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Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles
Open accessAbstract
Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on. The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.
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“Some of them propose new platforms just at a concept level [45,51,52,75,77,83,84,88,89,91], some of them present proofs of their concepts [76,79,81], and only three of them present a real implementation based on real blockchains: [78,80,82].”
From A Review of Blockchain-Based Systems in Transportation (2019) · cites this paper as [91]
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Meydani, A., Meidani, A., & Kazeminasab, M. M. (2023) · International Conference on the Internet of Things (IoT) · doi:10.1109/iot60973.2023.10365349 · Cited by 8
Liang, J., Miao, C., Chen, J., Zhu, R., Lu, K., & Jiao, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3386775 · Cited by 5
Kawakura, S., & Shibasaki, R. (2019) · European Journal of Agriculture and Food Sciences · doi:10.24018/ejfood.2019.1.2.13 · Cited by 5
Metadata
- Authors
- Cebe, M., Erdin, E., Akkaya, K., Aksu, H., & Uluagac, S.
- Affiliations
- Florida International University (US)
- Venue
- IEEE Communications Magazine
- Published
- 2018-10
- Volume
- 56
- Issue
- 10
- Pages
- 50-57
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2786393483
- Cited by
- 326
- References
- 16
- Citations by year
- 2026: 20 · 2025: 21 · 2024: 30 · 2023: 42 · 2022: 46 · 2021: 66 · 2020: 47 · 2019: 45 · 2018: 9
- FWCI
- 67.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Communications Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Bilkent University Institutional Repository (Bilkent University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, connected vehicles, autonomous vehicles, permissioned blockchain, vehicle data management, privacy, traceability, cyber-physical systems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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