An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT)

An overview of VANET vehicular networks

Today, with the development of intercity and metropolitan roadways and with various cars moving in various directions, there is a greater need than ever for a network to coordinate commutes. Nowadays, people spend a lot of time in their vehicles. Smart automobiles have developed to make that time safer, more effective, more fun, pollution-free, and affordable. However, maintaining the optimum use of resources and addressing rising needs continues to be a challenge given the popularity of vehicle users and the growing diversity of requests for various services. As a result, VANET will require modernized working practices in the future. Modern intelligent transportation management and driver assistance systems are created using cutting-edge communication technology. Vehicular Ad-hoc networks promise to increase transportation effectiveness, accident prevention, and pedestrian comfort by allowing automobiles and road infrastructure to communicate entertainment and traffic information. By constructing thorough frameworks, workflow patterns, and update procedures, including block-chain, artificial intelligence, and SDN (Software Defined Networking), this paper addresses VANET-related technologies, future advances, and related challenges. An overview of the VANET upgrade solution is given in this document in order to handle potential future problems

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Introduction to the Special Issue on Sustainable Solutions for the Intelligent Transportation Systems

The intelligent transportation systems improve the transportation system’s operational efficiency and enhance its safety and reliability by high-tech means such as information technology, control technology, and computer technology. In recent years, sustainable development has become an important topic in intelligent transportation’s development, including new infrastructure and energy distribution, new energy vehicles and new transportation systems, and the development of low-carbon and intelligent transportation equipment. New energy vehicles’ development is a significant part of green transportation, and its automation performance improvement is vital for smart transportation. The development of intelligent transportation and green, low-carbon, and intelligent transportation equipment needs to be promoted, a significant feature of transportation development in the future. For intelligent infrastructure and energy distribution facilities, the electricity for popular electric vehicles and renewable energy, such as nuclear power and hydrogen power, should be considered

A secured message transmission protocol for vehicular ad hoc networks

Vehicular Ad hoc Networks (VANETs) become a very crucial addition in the Intelligent Transportation System (ITS). It is challenging for a VANET system to provide security services and parallelly maintain high throughput by utilizing limited resources. To overcome these challenges, we propose a blockchain-based Secured Cluster-based MAC (SCB-MAC) protocol. The nearby vehicles heading towards the same direction will form a cluster and each of the clusters has its blockchain to store and distribute the safety messages. The message which contains emergency information and requires Strict Delay Requirement (SDR) for transmission are called safety messages (SM). Cluster Members (CMs) sign SMs with their private keys while sending them to the blockchain to confirm authentication, integrity, and confidentiality of the message. A Certificate Authority (CA) is responsible for physical verification, key generation, and privacy preservation of the vehicles. We implemented a test scenario as proof of concept and tested the safety message transmission (SMT) protocol in a real-world platform. Computational and storage overhead analysis shows that the proposed protocol for SMT implements security, authentication, integrity, robustness, non-repudiation, etc. while maintaining the SDR. Messages that are less important compared to the SMs are called non-safety messages (NSM) and vehicles use RTS/CTS mechanism for NSM transmission. Numerical studies show that the proposed NSM transmission method maintains 6 times more throughput, 2 times less delay and 125% less Packet Dropping Rate (PDR) than traditional MAC protocols. These results prove that the proposed protocol outperforms the traditionalMAC protocols

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.publishedVersio

Blockchain-enabled capabilities in transport operations: an overview of the literature

The blockchain was initially developed for use in the banking sector. However, over time, different areas of knowledge have adopted these technologies, including transportation operations. This use of blockchain in the transport sector is mainly due to the ability of this technology to enable the data generated by these activities to be reliable. In addition to aspects related to data immutability, blockchain enables greater data privacy, as well as making it possible for the data control process to be decentralized. In this sense, it was carried out a systematic literature review (RSL) to identify the general publications panorama on the topic, and to identify the capabilities enabled by the blockchain in the context of transportation operations. RSL has great potential to make it possible to deepen the literature on a given topic. The analysis of the RSL results included the realization of two stages. The first step consisted of a quantitative analysis of data from a sample of 50 articles, to identify this research field about the distribution by journal, year, and author. This first step enabled a general analysis of the field of study on the use of blockchain in transportation. The second stage consisted of a qualitative analysis of the ten most relevant articles in this field of study. In this way, it was possible to understand more about the use of blockchain in transport operations, as well as to identify seven capabilities enabled by the blockchain. These capabilities represent abilities that blockchain technology allows the transport sector today, demonstrating the importance of its use, as well as of study

Blockchain-Enabled Federated Learning Approach for Vehicular Networks

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system's accuracy stands at 91.92\%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.Comment: 7 page

Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: Next Frontier for Intelligent Safe-Driving Assessment

Securing safe-driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Besides, diverse malicious network attacks become ubiquitous along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. Motivated by this, in this article, we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), that offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then, we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks.Comment: 8 pages, 6 figures. This paper has been accepted for publication by IEEE Networ