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A dynamic and optimized routing approach for VANET communication in smart cities to secure intelligent transportation system via a chaotic multi-verse optimization algorithm

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Abstract

VANET technology is an essential component of Intelligent Transportation Systems, which makes c communication between moving cars and stationary Road Side Units more accessible. It allows vehicle nodes to share crucial data among communication devices. VANET has significant potential to enhance traffic efficiency and road safety. This is accomplished by decreasing the chances of collisions between vehicles and reducing the number of accidents. Man-in-the-middle (MITM) attacks are a crucial issue in VANET which needs significant consideration from researchers. To solve the problem of man-in-the-middle attacks, this article presents a dynamic and optimized routing approach for VANET conversation in smart cities by utilizing a chaotic secure multi-verse optimization algorithm. The strategy that has been proposed seeks to achieve the goal of ensuring safe and effective interaction between vehicles participating in VANETs by dynamically determining the optimal path for the exchange of data. A chaotic protect multi-verse optimization approach is used to generate several random sequences from which the most secure route may be selected. This is done to enhance the security of the VANET transmission network during transmission. The results of the trials indicate that the suggested technique is more successful in avoiding MITM and improving the functioning of VANET connections in settings that are characterized by intelligent cities.

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Data availability

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

Abbreviations

VANET:

Vehicular ad hoc network

MITMA:

Man-in-the-middle

WiFi:

Wireless fidelity

LTE:

Long-term evolution

ITS:

Intelligent transportation systems

GA:

Genetic algorithm

DORA:

Dynamic and optimized routing approach

PDR:

Packet delivery ratio

DSRC:

Dedicated short-range communication

SVM:

Support vector machines

AODV:

Ad-hoc on-demand distance vector

D.Q.N.:

Deep Q networks

DRL:

Deep reinforcement learning

MANET:

Mobile ad hoc networks

SMO:

Secure multi-verse optimisation

CSMO:

Chaotic secure multi-verse optimisation

MHDOR:

MVO-based hybrid dynamic and optimized routing

BS:

Base station

E2E:

End-to-end

CDR:

Coordinated direct and relay

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Acknowledgements

We would like to acknowledge all who have directly/indirectly supported this research.

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Authors and Affiliations

  1. Maharshi Dayanand University, Rohtak, Haryana, 124001, India

    Sumit, Rajender Singh Chhillar & Sandeep Dalal

  2. Amity University Haryana, Gurugram, 122413, India

    Surjeet Dalal

  3. Galgotias University, Greater Noida, Uttar Pardesh, 244401, India

    Umesh Kumar Lilhore & Sarita Samiya

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Contributions

Conceptualization, SK; methodology, UKL; validation, RC; formal analysis, SSD; investigation, SSD; resources, SK; data curation, SS; writing—original draft, RC; supervision, SK. All authors contributed equally to this research. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Surjeet Dalal.

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Sumit, Chhillar, R.S., Dalal, S. et al. A dynamic and optimized routing approach for VANET communication in smart cities to secure intelligent transportation system via a chaotic multi-verse optimization algorithm. Cluster Comput 27, 7023–7048 (2024). https://doi.org/10.1007/s10586-024-04322-9

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