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Malicious Vehicle Detection in Intelligent Transportation Systems
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Advances in Robotics & Automation

ISSN: 2168-9695

Open Access

Commentary - (2022) Volume 11, Issue 6

Malicious Vehicle Detection in Intelligent Transportation Systems

Helen Groll*
*Correspondence: Helen Groll, Department of Artificial Intelligence in Biomedical Engineering, Indian Institute of Technology, Delhi, India, Email:
Department of Artificial Intelligence in Biomedical Engineering, Indian Institute of Technology, Delhi, India

Received: 05-Jun-2022, Manuscript No. ARA-22-72744; Editor assigned: 07-Jun-2022, Pre QC No. P-72744; Reviewed: 10-Jun-2022, QC No. Q-72744; Revised: 15-Jun-2022, Manuscript No. R-72744; Published: 20-Jun-2022 , DOI: 10.37421/2168-9695.2022.11.215
Citation: Groll, Helen. “Malicious Vehicle Detection in Intelligent Transportation Systems.” Adv Robot Autom 11 (2022): 215.
Copyright: © 2022 Groll H. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Description

In the cutting edge time, vehicle areas have modernized because of advances in correspondence frameworks. As of late, the quantity of vehicles has likewise expanded because of the tremendous populace development. This progress brings new encounters for independent and self-driving vehicles. New administrations are being presented in cutting edge vehicles, for example, correspondence and charging administrations. The vehicle area has likewise taken incredible steps and is being changed into a shrewd and smart organization. Traditional vehicles are being changed into brilliant and electric vehicles, known as electric vehicles (EVs). The EVs are associated with an organization and speak with one another. The organization made by associating vehicles and specialized gadgets is known as the Internet of Vehicles (IoVs). In IoVs, vehicles are outfitted with different sensors that gather data from different vehicles and Roadside Units (RSUs) and process it for different decision-production [1].

They additionally speak with charging stations. Vehicle to Grid (V2G) and Vehicle to Vehicle (V2V) are two normal correspondence channels through which vehicles speak with different units. In V2V, vehicles speak with different vehicles and trade data like street and weather patterns. In V2G, then again, vehicles speak with power networks to meet their energy needs [2]. The different correspondence modes for electric vehicles incorporate Vehicle-to- Infrastructure (V2I) and Vehicle-to-Everything (V2X). V2I includes vehicles speaking with adjacent foundation, while V2X includes correspondence among vehicles and encompassing structures, tollgates, service stations, and so on. Electric vehicle development is putting up two new ideas for sale to the public: Grid to Vehicle (G2V) and V2G. EVs have bidirectional correspondence and energy stream. The vehicle area has encountered quick development lately. As the quantity of vehicles utilizing fuel increments, so does the probability of street clog, bringing about contamination. Examination and science have zeroed in on EVs as a spotless energy hotspot for the climate. They lessen the requirement for oil while additionally diminishing gas outflows [3].

Customary unified approaches utilized in vehicular organizations (VNs) face capacity and security challenges. For instance, the review introduced in addresses model reversal assaults utilizing profound generative models. The creators in use blockchain in wise vehicles (IVs) for protection and security purposes. Nonetheless, the VN doesn't think about dispersed memory the board and channel dependability. Blockchain is utilized in the proposed framework to address security issues. It gives security to clients and advances decentralization. It is a disseminated, decentralized, and permanent record that gives security, reliability, and straightforwardness for information [4]. A duplicate of the disseminated record is accessible to all organize members.

The proposed model additionally settles the trust issues among IVs and recognizes credible and inauthentic clients by identifying the vindictive IVs in the organization. At the point when an IV is placed into the organization, it is enlisted through CA and gets a nom de plume. This ID is utilized for correspondence. In the proposed model, V2V and V2I correspondences are started, where all exchanges are approved by means of AES and malevolent IVs are recognized in light of their standing qualities. These standing qualities are produced by an insightful agreement in light of the value-based history of the IVs. It likewise presents a multi-chain idea where exchange information and pernicious IVs are put away in two branches: the I-chain and F-chain. A brilliant agreement is proposed for the multi-fasten component to decrease calculation time and oversee stockpiling necessities. IPFS is coordinated with CA to tackle the capacity issue [5]. In IPFS, information is separated into lumps, and each piece is doled out special hash esteem. These hash values are put away in the blockchain, and the information are put away in IPFS, which has less expense.

Conflict of Interest

None.

References

  1. Hîrţan, Liviu-Adrian, Ciprian Dobre and Horacio González-Vélez. “Blockchain-based reputation for intelligent transportation systems.” Sensors 20 (2020): 791.
  2. Google Scholar, Crossref, Indexed at

  3. Kim, MyeongHyun, KiSung Park, SungJin Yu and JoonYoung Lee, et al. “A secure charging system for electric vehicles based on blockchain.” Sensors 19 (2019): 3028.
  4. Google Scholar, Crossref, Indexed at

  5. Jabbar, Rateb, Mohamed Kharbeche, Khalifa Al-Khalifa and Moez Krichen, et al. “Blockchain for the internet of vehicles: A decentralized IoT solution for vehicles communication using Ethereum.” Sensors 20 (2020): 3928.
  6. Google Scholar, Crossref, Indexed at

  7. Khan, Prince Waqas and Yung-Cheol Byun. “Smart contract centric inference engine for intelligent electric vehicle transportation system.” Sensors 20 (2020): 4252.
  8. Google Scholar, Crossref, Indexed at

  9. Kudva, Sowmya, Shahriar Badsha, Shamik Sengupta and Ibrahim Khalil, et al. “Towards secure and practical consensus for blockchain based vanet.” Inf Sci 545 (2021): 170-187.
  10. Google Scholar, Crossref

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