2023
DOI: 10.1109/tits.2023.3262398
|View full text |Cite
|
Sign up to set email alerts
|

Social Psychology Inspired Distributed Ledger Technique for Anomaly Detection in Connected Vehicles

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 9 publications
(1 citation statement)
references
References 36 publications
0
1
0
Order By: Relevance
“…However, the combination of intelligent vehicle trades and blockchain still needs extensive research. On one hand, traditional blockchain can hardly meet the requirements of IoV such as low latency and high throughput, which provides an opportunity for the development and application of blockchain with new directed acyclic graph (DAG) [10][11][12] architecture such as IoTA [13]. On the other hand, blockchain-based smart contracts [14] can automatically execute the contract terms, which improves the efficiency and reliability of execution and also reduces the errors and disputes that may occur during execution, which further ensures the security of intelligent transportation-based applications.…”
Section: Introductionmentioning
confidence: 99%
“…However, the combination of intelligent vehicle trades and blockchain still needs extensive research. On one hand, traditional blockchain can hardly meet the requirements of IoV such as low latency and high throughput, which provides an opportunity for the development and application of blockchain with new directed acyclic graph (DAG) [10][11][12] architecture such as IoTA [13]. On the other hand, blockchain-based smart contracts [14] can automatically execute the contract terms, which improves the efficiency and reliability of execution and also reduces the errors and disputes that may occur during execution, which further ensures the security of intelligent transportation-based applications.…”
Section: Introductionmentioning
confidence: 99%