A Deep Learning Approach for Sustainable Ad Hoc Vehicular Network

被引：0

作者：

Thorat, Samrat Subodh ^{[1
]}

Rojatkar, Dinesh Vitthalrao ^{[2
]}

Deshmukh, Prashant R. ^{[2
]}

机构：

[1] Govt Coll Engn, Yavatmal Elect & Telecommun Engn, Yavatmal, India

[2] Govt Coll Engn, Amravati Elect Engn, Amravati, India

来源：

SMART TRENDS IN COMPUTING AND COMMUNICATIONS, VOL 2, SMARTCOM 2024 | 2024年 / 946卷

关键词：

5G-VANET; Deep neural network; Controller area network; Intrusion detection system; Datasets for ad hoc vehicular system; Deep belief network; Long-term term short-term memory; IoT-based dataset;

D O I：

10.1007/978-981-97-1323-3_37

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the era of autonomous vehicles and Google cars, Vehicular Ad Hoc networks are slowly and steadily becoming a reality. V2V and V2I are two prominent and important variants of vehicular networks. RSUs are not available wherever due to any constraint like geographical terrain, so V2V is the only form available, and it should be sustainable not only for geographical constraint but also for intrusion in the network. Many AI algorithms are used for classification, but performance does not necessarily increase with an increase in data size since saturation is reached. In deep learning with an increase in data size, performance increases manifold as compared to machine learning algorithms.

引用

页码：429 / 443

页数：15

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