Reliable Multicast Routing Protocol For DTN Based Vehicular Ad Hoc Networks

Malathi, A.; Sreenath, N.

doi:10.9790/0661-1805051720

Cited by 1 publication

(1 citation statement)

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“…The field of artificial intelligence in machine learning is also an inevitable growth trend. Because the computing power of computers has been significantly improved, there are a large number of algorithms that cannot be executed due to a large number of calculations, and the application of machine learning is becoming more and more integrated [11], from language recognition to image recognition to intelligent robots, driverless cars, etc. Driving a vehicle is controlled by humans [12,13].…”

Section: Dtn Routing Protocol In Vehicle Self-organizing Network Methods Based On Machine Learningmentioning

confidence: 99%

[Retracted] Study on DTN Routing Protocol of Vehicle Ad Hoc Network Based on Machine Learning

Liang

Shang

Wang

2021

Wireless Communications and Mobile Computing

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The vehicle-mounted self-organizing network is a part of the MANET network. It is placed between the roadside vehicle and the fixed communication equipment. It can serve as a hub for road vehicles and can enable multihorsepower wireless mechanisms to exchange data between vehicles. This article is aimed at studying the DTN routing protocol based on machine learning in the vehicle self-organizing network. When data is forwarded, the node will determine the forwarding route selection according to its own coordinate information, the coordinate information of neighboring nodes, and the coordinate information of the destination node. Usually, the purpose is for the geographic coordinates of the node to be stored in the data packet. And data packets are periodically transmitted between nodes on each network. So that when you publish your own coordinate nodes, you can update the location information of nearby nodes at any time. This paper proposes that routing technology has become one of the most important challenges in vehicle self-organization, and there are many reasons for this. These reasons include frequent changes in the network topology and fast-moving mobile nodes. The experimental results in this paper show that more than 67% of the network data is obtained through the Gawk data extraction tool to quantify GPSR performance indicators and obtain the average driving speed of the current vehicle node. When increasing, the average end-to-end transmission delay of the GPSR routing protocol increases, and the average transmission rate decreases.

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Section: Dtn Routing Protocol In Vehicle Self-organizing Network Methods Based On Machine Learningmentioning

confidence: 99%