August 15, 2025Journal of Mobile MultimediaOpen Access

An Extensive Study on the State-of-the-Art Literature to Identify Major Approaches and Trust Issues of Trust-based Models in VANETs

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Trust-based models enhance safety and efficiency in vehicular ad-hoc networks, addressing security and privacy concerns.
Statistics reveal that integrating blockchain and machine learning can improve the reliability of trust models substantially.
The review categorizes approaches into entity-centric, data-centric, and hybrid models, assessing their strengths and weaknesses.
Interdisciplinary collaboration is crucial for developing effective and resilient trust systems against malicious activities.

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Implication

Systematic review identifies trust-based models and their limitations in vehicular ad-hoc networks, indicating potential improvements with blockchain and machine learning.

Cite This Study

Gupta et al. (2025) studied this question.

synapsesocial.com/papers/689fc962e46e096235ce1e97 https://doi.org/10.13052/jmm1550-4646.21345

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Implication

Systematic review identifies trust-based models and their limitations in vehicular ad-hoc networks, indicating potential improvements with blockchain and machine learning.

An Extensive Study on the State-of-the-Art Literature to Identify Major Approaches and Trust Issues of Trust-based Models in VANETs

Key Points

Discussion

Implication

Cite This Study

An Extensive Study on the State-of-the-Art Literature to Identify Major Approaches and Trust Issues of Trust-based Models in VANETs

Key Points

Discussion

Implication

Cite This Study