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Dieses Kapitel befasst sich mit der Anwendung der Blockchain-Technologie für sicheres und datenschutzschonendes Fahrzeug-Identitätsmanagement und Datenaustausch, wobei der Schwerpunkt auf der Kommunikation von Emissionsdaten in der EU liegt. Die Forschung untersucht den Einsatz von Blockchain zur Schaffung sicherer digitaler Identitäten für Fahrzeuge, die die Privatsphäre gewährleisten und gleichzeitig die EU-Vorschriften einhalten. Die Studie beinhaltet eine umfassende Simulation eines Blockchain-Netzwerks, das reale Bedingungen mit 27 Organisationen der Mitgliedstaaten und der Europäischen Kommission nachahmt. Zu den wichtigsten Erkenntnissen gehört die Fähigkeit des Systems, 2,7 Millionen Transaktionen mit 5400 gleichzeitigen Kunden zu bearbeiten und einen maximalen Durchsatz von 257 Transaktionen pro Sekunde zu erreichen. Die Forschung identifiziert auch Beschränkungen und Bereiche für weitere Optimierungen, wie etwa die Verwaltung eines hohen Kundenvolumens und die Verringerung der Transaktionskosten. In der Schlussfolgerung wird das Potenzial der Blockchain-Technologie hervorgehoben, die rechtlichen Anforderungen für den Austausch von Fahrzeugdaten zu erfüllen und gleichzeitig weitere Verbesserungen für großflächige Anwendungen vorzuschlagen.
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Abstract
As vehicular connectivity and digitisation surge, escalating data transmission to Original Equipment Manufacturers (OEMs) and public authorities is vital for the digital transition, for applications from legal compliance to traffic management. Amidst burgeoning data-sharing ecosystems, ensuring secure, private data transmission and General Data Protection Regulation (GDPR) compliant user control over vehicle identity and data-sharing permissions becomes pivotal, barring legal and enforcement exceptions. This research explores employing blockchain technology to safeguard privacy and security within a Vehicle Identity Management system, using CO2 emissions monitoring as an exemplar. Utilising an emulation-based environment, replicating vehicle interactions with European authorities and the European Commission (EC), the study demonstrates that blockchain systems, specifically for CO2 emissions monitoring, can meet transaction rate and latency demands of large-scale transport applications, accommodating 280 million vehicles reporting annually. This inquiry not only amplifies understanding of blockchain’s applicability in connected transportation systems and secure data exchange among vehicles, authorities, and stakeholders but also lays groundwork for future advancements in trustful, efficient, and secure data interchange, potentially benefiting authorities, industry, and end-users alike.
1 Introduction
1European policy increasingly emphasises the importance of connectivity and interoperability of various systems in the road transport sector. Hundreds of millions of vehicles are projected to interact on a daily basis with people, automated systems and each other. The deployment of new mobility solutions that will improve the sustainability, effectiveness and energy efficiency of the transport sector will rely on connectivity and secure data exchange between entities.
One technology suitable for this purpose are distributed ledgers, and particularly blockchain (BC) systems. At their core, blockchain systems are a type of distributed ledger technology that enables secure and transparent documentation of digital transactions. They are comprised of a series of blocks, each containing a group of validated transactions, which are cryptographically linked to the previous block, thereby creating an immutable and tamper-proof record.
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Blockchains could revolutionise how real-world data and assets are connected to their digital counterparts in several ways:
Smart Contracts: Automate transactions for predefined conditions.
Identity Management: generate secure and easily verifiable digital identities for entities, that may be used to control access to data.
Data Sharing and Interoperability: secure data sharing, fostering collaboration and interoperability.
This paper builds upon previous research [1], which investigated the application of BC technology for secure and reliable data communication in the road transport sector. This research changes the network structure to more accurately reflect the real-world. The key research questions addressed are the plausibility of using blockchain in a wide scale application covering large portions of the fleet, and what would be a possible system architecture for doing so. In this paper we investigate the communication of vehicle emissions data to European authorities. Such data retrieval directly from the vehicles is foreseen by existing EU regulations for CO2 emissions [2‐4] and have been proposed for the monitoring of regulated CO2 and pollutant emissions.
In the context of emissions monitoring of vehicles, the crucial element for preserving privacy is ensuring the Vehicle Identification Number (VIN) is hidden while verifying that it is both a valid and unique VIN registered at one of the EU27 Vehicle Registration Authorities. Centralised access management of identities raises concerns, such as security risks due to a single point of failure, performance issues from technical problems or Denial of Service (DoS) attacks, and privacy breaches following database leaks [5‐7].
2 Methodology
This research explores the application of BC technology for securely managing emission data from vehicles, targeting a throughput capable of handling the EU27 fleet of 280 million vehicles. A comprehensive description of the hardware and software utilized is documented in the JRC Science for Policy Report [1].
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Infrastructure and Experimentation:
Utilising JRC’s EPIC cluster, we emulated a realistic blockchain (BC) network for simulation purposes, involving the deployment of 80 EPIC nodes connected via a 1 Gbps line, each facilitated with 6 GB of RAM and 40 GB of storage [8]. Hyperledger Fabric (HLF) 2.2 [9] was employed for its modular and private architecture on Ubuntu 18.04, and experiments were executed within an orchestrated environment utilizing Kubernetes atop EPIC to efficiently handle network layers, storage allocation, and scaling.
Blockchain Network and Data Privacy:
The Hyperledger Fabric employs Private Data Collections (PDCs) to assure data privacy by ensuring only authorized entities access sensitive data, enabling GDPR compliance via irreversible data deletion, and offering adjustable access control to safeguard data while promoting secure sharing among organizations on the channel. A detailed breakdown of PDCs and Hyperledger terminology is available in referenced sources [9‐12].
2.1 Experimental Setup
Changes were made to the endorsement policy from previous experiments [1] to:
1.
require approval only from the Vehicle Registration Authority (RA) and the EC endorser, thereby reducing traffic and conserving storage, and
2.
transition to a realistic model, where clients connect, transmit transactions, and disconnect, more closely mimicking real-world operational circumstances.
Vehicles, upon registration with the relevant RA, are assigned an X.509 certificate and a VIN recorded in the RA’s global state database, enabling them to transmit emissions data to the pertinent EC organization. Vehicle data is stored on the peers of each RA and the EC, accessible only to these entities, while others receive a hash of the emissions data.
In a network emulating real-world scenarios, 100,000 VINs per RA were registered and then used to generate transaction processes involving connect-discover-authenticate-disconnect procedures, ensuring an extensive engagement of the entire system. A script enabled five connections, each representing a vehicle transmitting data, to mimic a genuine scenario involving multiple vehicle connections to the network. The GNU parallel tool was utilized to manage a substantial number of connections and execute multiple script instances concurrently [1].
3 Results
The successful simulation of a comprehensive network, including 27 Member State organisations and the EC, is depicted in Fig. 1 and Fig. 2. The system managed to support 200 clients per Member State (5400 total clients connected simultaneously), handling 2.7 million transactions while maintaining stability and reasonable performance. A saturation point of around 250 Transaction Per Second (TPS) was consistently reached, regardless of the number of organisations, indicating the maximum throughput achievable with the current configuration. Figure 1 shows that when there are only a few Member State organisations with a limited number of clients, the system does not reach saturation. For both 3 and 5 Member State configurations, saturation is achieved with 200 clients per Member State, as shown in Fig. 1 and Fig. 2. With 100 clients per Member State, the network reaches its peak performance at around 280 TPS and a latency of approximately 0.5 s. However, increasing the number to 200 clients per Member State results in a lower TPS of 245. The constant latency, as seen in Fig. 2, indicates that “this reduction is due to an increase in CPU utilisation caused by the larger number of clients” [1].
Fig. 1.
Experiment 2: “Transactions per second for 3, 5, 27 Member States + EC with varying numbers of simulated vehicles”.
Despite the BC network performance remaining stable, full utilisation each of the virtual CPUs was observed for all endorsing peers. The system with 27 Member States reached saturation even with a small number of clients. When 50 clients were connected simultaneously, “latency was significantly higher than in the 3 and 5 Member State configurations, increasing to almost 2 s with 200 clients. It is important to note that the system remained stable and no transaction failures or timeouts were observed on the client side” [1]. However, trials with 300 simultaneous clients, which are not shown in the figures, revealed increased instability across all Member State configurations, mainly due to timeouts and connection failures.
This research builds upon the initial experimental setup findings described in [1, 13], incorporating a modified consensus approach and client configuration to more closely resemble real-world scenarios. By mandating signatures solely from the endorsing peer of the RA in which the vehicle is registered and the endorsing peer of the EC, the new consensus approach addresses the concerns of maintaining data integrity and audit ability, reducing storage footprints and lowering network traffic.
The updated client configuration, which incorporates 100,000 VINs per RA and simulates a more intricate connect-discover-authenticate-disconnect process, provides a more accurate and comprehensive representation of real-world use cases. This enhanced approach captures the complex aspects of vehicular connections within the blockchain network, offering valuable insights into the network’s performance under realistic conditions.
In this experiment, the system was able to support a significant number of clients (200 per Member State, totalling 5400 simultaneous clients) while maintaining stability and reasonable performance. The saturation point of approximately 250 TPS was consistently reached, regardless of the number of organisations involved. This result indicates that the current configuration has a maximum achievable throughput, which may need further optimisation to accommodate higher transaction rates.
However, the experiment also revealed some limitations, such as increased latency and resource constraints with higher numbers of clients. These findings indicate that further optimisation of the network design, consensus approach, and hardware resources may be necessary to improve performance and accommodate larger numbers of clients.
The primary limitation of the system was its ability to accommodate a large amount of clients. “The system could not handle more than 200 simulated vehicles for each Member State organisation. When the number of vehicles in the simulation” [1] exceeded this limit, the system experienced difficulties. There was a dramatic increase in the number of transactions that timed out and clients that were disconnected from the system. This suggests that the system has a limited capacity to manage a high volume of clients at the same time.
In conclusion, this research demonstrates the potential of the modified consensus approach and client configuration for handling real-world use cases involving vehicular connections to the blockchain network. The results highlight the scalability and robustness of the proposed architecture while also identifying areas for further optimisation and refinement. Future work should focus on addressing these limitations and further improving the network design to ensure its suitability for large-scale emissions monitoring and other related applications.
4 Conclusion
This research aimed to evaluate the technical feasibility of sharing vehicle data through a blockchain-based system, utilising current technology. “Real-world fuel consumption monitoring served as a representative use case, involving a fleet of 280 million vehicles” [1], where each Member State is legally obligated to report CO2 emissions from road vehicles to the EC annually. Optimisations in the experimental setup enabled the system to successfully support 5400 concurrent clients (vehicles) distributed across 27 Member States. Each vehicle completed a full life cycle, taking approximately three seconds, achieving a maximum throughput of 257 TPS while maintaining system stability and responsiveness.
Ideally, processing the 280 million required transactions per year would take approximately 12.6 days for all EU vehicles. Although this result may not reflect real-world conditions with non-optimal timing and variable demand, it does indicate that the system can “meet the legal requirement of one transaction per vehicle per year” and even has the potential to reach the experimental goal of one transaction per vehicle per month. However, this work also identified significant HLF limitations, such as considerable “transaction overhead in terms of disk usage for each endorsement” [1] and managing numerous open client connections simultaneously. By addressing these limitations, the project team observed substantial performance improvements in the new experimental setup detailed in this research.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
The views expressed in this document are purely those of the authors and shall not be considered as an official position of the European Commission under any circumstance.
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