collectively, this thesis establishes a foundation for secure federated intelligence in IoV, contributing to the reliability, efficiency, and trustworthiness of next-generation ITS.

Due to the rapid development of Intelligent Transportation System (ITS), application scenarios such as autonomous valet parking, convoy arrangement, vehicle–road cooperative control and vehicle emergency hedging have been realized. On the other hand, users need data application services with low latency, high quality and large data volume. So, communication and networking in the Internet of Vehicles (IoV) become the key to improve ITS system in the future. This chapter elaborates some different communication model in IoV. Then, each communication and networking model are proposed based on system model and confronting problems. In addition, this chapter introduces Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Network/Cloud (V2N/C), and trajectory-based communication modes. Finally, some use cases are discussed to list related work.KeywordsMessageCommunicationNetworkingIoVV2VV2IV2PV2N/CTrajectory-Based

With the incoming introduction of 5G networks and the advancement in technologies such as network function virtualization and software defined networking, new and emerging networking technologies and use cases are taking shape. One such technology is the Internet of Vehicles (IoV), which describes an interconnected system of vehicles and infrastructure. Coupled with recent developments in artificial intelligence and machine learning, IoV is transformed into an intelligent transportation system (ITS). There are, however, several operational considerations that hinder the adoption of ITSs, including scalability, high availability, and data privacy. To address these challenges, federated learning, a collaborative and distributed intelligence technique, is suggested. Through an ITS case study, the ability of a federated model deployed on roadside infrastructure throughout the network to recover from faults by leveraging group intelligence while reducing recovery time and restoring acceptable system performance is highlighted. With a multitude of use cases and benefits, federated learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.

In recent years, the term Internet of Vehicles (IoV) has gained popularity among researchers. We are in the era of smart things, smart cities, smart homes, smart transport and smart home appliances, which has been realized due to Internet of Things (IoT). With the invent of smart cars and emerging communication technologies among vehicles, IoV has become the field of research and thereby attracted several vehicle industries and researchers. Internet of Vehicles is the amalgamation of VANET with IoT. Several research challenges need to be addressed for the development of efficient transportation system using IoV. This paper presents the architecture of the IoV with five layers and simple network model of IoV. Recent trends of IoV have been identified in this paper and are discussed to present state-of-the-art advancements and future trends in IoV.

Nowadays, Internet of Vehicles (IoV) are considered as the most important promoter domain in the Intelligent Transportation System (ITS). Vehicles in IoV are characterized by a high nodes’ mobility, high nodes’ number and high data storage. However, IoV suffer from many challenges in order to achieve robust communication between vehicles such as frequent link disconnection, delay, and network overhead. In the traditional Vehicular Ad hoc NETworks (VANETs), these problems have often been solved by using clustering algorithms. Clustering in IoV can overcome and minimize the communication problems that face vehicles by reducing the network overhead and ensure some Quality of Service (QoS) to make network connectivity more stable. In this work, we propose a new Geo-graphical Information based Clustering Algorithm “GICA” destined to IoV environment. The proposal aims to maintain the cluster structure while respecting the quality of service requirements as the network evolves. We evaluated our proposed approach using the NS3 simulator and the realistic mobility model SUMO.

IBMAS (Identity-based message authentication systems) is a viable option for safeguarding data transfers in Internet of Vehicles (IoV) settings. We cover the state-of-the-art in IBMAS for IoVs in this book chapter, including their goal, techniques, results, contributions, implications, limits, and relevance. The goal of this chapter is to give academics and practitioners an overview of IBMAS for IoVs, including their design and implementation, to assist them in understanding the potential benefits and limits of these systems. We provide a study of existing IBMAS for IoVs in the literature, assessing their features, performance, and security aspects. We also propose a technique for assessing the efficacy of IBMAS for IoVs, concentrating on essential parameters such as message authentication performance, scalability, and attack resistance. The results of our test show that IBMAS can offer excellent message authentication with little overhead in IoV situations. Our addition to the literature is a thorough examination of IBMAS for IoVs, including their benefits and drawbacks, as well as a practical approach for assessing their performance and security qualities. We also talk about the consequences of IBMAS for IoVs, such as how they could affect data security and privacy in smart transportation systems. However, IBMAS has significant disadvantages, such as its reliance on a trusted third party and the risk of key exposure. Notwithstanding these limitations, IBMAS is important for IoVs because of its capacity to offer efficient and secure message authentication, which is crucial for the safe and dependable functioning of IoV systems. Finally, this chapter is an excellent resource for academics and practitioners interested in IBMAS for IoVs, covering their design, implementation, and assessment, as well as their implications for data security and privacy in intelligent transportation systems.

Internet of Vehicles (IoV) presents a new generation of vehicular communications with limited computation offloading, energy and memory resources with 5G/6G technologies that have grown enormously and are being used in wide variety of Intelligent Transportation Systems (ITS). Due to the limited battery power in smart vehicles, the concept of energy consumption is one of the main and critical challenges of the IoV environments. Optimizing resource management strategies for improving the energy consumption using AI-based methods is one of important solutions in the IoV environments. There are various machine learning algorithms for selecting optimal solutions for energy-efficient resource management strategies. This paper presents the existing energy-aware resource management strategies for the IoV case studies, and performs a comparative analysis among their applied AI-based methods and machine learning algorithms. This analysis presents a technical and deeper understanding of the technical aspects of existing machine learning and AI-based algorithms that will be helpful in design of new hybrid AI approaches for optimizing resource management strategies with reducing their energy consumption.

It aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet.

Internet of Vehicles (IoV) has developed into a useful service platform between vehicles and other vehicles, vehicles and roadways, and vehicles and pedestrians as a result of advancements in communication technology. However, along with the intelligent and networked IoV, IoV security issues are increasingly emerging, which often lead to extremely serious consequences. For example, when a vehicle is remotely attacked and maliciously controlled, it can lead to user privacy leakage. Attackers can also control the driving of vehicles through attacks, and there may even be a large number of connected cars being controlled in bulk, ultimately causing major social security incidents. In the Internet of Vehicles, authentication technology is one of the main solutions to address the security issues of the Internet of Vehicles. Many researchers have conducted some research on the authentication technology of vehicular network, but the traditional authentication scheme under the centralized system has problems such as frequent single-point failures and high overhead. When blockchain technology is properly implemented to the vehicle network environment, its security outperforms that of centralized systems due to features like decentralization, tamper-proof, security, and traceability. Because of this, we construct an optimization technique based on the Practical Byzantine Fault Tolerance (PBFT) algorithm in this work and present an efficient IoV decentralized authentication scheme based on the consensus algorithm of blockchain technology. After performance analysis, it is proved that our scheme has better performance and higher security.

The Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applications/services bring explosively growing demands for mobile data traffic between connected vehicles and roadside units (RSUs), imposing the significant challenge of spectrum scarcity in the IoV. In this article, we propose a cooperative semantic-aware architecture to convey essential semantics from collaborated users to servers for lessening data traffic. In contrast to current solutions that are mainly based on piling up highly complex signal processing techniques and multiple access capabilities in terms of syntactic communications, this article puts forth the idea of semantic-aware content delivery in the IoV. Specifically, the successful transmission of essential semantics of the source data is pursued rather than the accurate reception of symbols regardless of their meaning, as in conventional syntactic communications. To assess the benefits of the proposed architecture, we provide a case study of an image retrieval task for vehicles in intelligent transportation systems (ITSs). Simulation results demonstrate that the proposed architecture outperforms existing solutions, with fewer radio resources, especially in a low-signal-to-noise-ratio (SNR) regime, which can shed light on the potential of the proposed architecture in extending applications in extreme environments.